Aeri Rachmad - Profile on Academia.edu (original) (raw)

Papers by Aeri Rachmad

Research paper thumbnail of Optimization Energy Consumption Using Constrains Temperature and Fan Speed on Salt Dryer Machine Based on Taguchi L9 Statistics Model

Jurnal Internasional Bereputasi MMEP, 2025

Madura island meet mostly of the salt demand in Indonesia, Sampang, Pamekasan, and Sumenep are th... more Madura island meet mostly of the salt demand in Indonesia, Sampang, Pamekasan, and Sumenep are three of the four districts of the island that produce salt. Salt ponds run on traditional way over generations, this situation is highly dependent on sunlight in the salt drying process. This research aims to find the optimal parameters on the salt drying machine on the efficiency of energy consumption on the machine. Optimization using Taguchi orthogonal array L9. General linear ANOVA used to define contribution level of each parameter. Fan speed parameter has 3 levels namely; low, medium, and high. Drying temperature has 3 levels they are; 18℃, 20℃ and 22℃. Quality control term on this research is smaller better, the less energy consumption during drying process leads to efficiency cost for dryer machine operation. The lowest energy consumption found on this research is 4687 kWh, achieved on temperature 20℃ and low fan speed. The highest energy consumption found on research is 5492 kWh, achieved on temperature 20℃ and low fan speed. Based on Taguchi L9 recommendation optimum combination is fan speed low, 20℃ on drying temperature. Parameter drying temperature is dominant compared to parameter fan speed. Parameter fan speed has contribution 0.64% and parameter dryer temperature process has contribution 94.7%.

Research paper thumbnail of Geometry Algorithm on Skeleton Image Based Semaphore Gesture Recognition

Semaphore, a way of communicating remotely, usually practiced in scouting activities. Information... more Semaphore, a way of communicating remotely, usually practiced in scouting activities. Information is delivered by gestures or movements using specific tools such as flags, paddles or rods. Teacher and instructors are needed for learning semaphore in conventional way as they will give examples and make correction when such an error occured. Based on the practical need to provide an alternative way to learn semaphore, this research proposes the use of geometry algorithm to develop a semaphore gesture recognition based on skeleton images that read from Kinect sensor. Euclidean distance and law cosines are two formulas that applied to generate gesture parameters of each alphabet. Recognition is achieved by comparing a pair of values of model and real-time gesture. Accuracy of this system that have been measured using RMSE with 30° of tolerance yields 90.76% for Alphabet and 88% for Word.

Research paper thumbnail of Application of ant colony optimization algorithm to determine optimal value in choosing tourist attractions in Bangkalan -Madura

AIP Conference Proceedings 2679, 020009 , 2023

The impact of COVID-19 leaves a deep sadness, especially in the tourism sector. Many tourist plac... more The impact of COVID-19 leaves a deep sadness, especially in the tourism sector. Many tourist places are not open to prevent the transmission of this virus. Madura is one of the islands located in the province of East Java which has many tourist attractions. Bangkalan is one of the districts in Madura which has 21 tourism spots. There are many tourist attractions scattered in Bangkalan district, but there is a problem arisen in determining the shortest path to get to these tourist attractions. There are several route options available in each area. Optimal value search can be used to obtain the highest and lowest values of a problem. One of the popular problems that can be solved by optimization algorithms is the Traveling Salesman Problem (TSP) to determine the closest route using the Ant Colony Optimization algorithm. The ant algorithm is an algorithm adopted from the behaviour of the ant colony. An ant colony can find the shortest route between the nest and a food source based on footprints on the trajectory it has traversed. The more ants that pass through a track, the clearer the footprints will be. Ant Colony algorithm is very appropriate to be applied in solving optimization problems, one of which is to determine the shortest path. The final result of this discussion is the algorithm used is able to determine the shortest path to find tourist destinations as an alternative route. The accuracy results obtained are 100% with a rho value of 0.5, an alpha value of 1, and a beta value of 1.

Research paper thumbnail of Prediction of corn crop yield using backpropagation neural network algorithm

AIP Conference Proceedings 2679, 020006 , 2023

Corn is one type of food crop commodity in Indonesia. Malang Regency is one of the producers that... more Corn is one type of food crop commodity in Indonesia. Malang Regency is one of the producers that ranks 10th in corn production in the East Java region. People are very interested in planting corn because this crop commodity has many benefits so as to make the demand for production increase. There was a significant increase in market demand, but the uncertain amount of production made the supply of corn plants unable to be fulfilled properly. In this study, it predicted the demand for corn by using the Backpropagation Neural Network algorithm in Malang Regency. The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 every month using maize data from the Malang area. The results showed that the backpropagation algorithm produced an MSE value of 0.00004178.

Research paper thumbnail of Analysis average waiting time search performance in the queue process on CPU scheduling using the Round Robin, shortest job first and first in first out algorithm

AIP Conference Proceedings 2679, 020010 , 2023

In its activities, the CPU has a pattern of processes that coincide or precede each other. Where ... more In its activities, the CPU has a pattern of processes that coincide or precede each other. Where when one activity enters, another activity will come, and so on which is multiprogramming. Round Robin (RR), Shortest Job First (SJF), and First In First Out (FIFO) methods are CPU scheduling algorithms that are popularly used in multiprogramming systems. These three algorithms are used to find Average Waiting Time (AWT) with different process sequences. In the Round Robin algorithm, there is Qu that affects the number of process sequences. The AWT value is obtained from the number of waiting for processes divided by the number of existing processes. The makespan value in each algorithm must be the same even though the process sequence is different depending on the arrival time. This study describes and analyzes how the RR, SJF, and FIFO algorithms work in the queuing process on the CPU (Central Processing Unit). The result of this research is an analysis that is easy to understand in the form of an interactive programming application. In the resulting application, it can be seen that the queuing process in AWT runs in stages according to the principles of the three algorithms.

Research paper thumbnail of Clustering tourism places in Madura based facilities using fuzzy C-means

AIP Conference Proceedings 2679, 020007 , 2023

Madura Island consists of four districts namely Bangkalan, Sampang, Pamekasan, and Sumenep. Each ... more Madura Island consists of four districts namely Bangkalan, Sampang, Pamekasan, and Sumenep. Each district has many choices of interesting tourist attractions. There are various types of tourism ranging from natural tourism, cultural tourism, historical tourism, and artificial tourism. With the diversity of these tourist attractions, it is enough to invite many tourists to come on vacation to Madura Island. Each tourist attraction has a different number of visitors, the number of public facilities, and ticket prices. All the criteria possessed by each tourist attraction have their own assessment for potential tourists. The local Tourism Office must know the developments in each tourist attraction, so that it can maintain the quality of the tourist attraction. Improvement of infrastructure can be through public facilities provided at tourist objects is very necessary. The purpose of this study was to determine how well the Fuzzy C-Means method in grouping tourism objects in Madura. Fuzzy C-Means is a grouping method which the development of the k-means cluster is not a hierarchical method that allocates data into each group by utilizing fuzzy set theory. From the trials that have been carried out, the best grouping results are found in cluster 10 with a silhouette coefficient value of 0.825 which is included in the strong category.

Research paper thumbnail of Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Mathematical Modelling of Engineering Problems Vol. 10, No. 2, pp. 530-536, 2023

The decline in corn production can affect the continuity of food grown in society, especially in ... more The decline in corn production can affect the continuity of food grown in society, especially in Indonesia, which is a country with a high level of corn consumers. Several factors cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged. Therefore, a system is needed that can identify diseases in corn plants so that appropriate treatment can be carried out as early as possible to prevent severe damage to corn plants. With this research, the system can be built by utilizing machine learning in building a classification system using the Convolutional Neural Network (CNN) algorithm with a dataset of corn leaf images taken from farmers' fields in the Madura Region with four target classes namely healthy, gray leaf spot, blight, and common rust. Testing was carried out using several CNN architectural models such as SqueezeNet, AlexNet, ResNet-101, ResNet-50, and ResNet-18. The parameters used were 5 epochs with 100 iterations, a learning rate of 0.0001, using Adam optimization, and a data distribution of 70% for training data and 30% for testing data. The test results obtained in classifying corn images using the Convolutional Neural Network method with the ResNet-50 architecture provide a very good accuracy value of 95.59%.

