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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Martabe : Jurnal Pengabdian Kepada Masyarakat bit-Tech
Gusrino Yanto
Universitas Metamedia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Education Engineering Social Sciences

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Development of Digital Information Systems for Operational Efficiency of Savings and Loan Cooperatives Heru Saputra; Ilfa Stephane; Nency Extise Putri; Elisa Daniati Edison; Gusrino Yanto
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2931

Abstract

Savings and loan cooperatives are essential for advancing financial inclusion, especially in rural communities. However, many cooperatives still rely on manual or semi digital systems such as spreadsheets to manage member data and financial transactions. This condition results in low efficiency, delayed reporting, limited transparency, and frequent recording errors. This study aims to design and implement a web based cooperative information system to address operational inefficiencies at Korong Gadang Cooperative in West Sumatra, Indonesia, which serves over 120 active members. The system was developed using the Rapid Application Development (RAD) method, which emphasizes iterative prototyping and user involvement to accelerate development. The process consisted of three stages: requirements planning, system design and construction, and implementation. Modules include member management, savings and loan transactions, financial reporting, and an interactive dashboard. Black Box Testing was used to validate functionality, and user feedback was collected from 12 cooperative staff through questionnaires. The results show significant improvements in performance. Report preparation time decreased from 3–5 days to just 1 day. The system also enhanced data accuracy and transparency, enabling members and staff to access transaction information in real time. In conclusion, the web based cooperative information system developed with the RAD method has proven effective in improving efficiency and accountability. The system can be adopted by similar small-scale cooperatives with basic digital infrastructure. Future development may include mobile access, integration with payment systems, and analytical features to support data driven decisions.
PENGUATAN KAPASITAS KOPERASI MELALUI e-KOPERASI DIKOMETA PADA KSPPS KORONG GADANG UNTUK TRANSPARASI, AKUNTABILITAS DAN AKSELERASI SDGs Elisa Daniati Edison; Nency Extise Putri; Heru Saputra; Ilfa Stephane; Gusrino Yanto
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 8 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i8.3507-3516

Abstract

Pemberdayaan yang dilakukan pada  KPPS Korong Gadang Kecamatan Kuranji untuk memperkuat kapasitas koperasi melalui penerapan platform digital e-koperasi DIKOMETA. Penerapan teknologi tepat guna dengan pengembangan sistem informasi berbasis web dengan metode Rapid Application Development (RAD). Implementasi platform ini berfokus pada peningkatan transparansi dan akuntabilitas dalam pengelolaan koperasi, serta sebagai upaya mendukung akselerasi pencapaian Sustainable Development Goals (SDGs). Metode pelaksanaan meliputi pelatihan penggunaan aplikasi digital, pendampingan teknis, dan evaluasi operasional koperasi secara berkelanjutan. Capaian dari kegiatan menunjukkan adanya peningkatan signifikan dalam keterbukaan data keuangan, kemudahan akses informasi bagi anggota, serta penguatan tata kelola koperasi secara umum. Selain itu, pengabdian ini berkontribusi pada percepatan SDG khususnya di bidang pemberdayaan ekonomi dan kesenjangan sosial. Hasil pemberdayan ini menegaskan pentingnya digitalisasi sebagai instrumen strategis dalam pemberdayaan koperasi dan masyarakat.
Predicting High-Risk Pregnancies Using Machine Learning Algorithms and Explainable AI Sari Puspita; Gusrino Yanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7071

Abstract

Pregnancies classified as high-risk play a significant role in increasing health complications and deaths among mothers and newborns, making early risk identification essential for timely clinical intervention. Although machine learning has shown promising performance in predicting pregnancy risk, most existing studies rely on binary classification and provide limited model interpretability. This study proposes an interpretable multiclass machine learning framework that predicts pregnancy risk using maternal health records from primary healthcare facilities. A total of 2,553 maternal medical records collected from five community health centers in Koto Tangah District, Padang City, Indonesia, were analyzed. The proposed framework integrates data preprocessing, StandardScaler, Synthetic Minority Over-sampling Technique (SMOTE), GridSearchCV-based hyperparameter optimization, and Shapley Additive exPlanations (SHAP). Four machine learning algorithms, which are Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest, were systematically assessed with the application of Accuracy, Precision, Recall, F1-score, and ROC-AUC. Of the machine learning models considered, Random Forest performed best, achieving 97.26% accuracy, 90.44% precision, 98.06% recall, 93.65% F1-score, and 99.79% ROC-AUC. SHAP analysis identified Heart Rate, Blood Glucose, Diastolic Blood Pressure, and Systolic Blood Pressure as the most influential predictors, while also improving model transparency through feature contribution and interaction analysis. The results indicate that the suggested framework delivers precise and interpretable multiclass pregnancy risk prediction, demonstrating how it can assist with the early detection of pregnancy risks within primary care environments.
Co-Authors Elisa Daniati Edison Heru Saputra Ilfa Stephane Nency Extise Putri Sari Puspita