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All Journal JITEK (Jurnal Ilmiah Teknosains) Jusikom : Jurnal Sistem Komputer Musirawas Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Global Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Building of Informatics, Technology and Science Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Teknologi Informatika dan Komputer Jurnal Nasional Ilmu Komputer Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Abdimas Bina Bangsa Jurnal Media Informatika Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Ngabdimas : Pengabdian Pada Masyarakat Informatics and Enginering Dedication Digital Transformation Technology (Digitech) Knowbase : International Journal of Knowledge in Database Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Ngabdimas
Masdalipa, Risnaini
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Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Veterinary Other

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Integration of Machine Learning and Web-Based Expert Systems for Diabetes Risk Analysis in Pagar Alam Riduan Syahri; Desi Puspita; Risnaini Masdalipa
Knowbase : International Journal of Knowledge in Database Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.10268

Abstract

This study aims to develop an integrated system combining Machine Learning (ML) and a Web-Based Expert System for genomic and clinical data analysis to mitigate the rising diabetes cases in Pagar Alam City. The research adopts the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, encompassing business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases. Unlike previous studies relying on standard public datasets, this research integrates genomic profiles (TCF7L2 and KCNQ1 SNPs) alongside local clinical parameters from five sub-districts in Pagar Alam. Quantitative data from 640 samples were analyzed using the Support Vector Machine (SVM) algorithm. Evaluation results during the modeling phase show that the SVM model achieved a superior accuracy of 99.07%, demonstrating that integrating genomic data significantly enhances predictive precision. The web-based expert system implemented in the deployment phase provides personalized prevention recommendations based on individual risk profiles. This application is expected to serve as a strategic tool for the Pagar Alam government to enhance the effectiveness of prevention programs through localized and genetic-based interventions.
Co-Authors A. Haidar Mirza Abdi NAsrullah Alfis Arif Asminah Asminah Asminah, Asminah Barrorotul Azizah Bhebeind Aryanto Phubenast Debi Guzmaliza debigusmaliza debi Dedi Setiadi Desi Puspita destaria erika Dhiniati, Fameira Edowinsyah Edowinsyah, Edowinsyah Endang Etriyanti Febriansyah . Ferry Putrawansyah Fitria Rahmadayanti Gusmaliza, Debi Helen Moneka Inda Anggraini Muhammad Izman Herdiansyah Muntari, Siti Nurmaleni, Nurmaleni Panyahuti Matondang Puspita, Desi Repaldo Resty Seftiany Sasmita Sasmita Syahri, Riduan TRI SUSANTI Yadi, Yadi Yogi Isro Mukti