Research paper thumbnail of PENGARUH REDUKSI DIMENSI PADA CLUSTERING CITRA DAUN TOMAT MENGGUNAKAN DARKNET19 DAN K-MEANS EFFECT OF DIMENSIONALITY REDUCTION ON TOMATO LEAF IMAGE CLUSTERING USING DARKNET19 AND K-MEANS

Jurnal Ilmiah NERO Vol. 8 No. 1, 2023

Image Clustering adalah pengelompokan citra dari kelas tanpa label sebelumnya. Pada penelitian in... more Image Clustering adalah pengelompokan citra dari kelas tanpa label sebelumnya. Pada penelitian ini menggunakan citra clustering dengan data daun tomato leaf panthogen sebanyak 900 citra yang terdiri dari 3 cluster yaitu Bacterial Spot, Yellow Leaf Curl Virus dan Healthy. Pada setiap cluster memiliki 300 citra. Langkah awal yang dilakukan adalah ekstraksi fitur menggunakan DarkNet19. DarkNet19 menerapkan beberapa parameter seperti epoch sebanyak 160 dengan menggunakan Stochastic Gradient Descent, Learning Rate dimulai dari 0.01, weight decay 0.0005, dan augmentasi data termasuk random crops, rotations, hue, saturation, and exposure shifts. Selain ekstraksi fitur, pada penelitian ini juga melakukan pengurangan dimensi menggunakan Principal Component Analysis (PCA). Selanjutnya, proses clustering menggunakan K-Means Clustering. Penelitian ini bertujuan untuk mengetahui tingkat akurasi dalam proses Clustering penyakit daun tomat menggunakan metode CNN DarkNet19, PCA dan K-Means. Hasil klastering yang terbaik menggunakan K-Means Clustering dengan PCA 20 yang menghasilkan accuracy 97.7%, precission 97.73%, dan recall 97.67% dengan waktu komputasi 1 menit 16 detik.

Research paper thumbnail of Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN

ICONNSMAL 2022, AISR 177, pp. 30–38, 2023. , 2023

Tobacco is one of the largest agricultural products and is widely traded in the world market, inc... more Tobacco is one of the largest agricultural products and is widely traded in the world market, including in Indonesia. In Indonesia, tobacco leaves are used as raw material for cigarettes which are mostly produced by cigarette companies. The quality of tobacco leaves greatly affects the quality of cigarettes, this is because the condition of tobacco leaves is influenced by several factors including pests, diseases, and climate. This study uses the Gray Level Co-Occurrence Matrix (GLCM) method for texture feature extraction, while for classification uses the K-Nearest Neighbor (KNN) method to classify the quality of tobacco leaves. The data used in this study is the image of tobacco leaves taken directly in TonDowulan Village, Plandaan District, Jombang Regency at the age of the leaves of approximately 2 months. Tobacco leaf images used were 300 images consisting of 3 classes, namely Normal, Perforated, and Withered based on the level of leaf damage. The GLCM features used are Contrast, Correlation, Energy, Homogeneity, and Entropy which will then be classified using the KNN method where before performing feature extraction the data must be processed first at the preprocessing stage. The result of the training using GLCM and K-NN feature extraction produces the highest accuracy value when the neighbor value 1, pixel distance 3, and k-fold 2 are 83.33%.

Research paper thumbnail of Implementation of the Fuzzy Analytical Network Process Method in Decision Making on the Granting of Non-occupied Building Permits

Atlantis Press ICONNSMAL 2022, AISR 177, pp. 71–80, 2023., 2023

Building Permit (BP) is one of the authorities that can be given by local governments to people w... more Building Permit (BP) is one of the authorities that can be given by local governments to people who will construct buildings, both residential and non-residential buildings. The requirements for applying for BP for residential buildings are different from those for non-residential buildings. The criteria for selecting the BP granting authority are given to each region. One of the regions, namely Sampang Regency, in selecting the granting of a non-residential BP, considered several things including the completeness of the files, building layout, designation and intensity of buildings, building architecture, land suitability, environmental impact control, and community approval. The decision to grant a Nonresidential BP which was taken into consideration in the assessment, as well as the involvement of two regional apparatus as admins and appraisers caused the decision-making process to be less efficient and lack transparency. Therefore, a decision support system is needed using the Fuzzy Analytical Network Process (FANP) method to assist the decision-making process for granting non-residential BP. The FANP method is used to determine the importance of the criteria used to determine the granting of a non-residential BP permit. Based on the results of the tests that have been carried out, the accuracy of the system obtained is 97.12%. With this decision support system, it can speed up the decision-making process for granting non-residential BP with fairly accurate results.

Research paper thumbnail of Steganography on Color Images Using Least Significant Bit (LSB) Method

Atlantis Press_ICONNSMAL 2022, AISR 177, pp. 39–48, 2023, 2023

In some fields, high data security is required for data transmission. This raises concerns about ... more In some fields, high data security is required for data transmission. This raises concerns about misuse of data to irresponsible parties. To protect it, efforts were made to hide factual information on top of other information, namely steganography using the Least Significant Bit (LSB) method. This method has the advantage of a well-compressed image that is difficult to detect with the naked eye and has a fast process. In this study, the LSB method has two processes, namely the endcoding and decoding processes. The proposed method is tested and then evaluated MSE and computation time. In this research, each test uses 20 text messages and 20 images. The Text messages that were tested for insertion in images consisted of short messages, medium messages, and long messages. The text message will be inserted into the image with a small and a large image. The test results for the category of short messages inserted into thumbnails produce an average MSE value of 0.28 db, an average encoding and decoding processing time of 14 ms. The medium message category embedded in large images produces an average MSE value of 0.029 db, the average encoding and decoding time is 54ms. The long message category inserted in large images produces an average MSE value of 0.11 db, the average encoding and decoding time 1700 ms. The results of the three tests were that all text messages were successfully inserted into the images.

Research paper thumbnail of Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

ELINVO (Electronics, Informatics, and Vocational Education), vol 8(1):121-129, 2023

Image classification involves categorizing an image's pixels into specific classes based on their... more Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are timeconsuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.

Research paper thumbnail of Utilizing LSTM and K-NN for Anatomical Localization of Tuberculosis: A Solution for Incomplete Data

Mathematical Modelling of Engineering Problems, Vol 10, Issue 4, p1114, 2023

Tuberculosis (TB) is a prevalent lung disease that significantly contributes to mortality rates, ... more Tuberculosis (TB) is a prevalent lung disease that significantly contributes to mortality rates, with an estimated 98,000 fatalities observed in Indonesia alone. TB can be classified into two categories based on its anatomical location: pulmonary, when detected in lung parenchyma tissue, and extrapulmonary, when identified in organs outside the lungs. Current diagnostic procedures necessitate numerous laboratory tests and manual assessments, which are both time-consuming and susceptible to data incompleteness, thereby potentially influencing the diagnostic outcomes. This necessitates the development of a rapid and accurate classification system for the anatomical location of TB, which could aid medical professionals in diagnosis. In this study, we propose a novel classification system that utilizes the K-Nearest Neighbors (K-NN) algorithm to handle missing data, and the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing. For the classification of pulmonary and extrapulmonary TB, the study employs the Long Short-Term Memory (LSTM) method, the performance of which is compared with other models, namely Naï ve Bayes, Support Vector Machine (SVM), and Backpropagation. Although all four models demonstrated high levels of accuracy, the LSTM method outperformed the others, achieving 100% accuracy compared to Naï ve Bayes (99.4%), SVM (99.3%), and Backpropagation (99.7%). These results were obtained after implementing imputation and class balancing stages, and optimizing LSTM features such as the tanh activation function, learning rate of 0.01, 100 LSTM units, and the ADAM optimizer. The proposed system thus presents an effective solution for the rapid and accurate classification of TB based on anatomical location.

Research paper thumbnail of Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Mathematical Modelling of Engineering Problems, Vol 10, Issue 5, p1849, 2023

Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious proble... more Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries. Accurate early detection remains a challenge despite prevention efforts. The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscope. This research focuses on the use of Convolutional Neural Network (CNN) with the AlexNet, ResNet-18, ResNet-50, and VGG-16 architectures in the early detection and classification of Tuberculosis (TB) through processing images of TB patients' sputum. A dataset of sputum images was collected and processed to ensure quality and adequate representation. Each CNN model was trained using deep learning techniques on the prepared dataset. The aim of this research is to compare the performance of each model in recognizing and classifying sputum images containing Mycobacterium tuberculosis bacteria and those without TB bacteria. The research results show that AlexNet architecture outperforms ResNet-18, ResNet-50 and VGG-16 in classification accuracy of Mycobacterium tuberculosis. The best validation accuracy achieved was 93.42% with the fastest time of 5 minutes and 52 seconds using AlexNet architecture. Identifying the most appropriate AlexNet architectural model could unlock the potential for developing automated systems that efficiently identify TB, thereby enabling faster and more timely medical intervention.

