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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Hasil Pengabdian Masyarakat (JURIBMAS)
M. Ikhsan Wibowo
Politeknik Negeri Bengkalis
Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences

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STACKING ENSEMBLE MACHINE LEARNING MODEL FOR EARLY DETECTION OF CHRONIC KIDNEY DISEASE IN INDONESIA Agusviyanda; Hamdani; M. Khairul Anam; Agustin; M. Ikhsan Wibowo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6501

Abstract

Chronic Kidney Disease (CKD) is one of the major health problems that continues to rise and requires accurate early detection to prevent progression to end-stage renal failure. This study proposes a hybrid machine learning approach to automatically detect CKD by combining data balancing techniques, ensemble learning, and cross-validation. The dataset used was obtained from the Kaggle platform, consisting of 1,089 patient records, and was balanced using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Three boosting algorithms—Adaboost, XGBoost, and LightGBM—were used as base models and combined through a stacking approach with Logistic Regression as the meta-classifier. Evaluation was conducted using a 5-fold cross-validation scheme with accuracy, precision, recall, and F1-score as performance metrics. The results show that the stacking model achieved an average accuracy of 99.40%, outperforming individual models (LightGBM: 98.87%; Adaboost: 98.76%; XGBoost: 98.61%) and exceeding the performance of several previous studies. These findings indicate that the stacking approach, when combined with SMOTE and cross-validation, significantly enhances classification performance for CKD detection.
Implementation of a Medicine Distribution System to Determine Health Assistance Priorities for Flood-Affected Communities in Aceh Tamiang M. Khairul Anam; Tjut Rizqi Maysyarah Hadi; Michel Kasaf; Agusviyanda; M. Ikhsan Wibowo
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.v5i1.979

Abstract

Flooding in Kasih Sayang Village, Manyak Payed District, Aceh Tamiang Regency created challenges in the distribution of health assistance, particularly in data collection, prioritizing recipients, and recording medicine distribution. This community service activity aimed to implement a medicine distribution system to support the prioritization of health assistance for flood-affected communities. The methods used included initial visits, observation, interviews, questionnaires, program implementation, and partner response evaluation. The program was carried out through the symbolic handover of medicines and medical equipment to the village midwife and village head, followed by a trial of the medicine distribution application with partners. The results showed that the application could be used properly by partners and helped make recipient data management more organized, faster, and systematic. The evaluation involving the village head, village officials, and village midwife showed an average score of 3.72, which was categorized as very high. The highest score was found in the need for a system to determine aid recipient priorities at 3.90, followed by the urgency of post-flood health assistance at 3.85. Overall, this program provided practical benefits in supporting a more orderly, transparent, and accountable management of health assistance at the village level.
Co-Authors Agustin Agusviyanda Anam, M Khairul Hamdani Michel Kasaf Tjut Rizqi Maysyarah Hadi