Chilyatun Nisa' - Academia.edu (original) (raw)
Papers by Chilyatun Nisa'
THE PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON MARITIME EDUCATION AND TRAINING (The 5th ICMET) 2021
Lung cancer is deadly cancer same as colon cancer, both of them can grow simultaneously. Most res... more Lung cancer is deadly cancer same as colon cancer, both of them can grow simultaneously. Most researchers conduct research to detect one disease on one single body organ. So, in this study, we proposed a computer-aided diagnosing system using the Convolutional Neural Network (CNN) to detect lung and colon cancer tissues on the LC25000 dataset. The LC25000 dataset contains 25000 histopathological color image samples of colon and lung tissues which indicated cancer (adenocarcinoma) or not. In this study, we used three pre-trained CNN models, which are ShuffleNet V2, GoogLeNet, and ResNet18 also one simple customized CNN model. From the evaluation metrics tables given in this study, the highest accuracy to classify lung cancer was gained by ResNet18 with 98.82% accuracy but the shortest training time gained by ShuffleNet V2 with 1749.5 seconds. ShuffleNet V2 was the best model used to classify colon data, it gives 99.87% accuracy with the fastest training times 1202.3 seconds. The customized CNN model proposed by us get 93.02% accuracy to classify lung cancer and 88.26% accuracy for colon cancer. The proposed CNN model also gained the shortest training time which was better than GoogLeNet and ResNet18.
2020 6th Information Technology International Seminar (ITIS), 2020
One problem that is often faced in health data sets is a high level of imbalance. Imbalanced data... more One problem that is often faced in health data sets is a high level of imbalance. Imbalanced data can mean that the data used in machine learning has an unbalanced data distribution between different classes. One of the data is Alzheimer’s disease data. The data used are Dataset containing 6400 images of brain magnetic resonance imaging that is indicated as having Alzheimer’s disease or not. This study used Deep Learning Architecture to classify brains affected by Alzheimer’s disease and healthy brains. We produced a trained and predictive model using Deep Convolutional Neural Networks (CNN), where the data had previously gone through the oversampling stage due to an imbalance in the data distribution. From the evaluation metric table provided in this study, the highest accuracy for detecting Alzheimer’s disease was obtained by ResNetl8 with an accuracy of 60.67%. Still, the shortest training time was obtained by ShuffleNet V2 with 624.4 seconds using non-oversampled. While the results obtained by using oversampled, ShuffleNet V2 provide 60.75% accuracy with the fastest training time of 1478.6 seconds. The special CNN model that the author proposes gets an accuracy of 50% using non-oversampled and 55.27% accuracy using oversampled.
Prosiding Seminar Nasional Informatika Bela Negara, 2020
Menurut data produksi buah-buahan di Indonesia yang dipublikasikan oleh BPS, produksi apel pada t... more Menurut data produksi buah-buahan di Indonesia yang dipublikasikan oleh BPS, produksi apel pada tahun 2017 mengalami penurunan sebesar 3.3% atau sejumlah 10.780 ton dari tahun 2016 yang menghasilkan sebanyak 329.780 ton. Hal itu disebabkan oleh berbagai penyakit yang sering terjadi pada produksi apel, oleh karena itu pendeteksian penyakit daun apel yang tepat waktu menjadi sangat penting untuk industri apel yang berkembang dengan sehat. Sehingga dibutuhkan sistem yang efektif seperti klasifikasi citra digital pada tanaman. Metode yang digunakan pada penelitian ini merupakan adalah Convolutional Neural Network (CNN) dengan arsitektur InceptionV3. Penelitian ini menggunakan dataset Plant Pathology 2020 - FGV C7 sebanyak 1821 data citra dengan 4 kelas. Data dibagi menjadi 3 set data (latih, validasi, dan uji) dengan rasio 70:10:20. Hasil pengujian dievaluasi dengan menggunakan data uji, untuk proses evaluasi menggunakan confusion matrix. Berdasarkan hasil pelatihan mencapai akurasi 96....