Research paper thumbnail of Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram

RomanianJournalofAppliedSciencesandTechnology, 2023

Corn disease has a significant impact on both the food industry and the yield of corn crops since... more Corn disease has a significant impact on both the food industry and the yield of corn crops since corn serves as a fundamental and essential source of nutrition, especially for vegetarians and vegans. Therefore, ensuring the quality of corn is crucial, and to achieve this, protection against various diseases is necessary. Consequently, there is a pressing demand for an automated method capable of early-stage disease detection and prompt action. However, detecting diseases at an early stage poses a major challenge and is of utmost importance. This research focuses on the development of a classification model for corn stalk images using Random Forest. The model generates fine and coarse features of high quality to capture discriminative, boundary, pattern, and structural information used in the classification process. This research also utilizes the LBP (Local Binary Pattern) method and Color Histogram in the feature extraction process to obtain information related to texture and distinguishing patterns, that are employed in the classification process. Furthermore, the proposed model is evaluated using the corn plant image dataset, which was directly captured by the researcher in Madura, and consists of 3,000 data. The result of this research shows that the utilization of the proposed method can classify and identifying diseases in new data of digital images of corn stalks with an accuracy rate of 99.05%.

Research paper thumbnail of Evaluasi Keandalan Model Rekognisi Suara Burung Hama Menggunakan Platform Edge Impulse Pada Mikrokontroller Low Power

TRIAC, 2023

Penelitian ini mengekplorasi kemungkinan pemanfaatan teknologi edge machine learning dalam hal re... more Penelitian ini mengekplorasi kemungkinan
pemanfaatan teknologi edge machine learning dalam hal
rekognisi suara-suara burung hama yang bisa diaplikasikan
pada mikrokontroller ultra low power. Dalam paper ini
dilakukan uji kehandalan dari tiga algoritma mesin
pembelajaran (Machine Learning), kemudian
menyematkankannya ke mikrokontroller Seeed Xiao
NRF52840 Sense. Model pembelajaran mesin yang pertama
adalah Fast Convolutional Neural Netywork (CNNs) 1D
dengan 2 layer, model ke-2 adalah menggunakan arsitektur
berbasis transfer learning MobileNet. Dalam melakukan
training dan testing digunakan mesin pembelajaran
embedded platform Edge Impulse. Model pembelajaran yang
dihasilkan kemudian diimplementasi sebagai Arduino
Library baik sebagai representasi 32-bit floating point dan 8-
bit fixed integer. Nilai dugaan yang dihasilkan oleh
mikrokontroller dievaluasi dalam 4 kasus, yaitu
menggunakan kompiler Edge Impulse EON dan Tensor Flow
Lite (TFLite). Hasil penelitian juga melaporkan memory
footprint ( RAM dan Flash), nilai akurasi, dan waktu
dugaan (time inference)

Research paper thumbnail of TUBERCULOSIS CLASSIFICATION USING RANDOM FOREST WITH K-PROTOTYPE AS A METHOD TO OVERCOME MISSING VALUE

CMBN, 2023

Tuberculosis is a disease that attacks the core of the respiratory organs, which affects many peo... more Tuberculosis is a disease that attacks the core of the respiratory organs, which affects many people. This disease is one of the contributors to high mortality cases, especially in Indonesia. Based on its anatomical location, tuberculosis is divided into two classes, namely pulmonary for tuberculosis detected in lung parenchymal tissue and extrapulmonary for tuberculosis detected in organs other than the lungs. Detecting the location of the infection in the lungs requires some analysis of laboratory results for the triggering parameters where the analysis process is still done manually, so it takes longer, and because the input process is still done manually, patient data which causes the possibility of human error to be very high. Therefore, the solution offered and the aim of this study is the ease of patient diagnosis in determining the classification of TB disease. The method used in this study is k-prototype imputation to repair missing values that have different data types, then for tuberculosis data classification methods 2 ROCHMAN, MISWANTO, SUPRAJITNO, KAMILAH, RACHMAD, SANTOSA and medical record data processing using the Random Forest, Support Vector Machine, and Backpropagation methods. Of the three classification methods proposed in this study, all three have an excellent level of accuracy. However, the Random Forest method performs more than other methods, reaching 98.8%.

Research paper thumbnail of Penerapan Digitalisasi Data Umkm Berbasis Website Untuk Monitoring UMKM Di Desa Saroka

Jurnal Abdiwangi | Jurnal Pegabdian Kepada Masyarakat, 2023

Potensi ekonomi lokal pedesaan dapat menjadi salah satu faktor pendukung pembangunan desa yang da... more Potensi ekonomi lokal pedesaan dapat menjadi salah satu faktor
pendukung pembangunan desa yang dapat dimanfaatkan oleh
masyarakat untuk menciptakan nilai tambah. Salah satu cara yang
dapat membangun ekonomi masayarakat pedesaan adalah
dengan mendirikan usaha mikro, kecil dan menengah atau disebut
dengan UMKM. sektor UMKM berperan penting untuk memajukan
perekonomian masyarakat maupun negara. Desa memiliki peran
untuk mendukung pembangunan pada sektor tersebut. salah satu
fungsi desa adalah memberikan sarana prasarana terhadap
masyarakat desa salahsatunya dukungan terhadap UMKM yang
ada didesa. Saat ini desa masih mengalami kesulitan untuk
melakukan pendataan UMKM karena tumbuhnya usaha mikro
masyarakat tersebut seringkali tidak melibatkan desa. Dengan
demikian desa masih belum memiliki data induk UMKM hingga
data perkembangan UMKM tersebut secara realtime. Hal ini
menyebabkan desa tidak dapat mengambil keputusan secara tepat
untuk memberikan dukungan pada UMKM

Research paper thumbnail of Pengembangan Media Promosi Sekolah SMKS Al-Muhajirin Berbasis Augmented Reality Menggunakan Metode Marker Based Tracking

Jurnal Explore IT, 2023

Penelitian dan pengembangan ini bertujuan untuk mengatasi masalah dalam media promosi sekolah dan... more Penelitian dan pengembangan ini bertujuan untuk mengatasi masalah dalam media promosi sekolah dan kurangnya daya tarik serta keterbatasan dalam penyampaian informasi sekolah, mengakibatkan masih sedikitnya para calon siswa dan masyarakat untuk mengetahui informasi tentang sekolah. Produk yang dihasilkan oleh peneliti berupa media promosi sekolah SMKS Al-Muhajirin berbasis Augmented Reality menggunakan metode Marker Based Tracking yang dapat menampilkan informasi tambahan yang tidak dapat dilihat secara langsung dalam dunia nyata. Proses pengembangan produk ini mengadopsi model penelitian dan pengembangan SPRINT dengan tahapan tahapan yaitu understand, diverge, decide, prototype, validate. Evaluasi hasil terkait materi dan media menunjukkan bahwa produk yang telah dikembangkan memperoleh penilaian yang positif dan layak digunakan. Validasi oleh ahli materi menghasilkan proporsi sebesar 95%, dengan kualifikasi yang sangat layak. Sementara itu validasi oleh ahli media menggunakan standar ISO 25010 menunjukkan adanya variasi persentase, terutama pada pengujian marker based tracking memperoleh kualifikasi sangat layak dengan cara pengujiannya menggunakan jarak marker yang berbeda dan menggunakan variasi kecerahan cahaya dari terang, sangat terang, redup, sampai sangat redup dari hasil uji intensitas cahaya 100% dan uji jarak marker 100% semua hasil menunjukkan bahwa produk ini memenuhi syarat untuk digunakan. Selain itu, hasil uji coba yang melibatkan peserta didik juga menunjukkan tingkat kualifikasi yang sangat tinggi, dengan proporsi sebesar 94,68% dan 87,46%, dan memenuhi kriteria kualifikasi yang sangat layak. Hasil tersebut menunjukan bahwa media promosi tersebut memiliki keabsahan dan potensial yang layak untuk dijadikan pendukung dalam kegiatan promosi sekolah.

