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All Journal JITEK (Jurnal Ilmiah Teknosains) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal SOLMA Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Tekno Kompak Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Nasional Ilmu Komputer Jurnal Pengembangan Sistem Informasi dan Informatika Journal of Soft Computing Exploration LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Abdi Masyarakat Indonesia Ngabdimas : Pengabdian Pada Masyarakat Society: Jurnal Pengabdian Masyarakat Informatics and Enginering Dedication Knowbase : International Journal of Knowledge in Database Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Ngabdimas Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Syahri, Riduan
Institut Teknologi Pagar Alam
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Transportation Veterinary Other

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Pelatihan Pengembangan Aplikasi Machine Learning Berbasis Streamlit untuk Mendukung Penyelesaian Tugas Akhir Mahasiswa Febriansyah; Dedi Setiadi; Riduan Syahri
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.1350

Abstract

Mahasiswa Teknik Informatika yang mengangkat topik machine learning dalam tugas akhir masih mengalami kesulitan dalam mengimplementasikan model yang telah dibangun ke dalam aplikasi yang dapat digunakan secara langsung oleh pengguna. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi mahasiswa dalam mengembangkan aplikasi machine learning berbasis Streamlit guna mendukung penyelesaian tugas akhir. Kegiatan diikuti oleh 35 mahasiswa tingkat akhir Program Studi Teknik Informatika Institut Teknologi Pagar Alam (ITPA) dan dilaksanakan melalui pelatihan, demonstrasi, praktik langsung, pendampingan, serta evaluasi menggunakan pre-test dan post-test. Hasil kegiatan menunjukkan peningkatan kompetensi peserta yang ditandai dengan kenaikan nilai rata-rata dari 54,3 pada pre-test menjadi 86,7 pada post-test. Selain itu, sebanyak 31 peserta (88,6%) berhasil membangun aplikasi berbasis Streamlit dan 27 peserta (77,1%) berhasil mengintegrasikan model machine learning ke dalam aplikasi yang dikembangkan. Tingkat kepuasan peserta mencapai 92,5%, menunjukkan respons yang sangat positif terhadap kegiatan. Hasil ini menunjukkan bahwa pelatihan berbasis Streamlit efektif dalam meningkatkan keterampilan implementasi machine learning serta mendukung penyelesaian tugas akhir yang lebih aplikatif.
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.
Integrasi Explainable AI (SHAP) Pada Model Machine Learning Untuk Analisis Faktor Penentu Kualitas Fasilitas Kesehatan Berbasis Web Riduan Syahri; Fitria Rahmadayanti; Alfis Arif
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/ndmny220

Abstract

The quality and equitable distribution of healthcare facilities are vital indicators of regional public service development. Although modern Machine Learning models such as Extreme Gradient Boosting (XGBoost) achieve exceptionally high predictive performance in classifying medical facility quality levels, their black-box nature often limits decision-making transparency for policymakers. This study aims to integrate an Explainable Artificial Intelligence (XAI) approach using SHapley Additive exPlanations (SHAP) into an XGBoost model developed in Google Colab and deployed as an interactive web dashboard via Streamlit. The dataset comprises aggregated BPJS Healthcare Facility data across Indonesian regencies/cities supplemented with operational indicators. The experimental results demonstrate superior modeling performance, achieving an accuracy of 95.15% and an F1-Score of 97.30%. Through SHAP Summary Plot analysis, Skor_Fasilitas_UGD and Total_Faskes were identified as the primary dominant factors driving high-quality facility classifications. The Streamlit web application successfully visualizes individual feature contributions (SHAP Waterfall Plot) in real-time, providing intuitive clinical and managerial transparency for public health planning
Co-Authors Abdi NAsrullah Alfis Arif Alfis Arif Alfis Arif Andayani, Mastra Arie Yandi Saputra Buhori Muslim Debi Guzmaliza Dedi Setiadi Dedi Setiadi Desi Puspita Desi Puspita Edowinsyah Efan, Efan Febriansyah . Ferry Putrawansyah Ferry Putrawansyah, Ferry Fitria Rahmadayanti Gusmaliza, Debi Hakim, Revaldo Xsanal Ichsan, Onne Akbar Nur Masdalipa, Risnaini Padya, Inka Rizki Puspita, Desi Rahmayati, Desy Sari, Pita Tri Cahaya, Della Tri Susanti Xsanal Hakim, Revaldo Yohanes Riski Ramadhan Zulkipli Zulkipli