Annisa Annisa Uswatun Khasanah, S.T., M.Sc (original) (raw)

Papers by Annisa Annisa Uswatun Khasanah, S.T., M.Sc

OPSI

The logistics industry is growing very rapidly. One of big industry in Indonesia is PT. Tiki Line... more The logistics industry is growing very rapidly. One of big industry in Indonesia is PT. Tiki Line Nugraha Ekakurir (JNE), which has been established for 29 years. This company has an extensive network in all cities in Indonesia, with service points of 1,500 locations. JNE has an application called my JNE on Google Play, which received more than 86,000 reviews and since December 2019 only got a rating of 2.4 stars out of a total rating of 5 stars. This study is obtained to analysis JNE user review data from Google Play. The reviews used in this study totaled 1,876 classified into positive and negative sentiment classes using the Naïve Bayes Classifier algorithm and word associations were also implemented. Classification with naïve bayes classifier with 90% training data and 10% test data had the best accuracy of 85.87%. Furthermore, for the text association, information is obtained that JNE users are talking about "send"

IOP Conference Series: Materials Science and Engineering, 2017

The number of mobile shop is growing very fast in various regions in Indonesia including in Yogya... more The number of mobile shop is growing very fast in various regions in Indonesia including in Yogyakarta due to the increasing demand of mobile phone. This fact leads high competition among the mobile phone shops. In these conditions the mobile phone shop should have a good promotion strategy in order to survive in competition, especially for a small mobile phone shop. To create attractive promotion strategy, the companies/ shops should know their customer segmentation and the buying pattern of their target market. These kind of analysis can be done using Data mining technique. This study aims to segment customer using Agglomerative Hierarchical Clustering and know customer buying pattern using Association Rule Mining. This result conducted in a mobile shop in Sleman Yogyakarta. The clustering result shows that the biggest customer segment of the shop was male university student who come on weekend and from association rule mining, it can be concluded that tempered glass and smart phone "x" as well as action camera and waterproof monopod and power bank have strong relationship. This results that used to create promotion strategies which are presented in the end of the study.

IOP Conference Series: Materials Science and Engineering

Marketing strategy is an important thing that must be developed by retail. A method that can be u... more Marketing strategy is an important thing that must be developed by retail. A method that can be used to develop a marketing strategy based on customer buying pattern is Association Rule (AR). AR is the process of finding association relationships between products that occur in one transaction. The application of AR to analyze custumer buying patterns is referred to as Market Basket Analysis (MBA). Rule obtained from ARMBA is sometimes not enough to provide an analysis when the variability of costumer buying pattern is high. Overall Variability of Association Rule (OCVR) is an indicator that focuses on analyzing market basket which assumes high variability in custumer behavior in buying products. This study used custumer transaction data of a retail in Yogyakarta. The data consisted of 57784 transactions in a month involving 41248 items. This study produced rules for each period (weeks), then the rules were used for further analysis using OCVR. 59 rules produced on the 1st period, 48...

2019 International Conference on Information and Communications Technology (ICOIACT), 2019

One business that has developed along with the increase in information and communication technolo... more One business that has developed along with the increase in information and communication technology is the transportation service business. The last decade has emerged as a technology-based transportation business innovation, for example Grab. It is important for a company or organization to find out about people’s responses to their services or products. Public opinion on the product is not small in number, even though it is undeniable that public opinion has an impact on the company’s image. Therefore, a technique for analyzing the opinion is needed so that the company can monitor and organize their services. Public opinion is classified into positive or negative sentiment classes using SVM and Maximum Entropy methods. The labeling results are then analyzed by text association to find the relationship of each information obtained. Classification with SVM method produces 89.01% accuracy. Whereas Maximum Entropy obtained higher accuracy that is 90.46%. Text associations obtained fro...

In the era of globalization, the internet has become a human need in doing various things. Many i... more In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample. The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indi...