Research paper thumbnail of COMPARISON OF BACKPROPAGATION AND ERNN METHODS IN PREDICTING CORN PRODUCTION

Communications in Mathematical Biology and Neuroscience (CMBN), 2022

East Java is one of the producers of food crops in Indonesia. Some food crop commodities in East ... more East Java is one of the producers of food crops in Indonesia. Some food crop commodities in East Java Province are corn, soybeans, peanuts, sweet potatoes, and cassava. These food crops have many benefits to make the demand for production increase. The uncertain amount of food crop production will be a problem for the Department of Agriculture and Food Security of East Java Province in determining a policy. To overcome this problem, a system is needed to predict the production of food crops in East Java. This study compares the Backpropagation algorithm and Elman Recurrent Neural Networks (ERNN). The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 per quarter. The result of this research is that trial scenario 1 produces the best MSE value of 0.00000063 on the Backpropagation algorithm compared to ERNN which only gets an MSE value of 0.00000627. Trial scenario 2 produces the best MSE value, which is 0.000000003 in the Backpropagation algorithm with gradient descent momentum, this is also better when compared to ERNN which gets an MSE value of 0.00000407. It can be concluded that the best algorithm in this study is Backpropagation with gradient descent momentum because it produces MSE values with good prediction results from all algorithms compared.

Research paper thumbnail of Optimization Energy Consumption Using Constrains Temperature and Fan Speed on Salt Dryer Machine Based on Taguchi L9 Statistics Model

Jurnal Internasional Bereputasi MMEP, 2025

Madura island meet mostly of the salt demand in Indonesia, Sampang, Pamekasan, and Sumenep are th... more Madura island meet mostly of the salt demand in Indonesia, Sampang, Pamekasan, and Sumenep are three of the four districts of the island that produce salt. Salt ponds run on traditional way over generations, this situation is highly dependent on sunlight in the salt drying process. This research aims to find the optimal parameters on the salt drying machine on the efficiency of energy consumption on the machine. Optimization using Taguchi orthogonal array L9. General linear ANOVA used to define contribution level of each parameter. Fan speed parameter has 3 levels namely; low, medium, and high. Drying temperature has 3 levels they are; 18℃, 20℃ and 22℃. Quality control term on this research is smaller better, the less energy consumption during drying process leads to efficiency cost for dryer machine operation. The lowest energy consumption found on this research is 4687 kWh, achieved on temperature 20℃ and low fan speed. The highest energy consumption found on research is 5492 kWh, achieved on temperature 20℃ and low fan speed. Based on Taguchi L9 recommendation optimum combination is fan speed low, 20℃ on drying temperature. Parameter drying temperature is dominant compared to parameter fan speed. Parameter fan speed has contribution 0.64% and parameter dryer temperature process has contribution 94.7%.

Research paper thumbnail of Geometry Algorithm on Skeleton Image Based Semaphore Gesture Recognition

Semaphore, a way of communicating remotely, usually practiced in scouting activities. Information... more Semaphore, a way of communicating remotely, usually practiced in scouting activities. Information is delivered by gestures or movements using specific tools such as flags, paddles or rods. Teacher and instructors are needed for learning semaphore in conventional way as they will give examples and make correction when such an error occured. Based on the practical need to provide an alternative way to learn semaphore, this research proposes the use of geometry algorithm to develop a semaphore gesture recognition based on skeleton images that read from Kinect sensor. Euclidean distance and law cosines are two formulas that applied to generate gesture parameters of each alphabet. Recognition is achieved by comparing a pair of values of model and real-time gesture. Accuracy of this system that have been measured using RMSE with 30° of tolerance yields 90.76% for Alphabet and 88% for Word.

Research paper thumbnail of Application of ant colony optimization algorithm to determine optimal value in choosing tourist attractions in Bangkalan -Madura

AIP Conference Proceedings 2679, 020009 , 2023

The impact of COVID-19 leaves a deep sadness, especially in the tourism sector. Many tourist plac... more The impact of COVID-19 leaves a deep sadness, especially in the tourism sector. Many tourist places are not open to prevent the transmission of this virus. Madura is one of the islands located in the province of East Java which has many tourist attractions. Bangkalan is one of the districts in Madura which has 21 tourism spots. There are many tourist attractions scattered in Bangkalan district, but there is a problem arisen in determining the shortest path to get to these tourist attractions. There are several route options available in each area. Optimal value search can be used to obtain the highest and lowest values of a problem. One of the popular problems that can be solved by optimization algorithms is the Traveling Salesman Problem (TSP) to determine the closest route using the Ant Colony Optimization algorithm. The ant algorithm is an algorithm adopted from the behaviour of the ant colony. An ant colony can find the shortest route between the nest and a food source based on footprints on the trajectory it has traversed. The more ants that pass through a track, the clearer the footprints will be. Ant Colony algorithm is very appropriate to be applied in solving optimization problems, one of which is to determine the shortest path. The final result of this discussion is the algorithm used is able to determine the shortest path to find tourist destinations as an alternative route. The accuracy results obtained are 100% with a rho value of 0.5, an alpha value of 1, and a beta value of 1.

Research paper thumbnail of Prediction of corn crop yield using backpropagation neural network algorithm

AIP Conference Proceedings 2679, 020006 , 2023

Corn is one type of food crop commodity in Indonesia. Malang Regency is one of the producers that... more Corn is one type of food crop commodity in Indonesia. Malang Regency is one of the producers that ranks 10th in corn production in the East Java region. People are very interested in planting corn because this crop commodity has many benefits so as to make the demand for production increase. There was a significant increase in market demand, but the uncertain amount of production made the supply of corn plants unable to be fulfilled properly. In this study, it predicted the demand for corn by using the Backpropagation Neural Network algorithm in Malang Regency. The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 every month using maize data from the Malang area. The results showed that the backpropagation algorithm produced an MSE value of 0.00004178.

Research paper thumbnail of Analysis average waiting time search performance in the queue process on CPU scheduling using the Round Robin, shortest job first and first in first out algorithm

AIP Conference Proceedings 2679, 020010 , 2023

In its activities, the CPU has a pattern of processes that coincide or precede each other. Where ... more In its activities, the CPU has a pattern of processes that coincide or precede each other. Where when one activity enters, another activity will come, and so on which is multiprogramming. Round Robin (RR), Shortest Job First (SJF), and First In First Out (FIFO) methods are CPU scheduling algorithms that are popularly used in multiprogramming systems. These three algorithms are used to find Average Waiting Time (AWT) with different process sequences. In the Round Robin algorithm, there is Qu that affects the number of process sequences. The AWT value is obtained from the number of waiting for processes divided by the number of existing processes. The makespan value in each algorithm must be the same even though the process sequence is different depending on the arrival time. This study describes and analyzes how the RR, SJF, and FIFO algorithms work in the queuing process on the CPU (Central Processing Unit). The result of this research is an analysis that is easy to understand in the form of an interactive programming application. In the resulting application, it can be seen that the queuing process in AWT runs in stages according to the principles of the three algorithms.

Research paper thumbnail of Clustering tourism places in Madura based facilities using fuzzy C-means

AIP Conference Proceedings 2679, 020007 , 2023

Madura Island consists of four districts namely Bangkalan, Sampang, Pamekasan, and Sumenep. Each ... more Madura Island consists of four districts namely Bangkalan, Sampang, Pamekasan, and Sumenep. Each district has many choices of interesting tourist attractions. There are various types of tourism ranging from natural tourism, cultural tourism, historical tourism, and artificial tourism. With the diversity of these tourist attractions, it is enough to invite many tourists to come on vacation to Madura Island. Each tourist attraction has a different number of visitors, the number of public facilities, and ticket prices. All the criteria possessed by each tourist attraction have their own assessment for potential tourists. The local Tourism Office must know the developments in each tourist attraction, so that it can maintain the quality of the tourist attraction. Improvement of infrastructure can be through public facilities provided at tourist objects is very necessary. The purpose of this study was to determine how well the Fuzzy C-Means method in grouping tourism objects in Madura. Fuzzy C-Means is a grouping method which the development of the k-means cluster is not a hierarchical method that allocates data into each group by utilizing fuzzy set theory. From the trials that have been carried out, the best grouping results are found in cluster 10 with a silhouette coefficient value of 0.825 which is included in the strong category.

Research paper thumbnail of Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Mathematical Modelling of Engineering Problems Vol. 10, No. 2, pp. 530-536, 2023

The decline in corn production can affect the continuity of food grown in society, especially in ... more The decline in corn production can affect the continuity of food grown in society, especially in Indonesia, which is a country with a high level of corn consumers. Several factors cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged. Therefore, a system is needed that can identify diseases in corn plants so that appropriate treatment can be carried out as early as possible to prevent severe damage to corn plants. With this research, the system can be built by utilizing machine learning in building a classification system using the Convolutional Neural Network (CNN) algorithm with a dataset of corn leaf images taken from farmers' fields in the Madura Region with four target classes namely healthy, gray leaf spot, blight, and common rust. Testing was carried out using several CNN architectural models such as SqueezeNet, AlexNet, ResNet-101, ResNet-50, and ResNet-18. The parameters used were 5 epochs with 100 iterations, a learning rate of 0.0001, using Adam optimization, and a data distribution of 70% for training data and 30% for testing data. The test results obtained in classifying corn images using the Convolutional Neural Network method with the ResNet-50 architecture provide a very good accuracy value of 95.59%.

