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All Journal Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Pengabdian Masyarakat (Jubdimas)
Erianto Ongko
Institut Modern Arsitektur dan Teknologi
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PELATIHAN PENERAPAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN JUMLAH PEMBERIAN PAKAN IKAN DI DESA MARIENDAL II Muhammad Khahfi Zuhanda; Hartono Hartono; Sayuti Rahman; Arnes Sembiring; Rahmad Syah; Dadan Ramdan; Mendarissan Aritonang; Citra Rahmadhani; Suswati Suswati; Habib Satria; Erianto Ongko
JUBDIMAS ( Jurnal Pengabdian Masyarakat) Vol 5 No 1 (2026): Artikel Pengabdian Maret 2026
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jubdimas.v5i1.403

Abstract

This community service activity aims to improve the efficiency of fish feeding management through the implementation of a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method in Mariendal II Village. The main problem faced by fish farmers is the manual feeding process based on estimation, leading to inefficiency and suboptimal fish growth. The method used in this activity is a participatory approach consisting of socialization, training, technology implementation, and evaluation. The developed system considers several criteria, including fish biomass, age, population, water quality, feeding time, and feed type. The results show that participants experienced significant improvements in knowledge and skills in using the DSS. The system successfully provided optimal feeding recommendations and was integrated with an automatic feeder, resulting in more consistent and efficient feeding practices. This activity also increased farmers’ awareness of technology adoption in aquaculture. Overall, the implementation of DSS contributes to reducing feed waste, improving productivity, and supporting sustainable fish farming practices.
Metaheuristic nurse scheduling with hospital clustering using flower pollination algorithm Muhammad Khahfi Zuhanda; Hartono Hartono; Sayuti Rahman; Prana Ugiana Gio; Erianto Ongko
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11243

Abstract

Effective nurse scheduling is essential to ensure balanced workloads, reduce fatigue, and maintain healthcare service quality. However, the nurse scheduling problem (NSP) is complex due to constraints related to nurse skills, task requirements, and legal working-hour limits. This study proposes an integrated framework combining a mathematical optimization model with metaheuristic algorithms to generate optimal daily nurse activity schedules. Genetic algorithm (GA) and simulated annealing (SA) are employed to produce near-optimal solutions for nurse populations ranging from 3 to 50 individuals, considering skill-level compatibility, workload balance, and maximum working hours. Experimental results using real scheduling data from 30 nurses across three skill levels demonstrate that all generated schedules satisfy the imposed constraints, with no nurse exceeding the 12hour daily working limit. Comparative analysis shows that GA achieves lower scheduling costs for larger nurse populations, while SA consistently requires significantly shorter computation times, making it suitable for time-sensitive applications. In addition, the flower pollination algorithm (FPA) is used to cluster 3,155 hospitals based on bed capacity, service variety, and workforce size, supporting data-driven workforce distribution analysis. The proposed framework integrates operational scheduling optimization with hospital-level clustering, providing practical decision support for healthcare workforce planning.
A Hybrid Framework Combining U-Net, Ant Colony Optimization, and CNN for Rice Leaf Disease Classification under Class Imbalance Erianto Ongko; Asmah Indrawati; Sukiman Sukiman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6910

Abstract

Accurate classification of rice plant diseases is essential for early intervention and precision agriculture. However, real-world datasets often suffer from complex backgrounds, high-dimensional features, and severe class imbalances, which compromise classification performance. This study proposes an integrated framework combining image segmentation using U-Net, feature selection via Ant Colony Optimization (ACO), hybrid sampling to handle class imbalance, and final classification using a Convolutional Neural Network (CNN). Segmentation isolates disease-affected areas, ACO optimizes feature subsets, and hybrid sampling balances class distribution using undersampling and SMOTE. The proposed method was tested on four rice leaf disease datasets—Brown Spot, Leaf Blast, Leaf Blight, and Leaf Scald—exhibiting significant class imbalance. Experimental results show that the proposed approach outperforms baseline models (SegNet, PspNet, and E-Net) across multiple metrics: Accuracy, IoU, Precision, and Recall. This indicates the framework’s robustness and potential for real-world deployment in precision agriculture. Future work will focus on model compression and real-time implementation in IoT systems.
Sosialisasi Pemanfaatan IoT Berbasis Machine Learning pada Deteksi Penyakit Tanaman Sawit untuk Pertanian Berkelanjutan di Dusun I Bukit Gantung Desa Sumber Mulyo Hartono; M. Khahfi Zuhanda; Sayuti Rahman; Retna Astuti Kuswardani; Suswati; Muhammad Zen; Erianto Ongko; Lili Suryati
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7148

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan untuk meningkatkan literasi digital petani sawit di Dusun I Bukit Gantung, Desa Sumber Mulyo, melalui sosialisasi pemanfaatan Internet of Things (IoT) berbasis machine learning untuk smart agriculture dalam deteksi dini penyakit tanaman sawit. Pelatihan dirancang dalam empat tahapan, yaitu persiapan perangkat, penyampaian materi konseptual, praktik instalasi IoT, penerapan machine learning, serta pendampingan lapangan. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur perubahan kompetensi peserta terhadap konsep IoT, machine learning, instalasi sensor, dan interpretasi hasil deteksi. Berdasarkan analisis, evaluasi hasil pelatihan menunjukkan peningkatan sebesar 47%, dengan kenaikan tertinggi pada keterampilan instalasi sensor dan membaca hasil aplikasi (+54%). Penerapan teknologi ini membantu petani melakukan deteksi penyakit lebih cepat dan akurat sehingga penggunaan pestisida dapat ditekan melalui penyemprotan selektif. Selain menghasilkan peningkatan kompetensi teknis, kegiatan ini meningkatkan keberterimaan teknologi di kalangan petani, terbukti dari 14 dari 15 kelompok yang secara konsisten menggunakan perangkat IoT pascapelatihan. Secara sosial, program ini mendorong perubahan perilaku kolektif menuju praktik budidaya yang lebih aman, efisien, dan berbasis data. Implementasi ini menunjukkan bahwa integrasi IoT–machine learning mampu memperkuat keberlanjutan pertanian sawit sekaligus meningkatkan kualitas pengelolaan kebun masyarakat, serta memberikan dasar penting bagi pengembangan sistem monitoring kesehatan tanaman yang lebih komprehensif dan adaptif pada skala komunitas.
Co-Authors Arnes Sembiring Asmah Indrawati Citra Rahmadhani Dadan Ramdan Habib Satria Hartono Hartono Hartono Lili Suryati M. Khahfi Zuhanda Mendarissan Aritonang Muhammad Khahfi Zuhanda Muhammad Zen Prana Ugi Rahmad B.Y Syah Rahman, Sayuti Retna Astuti Kuswardani Sukiman Sukiman Suswati Suswati Suswati