A.A.N. Perwira Redi, Fiki Rohmatul Maula, Fairuz Kumari, Natasha Utami Syaveyenda, Nanda Ruswandi... more A.A.N. Perwira Redi, Fiki Rohmatul Maula, Fairuz Kumari, Natasha Utami Syaveyenda, Nanda Ruswandi, Annisa Uswatun Khasanah*, Adji Chandra Kurniawan Logistics Engineering, Technology of Industries, Pertamina University, Jl. Teuku Nyak Arief, Kec. Kby. Lama, Kota Jakarta Se latan, Daerah Khusus Ibukota Jakarta 12220, Indonesia Department of Industrial Engineering, Universitas Islam Indonesia, Jalan Kaliurang Km. 14,5, Yogyakarta, 55584, Indonesia

MATEC Web of Conferences, 2018

Catalogues are the media that companies use to promote their products or services. Since catalogu... more Catalogues are the media that companies use to promote their products or services. Since catalogue is one of marketing media, the first essential step before designing product catalogue is determining the market target. Besides, it is also important to put some information that appeal to the target market, such as discount or promos by analysing customer pattern preferences in using services or buying product. This study conduct two data mining technique. The first is clustering analysis to segment customer and the second one is association rule mining to discover an interesting pattern about the services that commonly used by the customer at the same service time. Thus, the results will be used as a recommendation to make an attractive marketing strategy to be put in the service catalogue promo for a laundry in Sleman Yogyakarta. The clustering result showed that the biggest customer segment is university student who come 3 until 5 times in a month on weekends, while the association rule result showed that clothes, shoes, and bed sheet have strong relationship. The catalogue design is presented in the end of the paper.

International Journal of Information and Education Technology, 2019

Predicting student's performance is one way that can be conducted by university to monitor their ... more Predicting student's performance is one way that can be conducted by university to monitor their student to prevent student failed. Student final GPA is one parameter that must be full fill by student to graduate from university and it can be used to measure student's performance. Educational Data Mining is popular techniques to predict student's performance. This study tried to implement two popular data mining clustering and classification analysis to predict student's performance. K-means algorithm is used since it is very popular and easy to be implemented clustering algorithm. Linear Regression and Support Vector Machine (SVM) then used to predict the final GPA since the attributes used in this study is numerical data. The clustered data and non-clustered data were evaluated in the classification analysis and the MSE was compared. The result showed that clustered data had smaller RMSE and Linear Regression was better than SVM.

IOP Conference Series: Materials Science and Engineering, Jun 1, 2017

Student's performance prediction is essential to be conducted for a university to prevent stu... more Student's performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student's performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student's attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

Journal of Physics: Conference Series

Marketing is one of the high cost activities in product sales. Therefore, effective marketing is ... more Marketing is one of the high cost activities in product sales. Therefore, effective marketing is a must in a company and it should be able to encourage customers to purchase more products. One of the efforts to determine effective marketing strategies is clustering the customers and formulating correct actions for every customer cluster. Today, most of companies have digital data including customer transaction data. Techniques to analyse digital data to discover knowledge behind the data is also developed from time to time. One of the techniques in digital data analysis that receives major attention from researchers is machine learning; a technique to enable computer to do learning in analysing the data. This study presents the process of customer clustering to determine effective marketing strategy using a machine learning technique. Customers would be analysed based on 3 parameters, which are last date of coming (recency/R), purchase frequency (frequency/F) and total money spent f...

Nowadays clustering is applied in many different scopes of study. There are many methods that hav... more Nowadays clustering is applied in many different scopes of study. There are many methods that have been proposed, but the most widely used is K-means algorithm. Neural network has been also usedin clustering case, and the most popular neural network method for clustering is Self-Organizing Map (SOM). Both methods recently become the most popular and powerful one. Many scholarstry to employ and compare the performance of both mehods. Many papers have been proposed to reveal which one is outperform the other. However, until now there is no exact solution. Different scholar gives different conclusion. In this study, SOM and K-means are compared using three popular data set. Percent misclassified and output visualization graphs (separately and simultaneously with PCA) are presented to verify the comparison result.