Research paper thumbnail of PENGARUH REDUKSI DIMENSI PADA CLUSTERING CITRA DAUN TOMAT MENGGUNAKAN DARKNET19 DAN K-MEANS EFFECT OF DIMENSIONALITY REDUCTION ON TOMATO LEAF IMAGE CLUSTERING USING DARKNET19 AND K-MEANS

Jurnal Ilmiah NERO Vol. 8 No. 1, 2023

Image Clustering adalah pengelompokan citra dari kelas tanpa label sebelumnya. Pada penelitian in... more Image Clustering adalah pengelompokan citra dari kelas tanpa label sebelumnya. Pada penelitian ini menggunakan citra clustering dengan data daun tomato leaf panthogen sebanyak 900 citra yang terdiri dari 3 cluster yaitu Bacterial Spot, Yellow Leaf Curl Virus dan Healthy. Pada setiap cluster memiliki 300 citra. Langkah awal yang dilakukan adalah ekstraksi fitur menggunakan DarkNet19. DarkNet19 menerapkan beberapa parameter seperti epoch sebanyak 160 dengan menggunakan Stochastic Gradient Descent, Learning Rate dimulai dari 0.01, weight decay 0.0005, dan augmentasi data termasuk random crops, rotations, hue, saturation, and exposure shifts. Selain ekstraksi fitur, pada penelitian ini juga melakukan pengurangan dimensi menggunakan Principal Component Analysis (PCA). Selanjutnya, proses clustering menggunakan K-Means Clustering. Penelitian ini bertujuan untuk mengetahui tingkat akurasi dalam proses Clustering penyakit daun tomat menggunakan metode CNN DarkNet19, PCA dan K-Means. Hasil klastering yang terbaik menggunakan K-Means Clustering dengan PCA 20 yang menghasilkan accuracy 97.7%, precission 97.73%, dan recall 97.67% dengan waktu komputasi 1 menit 16 detik.

Research paper thumbnail of Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN

ICONNSMAL 2022, AISR 177, pp. 30–38, 2023. , 2023

Tobacco is one of the largest agricultural products and is widely traded in the world market, inc... more Tobacco is one of the largest agricultural products and is widely traded in the world market, including in Indonesia. In Indonesia, tobacco leaves are used as raw material for cigarettes which are mostly produced by cigarette companies. The quality of tobacco leaves greatly affects the quality of cigarettes, this is because the condition of tobacco leaves is influenced by several factors including pests, diseases, and climate. This study uses the Gray Level Co-Occurrence Matrix (GLCM) method for texture feature extraction, while for classification uses the K-Nearest Neighbor (KNN) method to classify the quality of tobacco leaves. The data used in this study is the image of tobacco leaves taken directly in TonDowulan Village, Plandaan District, Jombang Regency at the age of the leaves of approximately 2 months. Tobacco leaf images used were 300 images consisting of 3 classes, namely Normal, Perforated, and Withered based on the level of leaf damage. The GLCM features used are Contrast, Correlation, Energy, Homogeneity, and Entropy which will then be classified using the KNN method where before performing feature extraction the data must be processed first at the preprocessing stage. The result of the training using GLCM and K-NN feature extraction produces the highest accuracy value when the neighbor value 1, pixel distance 3, and k-fold 2 are 83.33%.

Research paper thumbnail of Implementation of the Fuzzy Analytical Network Process Method in Decision Making on the Granting of Non-occupied Building Permits

Atlantis Press ICONNSMAL 2022, AISR 177, pp. 71–80, 2023., 2023

Building Permit (BP) is one of the authorities that can be given by local governments to people w... more Building Permit (BP) is one of the authorities that can be given by local governments to people who will construct buildings, both residential and non-residential buildings. The requirements for applying for BP for residential buildings are different from those for non-residential buildings. The criteria for selecting the BP granting authority are given to each region. One of the regions, namely Sampang Regency, in selecting the granting of a non-residential BP, considered several things including the completeness of the files, building layout, designation and intensity of buildings, building architecture, land suitability, environmental impact control, and community approval. The decision to grant a Nonresidential BP which was taken into consideration in the assessment, as well as the involvement of two regional apparatus as admins and appraisers caused the decision-making process to be less efficient and lack transparency. Therefore, a decision support system is needed using the Fuzzy Analytical Network Process (FANP) method to assist the decision-making process for granting non-residential BP. The FANP method is used to determine the importance of the criteria used to determine the granting of a non-residential BP permit. Based on the results of the tests that have been carried out, the accuracy of the system obtained is 97.12%. With this decision support system, it can speed up the decision-making process for granting non-residential BP with fairly accurate results.

Research paper thumbnail of Steganography on Color Images Using Least Significant Bit (LSB) Method

Atlantis Press_ICONNSMAL 2022, AISR 177, pp. 39–48, 2023, 2023

In some fields, high data security is required for data transmission. This raises concerns about ... more In some fields, high data security is required for data transmission. This raises concerns about misuse of data to irresponsible parties. To protect it, efforts were made to hide factual information on top of other information, namely steganography using the Least Significant Bit (LSB) method. This method has the advantage of a well-compressed image that is difficult to detect with the naked eye and has a fast process. In this study, the LSB method has two processes, namely the endcoding and decoding processes. The proposed method is tested and then evaluated MSE and computation time. In this research, each test uses 20 text messages and 20 images. The Text messages that were tested for insertion in images consisted of short messages, medium messages, and long messages. The text message will be inserted into the image with a small and a large image. The test results for the category of short messages inserted into thumbnails produce an average MSE value of 0.28 db, an average encoding and decoding processing time of 14 ms. The medium message category embedded in large images produces an average MSE value of 0.029 db, the average encoding and decoding time is 54ms. The long message category inserted in large images produces an average MSE value of 0.11 db, the average encoding and decoding time 1700 ms. The results of the three tests were that all text messages were successfully inserted into the images.

Research paper thumbnail of Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

ELINVO (Electronics, Informatics, and Vocational Education), vol 8(1):121-129, 2023

Image classification involves categorizing an image's pixels into specific classes based on their... more Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are timeconsuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.

Research paper thumbnail of Utilizing LSTM and K-NN for Anatomical Localization of Tuberculosis: A Solution for Incomplete Data

Mathematical Modelling of Engineering Problems, Vol 10, Issue 4, p1114, 2023

Tuberculosis (TB) is a prevalent lung disease that significantly contributes to mortality rates, ... more Tuberculosis (TB) is a prevalent lung disease that significantly contributes to mortality rates, with an estimated 98,000 fatalities observed in Indonesia alone. TB can be classified into two categories based on its anatomical location: pulmonary, when detected in lung parenchyma tissue, and extrapulmonary, when identified in organs outside the lungs. Current diagnostic procedures necessitate numerous laboratory tests and manual assessments, which are both time-consuming and susceptible to data incompleteness, thereby potentially influencing the diagnostic outcomes. This necessitates the development of a rapid and accurate classification system for the anatomical location of TB, which could aid medical professionals in diagnosis. In this study, we propose a novel classification system that utilizes the K-Nearest Neighbors (K-NN) algorithm to handle missing data, and the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing. For the classification of pulmonary and extrapulmonary TB, the study employs the Long Short-Term Memory (LSTM) method, the performance of which is compared with other models, namely Naï ve Bayes, Support Vector Machine (SVM), and Backpropagation. Although all four models demonstrated high levels of accuracy, the LSTM method outperformed the others, achieving 100% accuracy compared to Naï ve Bayes (99.4%), SVM (99.3%), and Backpropagation (99.7%). These results were obtained after implementing imputation and class balancing stages, and optimizing LSTM features such as the tanh activation function, learning rate of 0.01, 100 LSTM units, and the ADAM optimizer. The proposed system thus presents an effective solution for the rapid and accurate classification of TB based on anatomical location.