Proceedings of the International Joint Conference on Science and Engineering (IJCSE 2020)
THE PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON MARITIME EDUCATION AND TRAINING (The 5th ICMET) 2021
Lung cancer is deadly cancer same as colon cancer, both of them can grow simultaneously. Most res... more Lung cancer is deadly cancer same as colon cancer, both of them can grow simultaneously. Most researchers conduct research to detect one disease on one single body organ. So, in this study, we proposed a computer-aided diagnosing system using the Convolutional Neural Network (CNN) to detect lung and colon cancer tissues on the LC25000 dataset. The LC25000 dataset contains 25000 histopathological color image samples of colon and lung tissues which indicated cancer (adenocarcinoma) or not. In this study, we used three pre-trained CNN models, which are ShuffleNet V2, GoogLeNet, and ResNet18 also one simple customized CNN model. From the evaluation metrics tables given in this study, the highest accuracy to classify lung cancer was gained by ResNet18 with 98.82% accuracy but the shortest training time gained by ShuffleNet V2 with 1749.5 seconds. ShuffleNet V2 was the best model used to classify colon data, it gives 99.87% accuracy with the fastest training times 1202.3 seconds. The customized CNN model proposed by us get 93.02% accuracy to classify lung cancer and 88.26% accuracy for colon cancer. The proposed CNN model also gained the shortest training time which was better than GoogLeNet and ResNet18.
2020 6th Information Technology International Seminar (ITIS), 2020
One problem that is often faced in health data sets is a high level of imbalance. Imbalanced data... more One problem that is often faced in health data sets is a high level of imbalance. Imbalanced data can mean that the data used in machine learning has an unbalanced data distribution between different classes. One of the data is Alzheimer’s disease data. The data used are Dataset containing 6400 images of brain magnetic resonance imaging that is indicated as having Alzheimer’s disease or not. This study used Deep Learning Architecture to classify brains affected by Alzheimer’s disease and healthy brains. We produced a trained and predictive model using Deep Convolutional Neural Networks (CNN), where the data had previously gone through the oversampling stage due to an imbalance in the data distribution. From the evaluation metric table provided in this study, the highest accuracy for detecting Alzheimer’s disease was obtained by ResNetl8 with an accuracy of 60.67%. Still, the shortest training time was obtained by ShuffleNet V2 with 624.4 seconds using non-oversampled. While the results obtained by using oversampled, ShuffleNet V2 provide 60.75% accuracy with the fastest training time of 1478.6 seconds. The special CNN model that the author proposes gets an accuracy of 50% using non-oversampled and 55.27% accuracy using oversampled.
Prosiding Seminar Nasional Informatika Bela Negara, 2020
Menurut data produksi buah-buahan di Indonesia yang dipublikasikan oleh BPS, produksi apel pada t... more Menurut data produksi buah-buahan di Indonesia yang dipublikasikan oleh BPS, produksi apel pada tahun 2017 mengalami penurunan sebesar 3.3% atau sejumlah 10.780 ton dari tahun 2016 yang menghasilkan sebanyak 329.780 ton. Hal itu disebabkan oleh berbagai penyakit yang sering terjadi pada produksi apel, oleh karena itu pendeteksian penyakit daun apel yang tepat waktu menjadi sangat penting untuk industri apel yang berkembang dengan sehat. Sehingga dibutuhkan sistem yang efektif seperti klasifikasi citra digital pada tanaman. Metode yang digunakan pada penelitian ini merupakan adalah Convolutional Neural Network (CNN) dengan arsitektur InceptionV3. Penelitian ini menggunakan dataset Plant Pathology 2020 - FGV C7 sebanyak 1821 data citra dengan 4 kelas. Data dibagi menjadi 3 set data (latih, validasi, dan uji) dengan rasio 70:10:20. Hasil pengujian dievaluasi dengan menggunakan data uji, untuk proses evaluasi menggunakan confusion matrix. Berdasarkan hasil pelatihan mencapai akurasi 96....
Proceedings of the International Joint Conference on Science and Engineering (IJCSE 2020)