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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Bumigora Information Technology (BITe) Journal of Electrical Engineering and Computer (JEECOM) Nusantara: Jurnal Pendidikan Indonesia juremi: jurnal riset ekonomi Reputasi: Jurnal Rekayasa Perangkat Lunak Journal Of Information System And Artificial Intelligence Jurnal Sistem Informasi dan Teknologi (SINTEK) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Sintek Kuwera Prosiding Seminar Nasional CORISINDO
Khasnur Hidjah
Universitas Bumigora, Mataram, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering

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Journal : Journal Of Information System And Artificial Intelligence

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Feature importance of using explanaible artificial intelligence (xai) and machine learning for diabetes disease classification Ahmad, Muhammad Maulana; Sulistianingsih, Neny; Hidjah, Khasnur
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.252

Abstract

Diabetes is one of the most significant global health problems in the modern era. This disease not only has a serious impact on the quality of life of sufferers, but also poses a great economic and social burden, both for individuals and the health service system as a whole. Therefore, early detection and effective treatment are very important in an effort to reduce the prevalence and negative impact of this disease. Therefore, the purpose of this study is to design a machine learning classification model that is able to identify feature importance with the help of the Explainable Artificial Intelligence (XAI) method in the case of diabetes. This model is expected to provide a clear interpretation of the most relevant features or symptoms, making it easier to detect whether a person has diabetes or not based on the symptoms that have been selected more optimally. The results of this study in the treatment or prediction of diabetes show that the results of the selection of LIME model features are higher than the accuracy of the SHAP model, where the highest is the LIME model which is processed using classification using the XGBoost algorithm with an accuracy of 98.47%, in addition to the LIME model using the Decisien Tree and Random Forest algorithms producing an accuracy of 91.97% and 91.49%, respectively. then the SHAP model using the XGBoost algorithm produced an accuracy of 0.9094%, the Decisien Tree algorithm produced an accuracy of 0.8059% and the Random Forest produced an accuracy of 88.46%, with the amount of data used as many as 70000 data, with 80% training data and 20% test data. The findings of this study are that the LIME feature selection combined with the XGBoost classification method has the best accuracy rate of 98.47% compared to the SHAP feature selection which is the same in combination with XGBoost with an accuracy of 90.94%. These findings also show that the selection of LIME features combined with the XGBoost algorithm is able to improve the interpretability of the model as well as maintain or even improve the accuracy of the predictions. This approach allows for the identification of the most relevant features more efficiently, thus supporting more informed decision-making in the data analysis process
Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN): Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN) Resmiranta, Dading Oktaviadi; Krismono, Bambang; Hidjah, Khasnur
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.271

Abstract

The face is an important object in the biometric identification system. However, low image quality due to uneven lighting, noise, and variations in facial expressions can interfere with the accuracy of the recognition system. The study investigated the use of Convolutional Artificial Neural Network (CNN) combined with hybrid screening techniques to improve image quality, thereby improving the accuracy of facial recognition systems. Filters used include weight mean filtering, median filtering, Contrast-Limited Adaptive Histogram Equalization and gaussian filtering, wavelet filtering. The pre-processed image was then trained using image denoising measurements of the Structural Similarity Index, Mean Squared Error, and Peak Signal to Noise Ratio. The main objective of this study is to evaluate the best filtration combination to produce high accuracy in face classification. The datasets used were 55 classes and 100 images per class. The inceptionV3 architecture model is used for classifications with a number of epochs of 10. Evaluation was carried out on a facial data set with an 80%:20% scheme. The results of the experiment showed that the hybrid method produced the best performance with 94.5% validation accuracy, 94.2% precision, and 94.6% recall, an increase of +1.4% compared to baseline. The (original) baseline itself recorded 93.1% validation accuracy, 92.8% precision, and 93.2% recall. In addition, the loss graph shows that the pre-process model has faster and more stable convergence than the non-pre-processing model. These results confirm that the application of preprocessing, especially the hybrid approach, is able to improve the accuracy and stability of the model in image classification tasks.
Co-Authors Abda Abda Adam, M. Awaludin Ahmad, Muhammad Maulana Anthony Anggrawan Ayu Dasriani, Ni Gusti Cahaya Ardi, L. Nanda Christine Eirene Dian Syafitri Dian Syafitri Chani Saputri Dyah Susilowati Fahry, Fahry Fitra Rizki Ramdhani Hairani Hairani Helna Wardhana Herawati, Baiq Candra Juvinal Ximenes Guterres Mayadi Mayadi Muhammad Innuddin Muhammad Ridho Akbar Muhammad Zulfikri Neny Sulistianingsih Putradi, Angga Reny Refitaningsih Peby Ria Resmiranta, Dading Oktaviadi RR. Ella Evrita Hestiandari Santoso, Heroe Satrani, Azral Setyawan, Ari Siti Soraya Tanwir, Tanwir Triwijoyo, Bambang Krismono