Research paper thumbnail of Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Mathematical Modelling of Engineering Problems, Vol 10, Issue 5, p1849, 2023

Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious proble... more Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries. Accurate early detection remains a challenge despite prevention efforts. The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscope. This research focuses on the use of Convolutional Neural Network (CNN) with the AlexNet, ResNet-18, ResNet-50, and VGG-16 architectures in the early detection and classification of Tuberculosis (TB) through processing images of TB patients' sputum. A dataset of sputum images was collected and processed to ensure quality and adequate representation. Each CNN model was trained using deep learning techniques on the prepared dataset. The aim of this research is to compare the performance of each model in recognizing and classifying sputum images containing Mycobacterium tuberculosis bacteria and those without TB bacteria. The research results show that AlexNet architecture outperforms ResNet-18, ResNet-50 and VGG-16 in classification accuracy of Mycobacterium tuberculosis. The best validation accuracy achieved was 93.42% with the fastest time of 5 minutes and 52 seconds using AlexNet architecture. Identifying the most appropriate AlexNet architectural model could unlock the potential for developing automated systems that efficiently identify TB, thereby enabling faster and more timely medical intervention.

Research paper thumbnail of Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram

RomanianJournalofAppliedSciencesandTechnology, 2023

Corn disease has a significant impact on both the food industry and the yield of corn crops since... more Corn disease has a significant impact on both the food industry and the yield of corn crops since corn serves as a fundamental and essential source of nutrition, especially for vegetarians and vegans. Therefore, ensuring the quality of corn is crucial, and to achieve this, protection against various diseases is necessary. Consequently, there is a pressing demand for an automated method capable of early-stage disease detection and prompt action. However, detecting diseases at an early stage poses a major challenge and is of utmost importance. This research focuses on the development of a classification model for corn stalk images using Random Forest. The model generates fine and coarse features of high quality to capture discriminative, boundary, pattern, and structural information used in the classification process. This research also utilizes the LBP (Local Binary Pattern) method and Color Histogram in the feature extraction process to obtain information related to texture and distinguishing patterns, that are employed in the classification process. Furthermore, the proposed model is evaluated using the corn plant image dataset, which was directly captured by the researcher in Madura, and consists of 3,000 data. The result of this research shows that the utilization of the proposed method can classify and identifying diseases in new data of digital images of corn stalks with an accuracy rate of 99.05%.

Research paper thumbnail of Evaluasi Keandalan Model Rekognisi Suara Burung Hama Menggunakan Platform Edge Impulse Pada Mikrokontroller Low Power

TRIAC, 2023

Penelitian ini mengekplorasi kemungkinan pemanfaatan teknologi edge machine learning dalam hal re... more Penelitian ini mengekplorasi kemungkinan
pemanfaatan teknologi edge machine learning dalam hal
rekognisi suara-suara burung hama yang bisa diaplikasikan
pada mikrokontroller ultra low power. Dalam paper ini
dilakukan uji kehandalan dari tiga algoritma mesin
pembelajaran (Machine Learning), kemudian
menyematkankannya ke mikrokontroller Seeed Xiao
NRF52840 Sense. Model pembelajaran mesin yang pertama
adalah Fast Convolutional Neural Netywork (CNNs) 1D
dengan 2 layer, model ke-2 adalah menggunakan arsitektur
berbasis transfer learning MobileNet. Dalam melakukan
training dan testing digunakan mesin pembelajaran
embedded platform Edge Impulse. Model pembelajaran yang
dihasilkan kemudian diimplementasi sebagai Arduino
Library baik sebagai representasi 32-bit floating point dan 8-
bit fixed integer. Nilai dugaan yang dihasilkan oleh
mikrokontroller dievaluasi dalam 4 kasus, yaitu
menggunakan kompiler Edge Impulse EON dan Tensor Flow
Lite (TFLite). Hasil penelitian juga melaporkan memory
footprint ( RAM dan Flash), nilai akurasi, dan waktu
dugaan (time inference)

Research paper thumbnail of TUBERCULOSIS CLASSIFICATION USING RANDOM FOREST WITH K-PROTOTYPE AS A METHOD TO OVERCOME MISSING VALUE

CMBN, 2023

Tuberculosis is a disease that attacks the core of the respiratory organs, which affects many peo... more Tuberculosis is a disease that attacks the core of the respiratory organs, which affects many people. This disease is one of the contributors to high mortality cases, especially in Indonesia. Based on its anatomical location, tuberculosis is divided into two classes, namely pulmonary for tuberculosis detected in lung parenchymal tissue and extrapulmonary for tuberculosis detected in organs other than the lungs. Detecting the location of the infection in the lungs requires some analysis of laboratory results for the triggering parameters where the analysis process is still done manually, so it takes longer, and because the input process is still done manually, patient data which causes the possibility of human error to be very high. Therefore, the solution offered and the aim of this study is the ease of patient diagnosis in determining the classification of TB disease. The method used in this study is k-prototype imputation to repair missing values that have different data types, then for tuberculosis data classification methods 2 ROCHMAN, MISWANTO, SUPRAJITNO, KAMILAH, RACHMAD, SANTOSA and medical record data processing using the Random Forest, Support Vector Machine, and Backpropagation methods. Of the three classification methods proposed in this study, all three have an excellent level of accuracy. However, the Random Forest method performs more than other methods, reaching 98.8%.

Research paper thumbnail of Penerapan Digitalisasi Data Umkm Berbasis Website Untuk Monitoring UMKM Di Desa Saroka

Jurnal Abdiwangi | Jurnal Pegabdian Kepada Masyarakat, 2023

Potensi ekonomi lokal pedesaan dapat menjadi salah satu faktor pendukung pembangunan desa yang da... more Potensi ekonomi lokal pedesaan dapat menjadi salah satu faktor
pendukung pembangunan desa yang dapat dimanfaatkan oleh
masyarakat untuk menciptakan nilai tambah. Salah satu cara yang
dapat membangun ekonomi masayarakat pedesaan adalah
dengan mendirikan usaha mikro, kecil dan menengah atau disebut
dengan UMKM. sektor UMKM berperan penting untuk memajukan
perekonomian masyarakat maupun negara. Desa memiliki peran
untuk mendukung pembangunan pada sektor tersebut. salah satu
fungsi desa adalah memberikan sarana prasarana terhadap
masyarakat desa salahsatunya dukungan terhadap UMKM yang
ada didesa. Saat ini desa masih mengalami kesulitan untuk
melakukan pendataan UMKM karena tumbuhnya usaha mikro
masyarakat tersebut seringkali tidak melibatkan desa. Dengan
demikian desa masih belum memiliki data induk UMKM hingga
data perkembangan UMKM tersebut secara realtime. Hal ini
menyebabkan desa tidak dapat mengambil keputusan secara tepat
untuk memberikan dukungan pada UMKM

Research paper thumbnail of Pengembangan Media Promosi Sekolah SMKS Al-Muhajirin Berbasis Augmented Reality Menggunakan Metode Marker Based Tracking

Jurnal Explore IT, 2023

Penelitian dan pengembangan ini bertujuan untuk mengatasi masalah dalam media promosi sekolah dan... more Penelitian dan pengembangan ini bertujuan untuk mengatasi masalah dalam media promosi sekolah dan kurangnya daya tarik serta keterbatasan dalam penyampaian informasi sekolah, mengakibatkan masih sedikitnya para calon siswa dan masyarakat untuk mengetahui informasi tentang sekolah. Produk yang dihasilkan oleh peneliti berupa media promosi sekolah SMKS Al-Muhajirin berbasis Augmented Reality menggunakan metode Marker Based Tracking yang dapat menampilkan informasi tambahan yang tidak dapat dilihat secara langsung dalam dunia nyata. Proses pengembangan produk ini mengadopsi model penelitian dan pengembangan SPRINT dengan tahapan tahapan yaitu understand, diverge, decide, prototype, validate. Evaluasi hasil terkait materi dan media menunjukkan bahwa produk yang telah dikembangkan memperoleh penilaian yang positif dan layak digunakan. Validasi oleh ahli materi menghasilkan proporsi sebesar 95%, dengan kualifikasi yang sangat layak. Sementara itu validasi oleh ahli media menggunakan standar ISO 25010 menunjukkan adanya variasi persentase, terutama pada pengujian marker based tracking memperoleh kualifikasi sangat layak dengan cara pengujiannya menggunakan jarak marker yang berbeda dan menggunakan variasi kecerahan cahaya dari terang, sangat terang, redup, sampai sangat redup dari hasil uji intensitas cahaya 100% dan uji jarak marker 100% semua hasil menunjukkan bahwa produk ini memenuhi syarat untuk digunakan. Selain itu, hasil uji coba yang melibatkan peserta didik juga menunjukkan tingkat kualifikasi yang sangat tinggi, dengan proporsi sebesar 94,68% dan 87,46%, dan memenuhi kriteria kualifikasi yang sangat layak. Hasil tersebut menunjukan bahwa media promosi tersebut memiliki keabsahan dan potensial yang layak untuk dijadikan pendukung dalam kegiatan promosi sekolah.

