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All Journal JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma Journal of Information Technology and Computer Science (JOINTECS) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Indonesian Journal of Applied Informatics Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal ICT : Information Communication & Technology JSAI (Journal Scientific and Applied Informatics) INTI Nusa Mandiri Generation Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Reswara: Jurnal Pengabdian Kepada Masyarakat Yayasan Cita Cendikiawan Al Khwarizmi Jurnal J-PEMAS Edu Cendikia: Jurnal Ilmiah Kependidikan Jurnal Pendidikan Sains dan Komputer Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Puan Indonesia Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Journal of Information Systems and Technology Research Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Bulletin of Informatics and Data Science Bulletin of Information System Research The Indonesian Journal of Computer Science
Budy Satria
Universitas Andalas
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Digitalization of Rural Water Management: Android-Based Billing for Community Systems using the ADDIE model Nurfiah Nurfiah; Afdhal Dinilhak; Luthfil Khairi; Budy Satria; Anggi Hadi Wijaya; Ajeng Dwi Asti; Arifan Rahman
Journal of Information Systems and Technology Research Vol. 4 No. 2 (2025): May 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i02.1135

Abstract

The integration of information technology in everyday life has changed the way people work, learn, socialize, make transactions, and make decisions. The use of Android-based smartphones is a real example of the use of technology. Android, which is open-source, has encouraged the development of applications widely according to need. Access to water is a fundamental human right. PAMSIMAS is a flagship program of the regional and central governments that seeks to meet water needs through the provision of clean water services in line with the Sustainable Development Goals (SDGs). In Durian Seribu Village, PAMSIMAS is a service to meet the water needs of the community and become a solution for rural communities to get clean water at low cost, but its management is still manual, such as recording water usage and billing, so it is inefficient, time-consuming, and prone to errors. From these problems, this study proposes the development and implementation of an Android application designed to simplify the recording and billing process for the PAMSIMAS program in Durian Seribu Village. This application aims to simplify management, increase data transparency, and simplify reporting. The results of tests that have been carried out using the black box method show that this application can facilitate officers in recording and billing payments for PAMSIMAS water usage. Officers only need to enter the total water usage, and the application will automatically calculate and print a receipt as proof of payment. Officers also do not need to calculate manually when reporting the total payment to the administrator. For administrators, this application makes it easier to monitor and evaluate the performance of recording officers. After the application was used for recording and billing, PAMSIMAS's revenue increased by around 30% from the revenue before using the application.
Analisis Komparatif Model Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, dan K-Nearest Neighbor untuk Klasifikasi Penyakit Batu Empedu Menggunakan Machine Learning Ahda Rindang Al Amin; Budy Satria
Bulletin of Information System Research Vol 4 No 2 (2026): April 2026
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v4i2.281

Abstract

Gallstone disease is a common medical condition that often presents without symptoms until complications occur. Early prediction of this disease can improve patient outcomes through timely intervention. This study aims to compare the performance of five classification algorithms in predicting gallstone disease using clinical data: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). The dataset used was obtained from the UCI Machine Learning Repository and contains clinical data from 319 patients, comprising 38 numerical features. Outlier handling was conducted using Winsorized Transformation and RobustScaler. Each model was optimized using GridSearchCV and evaluated using accuracy, precision, recall, F1-score, and ROC AUC metrics. The results show that SVM and Logistic Regression achieved the best performance, each with 85.9% accuracy and an AUC above 0.90. Based on these findings, Logistic Regression and SVM are recommended as the most effective classification models for gallstone disease prediction using clinical data
DETEKSI DAN KLASIFIKASI PENYAKIT DAUN TANAMAN PORANG MENGGUNAKAN MODEL YOLOV8 Muhammad Al Hafiz; Budy Satria; Arifan Rahman
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8748

Abstract

Porang (Amorphophallus muelleri) is a high-value agricultural commodity whose productivity can be significantly reduced by leaf diseases. Conventional visual inspection is time-consuming and subjective, highlighting the need for an automated detection system that is both accurate and efficient. Previous studies on porang leaf diseases have primarily focused on image classification or object detection methods with limited capability to generalize under varying visual conditions. Therefore, this study aims to evaluate the performance of the YOLOv8n model integrated with data augmentation for detecting and classifying five conditions of porang leaves: early blight, late blight, konjac mosaic, insect attacks, and healthy leaves. The proposed approach combines YOLOv8n with on-the-fly data augmentation during training and Stratified 5-Fold Cross-Validation to improve the model's generalization capability. A total of 1,065 leaf images were used for model training and evaluation. Data augmentation techniques, including Mosaic, HSV color adjustment, and geometric transformations, were applied during training. Experimental results show that the proposed model achieved a mean Average Precision (mAP50) of 80.53%, a Precision of 83.11%, and a Recall of 70.66%, outperforming the baseline model without augmentation, which obtained an mAP50 of only 63.15%. Although the early blight and healthy classes achieved excellent detection performance (mAP50 > 90%), the relatively lower Recall was mainly caused by background bias in the insect class, resulting in several objects being missed during detection. Overall, the integration of YOLOv8n with data augmentation demonstrates its potential to improve the generalization capability of porang leaf disease detection systems under diverse visual conditions.
Co-Authors Abda Abda Abdullah Al Ramadhani Abdur Rohim Abdussalam Al Akbar Acihmah Sidauruk Adinda Raudhatul Jannah Afdhal Dinilhak Afifah Nur Latifah Afrianto, Nurdi Ahda Rindang Al Amin Ajeng Dwi Asti Al Hafiz, Muhammad Aldo Eko Syaputra Anggi Hadi Wijaya Arifan Rahman Arkan Ubaidillah Warman Ashari, Muhammad Al Azizah, Tiara Bister Purba Budi Permana Putra Candra Surya Chairil, Slamet Darmansah Darmansah Darmansah Darmansah Darmansah Darmansah Dedi Lesmana Devina Amanda Putri Dori Gusti Alex Candra Dwi Asti, Ajeng Efendi, Irman Efitra Efitra Efitra Fadilah, Sandi FAJAR, LM Firma Firma Fitra Yuda Gusla Nengsih, Yeyi Hafiz Mursalan Hendra, Yomei Karimah Irsyadiyah Khairi, Luthfil Khelvin Ovela Putra Leonard Tambunan Lidya Ningsih Liga Mayola LM Fajar Israwan, LM Fajar Luthfil Khairi Maulana, Fajar Meiditra, Irzon Muhammad Al Hafiz Muhammad Iqbal Mulia Sulistiyono Murni, Aprilia Mutamassikin Mutamassikin Nurcholis, Muhammad Tofa Nurfiah Prastya, Adi Qori Islami Aris Radillah, Teuku Raditya Wardhana Rahman, Arifan Rahman, Zumardi Rahmi Eka Putri Resad Setyadi SAKINAH, PUTRI Samosir, Khairunnisa Sandi Fadilah Sandi Fadilah Saputra, Alex Rizky Sarah Astiti S.Kom., M.MT Saskia Alifah Sepriano Sepriano Siti Sahara Lubis Sri Wulandari Supriatin Supriatin, Supriatin Syamsul Arifin Syarif Hidayatullah Tomy Nanda Putra Vita Amelia, Vita Wahyudi Willi Novrian Willi Novrian Yessi Ratna Sari Yessi Ratna Sari Yessi Ratna Sari Yeyi Gusla Nengsih Zamora, Sasya