Research paper thumbnail of COMPARISON OF BACKPROPAGATION AND ERNN METHODS IN PREDICTING CORN PRODUCTION

Communications in Mathematical Biology and Neuroscience (CMBN), 2022

East Java is one of the producers of food crops in Indonesia. Some food crop commodities in East ... more East Java is one of the producers of food crops in Indonesia. Some food crop commodities in East Java Province are corn, soybeans, peanuts, sweet potatoes, and cassava. These food crops have many benefits to make the demand for production increase. The uncertain amount of food crop production will be a problem for the Department of Agriculture and Food Security of East Java Province in determining a policy. To overcome this problem, a system is needed to predict the production of food crops in East Java. This study compares the Backpropagation algorithm and Elman Recurrent Neural Networks (ERNN). The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 per quarter. The result of this research is that trial scenario 1 produces the best MSE value of 0.00000063 on the Backpropagation algorithm compared to ERNN which only gets an MSE value of 0.00000627. Trial scenario 2 produces the best MSE value, which is 0.000000003 in the Backpropagation algorithm with gradient descent momentum, this is also better when compared to ERNN which gets an MSE value of 0.00000407. It can be concluded that the best algorithm in this study is Backpropagation with gradient descent momentum because it produces MSE values with good prediction results from all algorithms compared.

Research paper thumbnail of Pemikiran Akademisi Tentang Pengembangan Teknologi Informasi dan Pembangunan Kepulauan Madura

Pengukuhan Guru Besar UTM-ISBN 978-634-7091-71-0, 2025

Puji syukur kami panjatkan kehadirat Tuhan Yang Maha Esa yang telah memberikan rahmat serta karun... more Puji syukur kami panjatkan kehadirat Tuhan Yang Maha Esa yang telah
memberikan rahmat serta karunia-Nya kepada kami sehingga kami berhasil menyelesaikan Buku dengan judul Pidato Pengukuhan Guru Besar sesuai yang ditargetkan.
Buku ini berisikan peranan sistem informasi geografis dalam
pengelolaan ekosistem pesisir dan pulau-pulau keci, peranan data science dalam perkembangan dunia medis dan agriculture melalui data citra, inovasi varietas jagung unggul dalam mendukung ketahanan dan kedaulatan pangan di pulau madura, potensi dan pengembangan sumber daya genetik lokal madura untuk pemuliaan ketahanan tanaman terhadap cekaman abiotik kekeringan, inovasi teknologi deteksi abnormal paru-paru berbasis artificial intelligence untuk mempercepat diagnosis penyakit dalam.
Kami menyadari bahwa buku ini masih jauh dari sempurna, oleh karena
itu kritik dan saran dari semua pihak yang bersifat membangun selalu kami harapkan demi kesempurnaan buku ini.
Akhir kata, kami sampaikan terima kasih kepada semua pihak yang telah
berperan serta dalam penyusunan buku ini dari awal sampai akhir. Semoga Tuhan yang maha esa senantiasa meridhoi segala usaha kita. Amin.

Research paper thumbnail of DATA SCIENCES : Klasifikasi Bakteri Tuberculosis dengan Pendekatan Pengolahan Citra Digital dan Deep Learning

Buku Referensi, 2024

Puji syukur kami panjatkan kehadirat Tuhan Yang Maha Esa yang telah memberikan rahmat serta karun... more Puji syukur kami panjatkan kehadirat Tuhan Yang Maha Esa yang telah memberikan rahmat serta karunia-Nya kepada kami sehingga kami berhasil menyelesaikan Buku dengan judul DATA SCIENCES : Klasifikasi Bakteri Tuberculosis dengan Pendekatan Pengolahan Citra Digital dan Deep Learning sesuai yang ditargetkan.
Buku ini berisikan pendahuluan mengenai tuberkulosis, sejarah dan epidemiologi tuberkulosis, serta jenis-jenis tuberkulosis. Dalam buku ini juga membahas pengolahan citra medis mulai dari definisi citra, prinsip dasar pengolahan citra, alat dan teknik pengolahan citra hingga pentingnya citra medis dalam pengolahan. Kami menyadari bahwa Buku ini masih jauh dari sempurna, oleh karena itu kritik dan saran dari semua pihak yang bersifat membangun selalu kami harapkan demi kesempurnaan buku ini.
Akhir kata, kami sampaikan terima kasih kepada semua pihak yang telah berperan serta dalam penyusunan Buku ini dari awal sampai akhir. Semoga Tuhan Yang Maha Esa senantiasa meridhoi segala usaha kita. Amin.

Research paper thumbnail of ALGORITMA PEMOGRAMAN Pendekatan Praktis Menggunakan Python

Research paper thumbnail of Sistem Pendukung Keputusan Konsep dan Aplikasi

Rumah Cemerlang (RC), 2021

Research paper thumbnail of KECERDASAN KOMPUTASIONAL Konsep dan Aplikasi

P uji syukur kita panjatkan kehadirat Allah SWT, karena dengan karunia serta rahmatnya kami dapat... more P
uji syukur kita panjatkan kehadirat Allah SWT, karena dengan karunia serta rahmatnya kami dapat menyelesaikan buku ajar “Kecerdasan Komputasional” ini. Materi dalam buku ini telah disesuaikan dengan Propram Pembelajaran (PP) dan Rencana Pembelajaran Semester (RPS) Fakultas Teknik. Secara garis besar buku ini, membahas tentang bagaimana menyelesaikan masalah menggunakan kecerdasan komputasional. Serta metode apa saja yang dapat digunakan untuk membantu menyelesaikan permasalahan.
Buku ini dapat dipergunakan oleh mahasiswa jurusan teknik informatika untuk lebih memahami tentang mata kuliah kecerdasan komputasional yang merupakan mata kuliah wajib pada jurusan Teknik Informatika.
Dengan selesainya buku ajar ini, tak lupa kami ucapkan terimakasih kepada Dekan Fakultas Teknik atas semua fasilitas yang telah disediakan demi kelancaran buku ajar ini. Terima kasih rekan-rekan dosen jurusan Teknik Informatika dan Teknik Multimedia Jaringan atas bantuan dan kerjasamanya. Besar harapan kami semoga buku ajar ini dapat bermanfaat, serta demi penyempurnaan buku ajar ini kami mohon saran dan kritik dari para pembaca.

Research paper thumbnail of Determination of K values in the K means clustering algorithm for national salt commodities

ICCGANT, 2023

Indonesia is a country with a region that has great potential in the maritime sector, 70% of its ... more Indonesia is a country with a region that has great potential in the maritime sector, 70% of its territory consists of 17,508 islands making Indonesia a maritime country with a coastline that stretches 108,000 km. Indonesia has unprecedented Salt Lake products from different locales like Sampang, Cirebon, Rembang, and Indramayu. These resources need to be detected and inventoried in an appropriate manner. Local communities are increasingly cornered and finding it difficult to compete. For the essential ware bunch, by keeping up with the security of the salt product in the public arena, whose capability is indispensable, the salt exchanging framework is directed. The examination objective is to utilize the K-Means Bunching strategy to bunch public salt products. Clustering is a method for data analysis, where each object is identified and grouped based on similarities or similarities in several aspects. The result of the testing the K-Means Clustering technique with the Silhouette Coefficient strategy show that the K-Means Clustering strategy with the nearest distance determined involving Euclidean Distance is a decent decision for research. The Silhouette Coefficient evaluation yielded results with k=2 and the value of 0.721

Research paper thumbnail of Classification of Farmer Groups Using the Fuzzy Analytic Hierarchy Process Method

AIP Conf. Proc. 3250, 2025

Agriculture is a sector that has an important role in the economy in Indonesia. The location of t... more Agriculture is a sector that has an important role in the economy in Indonesia. The location of the State of Indonesia itself is on the equator which makes the land fertile and suitable as agricultural land. To increase agricultural productivity, the role of farmer groups is needed. Farmer groups with a high class have the opportunity to produce high productivity as well. Farmer group classes are divided into four, namely beginner, advanced, intermediate, main. To determine the class of each farmer group, it is necessary to select from the Department of Agriculture with assessment indicators, namely planning, organizing, implementing activities, evaluating and reporting as well as leadership. With so many indicators used, the Fuzzy Analytic Hierarchy Process method was chosen as the weighting and checking of weight consistency. The results of this study are a system that can measure the performance of farmer groups based on existing criteria and produce a final score which will be a benchmark in determining the class of the farmer group to be beginner, advanced, intermediate and primary. Out of a total of 131 farmer groups, 5.3% were in the beginner class, 26.7% in the advanced class, 66.4% in the middle class and 1.5% in the main class. This system also produces a fairly high accuracy value of 94.65%.

Research paper thumbnail of Comparison of ResNet101V2 and ResNet152V2 architectures in microscopy-based tuberculosis bacteria identification

AIP Conf. Proc. 3250, 2025

Tuberculosis (TB) is a preventable and treatable infectious disease, but it remains a serious pro... more Tuberculosis (TB) is a preventable and treatable infectious disease, but it remains a serious problem in countries at risk, such as those with poverty and limited access to healthcare services. Caused by the bacterium Mycobacterium tuberculosis, TB can be fatal without proper treatment. Accurate early identification is challenging, despite prevention efforts being made. The primary method for detecting TB is by identifying bacteria in sputum samples using a microscope, but there are weaknesses such as varying interpretations and inconsistent image quality. Convolutional Neural Networks (CNN) have shown potential in improving the accuracy of identifying TB bacteria in microscopic images. This study compares the performance of two CNN architectures, ResNet101V2 and ResNet152V2, in identifying TB bacteria in microscopic images. ResNet152V2 shows better results with an accuracy of 8 3.86%, precision of 100.00%, recall of 66.39%, and an F1-score of 80.00%. Despite requiring longer computational time, efficiency remains high, demonstrating strong potential for medical applications. Future research can explore variations in architecture and parameters for even more optimal results.

Research paper thumbnail of Conformity assessment of software as a service (SaaS) for developing supply chain management applications in small and medium enterprises (SMEs) using Google Apps

AIP Conf. Proc. 3250, 2025

Software as a Service (SaaS) represents a prominent cloud computing platform utilized to facilita... more Software as a Service (SaaS) represents a prominent cloud computing platform utilized to facilitate the efficient development of software applications via the internet. Given the constraints that Small and Medium Enterprises (SMEs) face with regard to limited infrastructure, operational expenditures, and IT resources, these enterprises can derive substantial benefits from the SaaS offerings furnished by Google Apps to enhance their operational efficiency. In this study, we employ the Agile Software Development Life Cycle (ASDLC) process stages to systematically construct Supply Chain Management applications tailored to SMEs, harnessing the diverse array of SaaS functionalities provided by Google Apps. To measure the quality of development process, we present the performance metrics through the examination of various Software Quality (SQA) and also form the quality of application outcome use Technology Acceptance Model (TAM) variables. This assessment takes into account the utilization of the manifold SaaS component features offered by Google Apps within the ASDLC framework where it has resulting in exceptionally high quality and satisfactory values, making it suitable for testing and implementation for SMEs.

Research paper thumbnail of Identification of tuberculosis with the fuzzy Sugeno method and diet recommendations using the Naive Bayes method

AIP Conf. Proc. 3250, 2025

The development of tuberculosis, according to the World Health Organization (WHO) in 2014, stated... more The development of tuberculosis, according to the World Health Organization (WHO) in 2014, stated that it was estimated to affect 9.6 million people, with 12% of them being HIV-positive. Tuberculosis is a directly communicable disease caused by the tuberculosis bacterium (Mycobacterium Tuberculosis). Tuberculosis bacteria can be transmitted through physical contact, the air, the sputum of patients, and so on. Currently, many people are unaware of the early symptoms and dangers of tuberculosis, so an expert system is needed to diagnose tuberculosis early and provide dietary recommendations that can help expedite patient treatment. The aim of this research is to find out early whether a patient suffers from tuberculosis using the Fuzzy Sugeno method and can recommend healthy food patterns for sufferers using the healthy Naive Bayes method which can help. In this disease diagnosis expert system, the Fuzzy Sugeno method is used to make decisions by answering questions related to symptoms. Additionally, to ensure that patients have good nutritional status, dietary recommendations are provided using the Naive Bayes method to tailor diets according to the nutritional needs of tuberculosis patients. There are 280 data on tuberculosis which is divided into pulmonary, Lymphadenitis and Enteritis tuberculosis. Based on the test results, the total number of correct data was 239 data and the number of incorrect data was 41 data, using the Fuzzy Sugeno method for disease diagnosis resulted in an accuracy rate of 85.35%. Meanwhile, the test results on food recommendations used the Naive Bayes method with the highest level of accuracy, namely 89.28% with split data testing cross validation at k=3.

Research paper thumbnail of PEMANFAATAN METODE SIMPLE ADDITIVE WEIGHTING DALAM  SISTEM PENDUKUNG KEPUTUSAN PROMOSI JABATAN PADA PT  DUNIA MAKMUR JAYA

SNTEM, Volume 1, November 2021. hal. 1187-1197, 2021

Manajemen SDM dari perusahaan sangat mempengaruhi banyak aspek penentu keberhasilan kerja perusa... more Manajemen SDM dari perusahaan sangat mempengaruhi banyak aspek penentu keberhasilan
kerja perusahaan. Salah satu proses yang sangat penting dalam Departemen Sumber Daya Manusia
(SDM) suatu perusahaan atau badan yang promosi Jabatan. Secara umum, promosi itu diberikan
pada bos rekomendasi atau unit kerja masing-masing berdasarkan pekerjaan lama, penilaian kinerja
dan penilaian perilaku karyawan dalam melaksanakan tugasnya. Untuk itu maka diperlukan penilaian
karyawan. Pengolahan data yang dapat membantu memfasilitasi pengawas dan departemen sumber
daya manusia untuk mengambil keputusan yang berkaitan dengan promosi jabataan. Saat ini
pengolahan data penilaian karyawan perusahaan masih dilakukan dengan komputerisasi excel,
sehingga semakin besar risiko kesalahan memasukkan mengingat jumlah karyawan sangat banyak
dan dan dibutuhkan waktu yang relatif lama. Hal ini juga masih sering membingungkan informasi
mengenai pergerakan pembentukan karyawan. Metode yang digunakan dalam menentukan promosi
Promosi ini Simple Additive Weight (SAW). Di mana metode ini adalah metode penghitungan
tertimbang atau metode yang menyediakan kriteria tertentu yang berbobot sehingga setiap nilai
jumlah dari bobot dari hasil yang diperoleh akan menjadi keputusan akhir. Dilihat dari aspek
manajerial penilaian dapat dikembangkan dengan kriteria lain sesuai dengan kebutuhan perusahaan.
Perhitungan menggunakan Simple Additive Weighting, dengan mengacu pada kriteria pekerjaan,
evaluasi kinerja, dan penilaian perilaku karyawan, kemudian memilih seorang karyawan yang akan
mendapatkan promosi.
Kata kunci : Promosi Jabatan, Simple Additive Weighting, SPK

Research paper thumbnail of EDUCATION GAME UNTUK SORTING TRASH 3D BERBASIS ANDROID

SNTEM, Volume 1, November 2021, hal. 1238-1243, 2021

Kebersihan merupakan hal yang sangat penting dalam kehidupan manusia, bahkan saat ini menjaga keb... more Kebersihan merupakan hal yang sangat penting dalam kehidupan manusia, bahkan saat ini menjaga kebersihan merupakan program pencegahan penularan Covid-19. Oleh karena itu, kebersihan merupakan tanggung jawab kita. Ajakan dan peringatan untuk menjaga kebersihan seringkali dijumpai baik di baleho, selebaran maupun di tempel di tempat-tempat umum lainnya, terutama ajakan setiap orang untuk membuang sampah pada tempatnya menurut jenis pewadahannya. Namun, berdasarkan survey dilapangan, masih kurangnya kesadaran orang-orang akan kebersihan lingkungan. Oleh karena itu perlunya edukasi akan pengenalan sampah sejak dini oleh anak-anak. Pada penelitian ini mengembangkan aplikasi pengenalan sampah dalam bentuk game berbasis 3D. Tujuan dari penelitian ini akan pengembangan aplikasi ini adalah agar anak-anak mendapat pengetahuan akan sorting trash berbasis android. Metode yang digunakan pada penelitian ini adalah Game Development Life Cycle (GDLC). Berdasarakan hasil ujicoba kepada anak-anak dengan 21 responden didapatkan respon termotivasi sebesar 86% akan kemampuan aplikasi ini dapat memberikan pengetahuan akan memilah sampah daur ulangnya dalam bentuk game.