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All Journal Bulletin of Informatics and Data Science Journal of Renewable Energy and Smart Device
Adhi Setiawan Samsul
Halu Oleo University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analysis of Electricity Consumption Patterns a Hybrid Algorithm K-Means Clustering and Support Vector Machine Fika Saputri; Asminar Asminar; Tambi Tambi; Mustarum Musaruddin; Muhammad Nadzirin Anshari Nur; Adhi Setiawan Samsul
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.155

Abstract

Fluctuations in electricity consumption make it difficult to manage and control energy use efficiently. Therefore, an analytical method capable of accurately identifying electricity consumption patterns is needed. Previous studies have generally applied clustering and classification separately and relied mainly on historical data, limiting their ability to capture dynamic electricity consumption patterns and classify new observations. This study aims to analyze electricity consumption patterns using a machine learning approach based on K-Means Clustering and Support Vector Machine (SVM). The data used consist of historical data and 24-hour real-time data obtained from PT PLN (Persero) UP3 Kendari. The research stages include data preprocessing, feature engineering, standardization using Z-scores, the clustering process using K-Means, and classification using SVM in a hybrid approach. The novelty of this study lies in using K-Means cluster labels as target classes for SVM while combining historical and real-time data. The results show the formation of four clusters: Cluster 0 representing stable moderate consumption, Cluster 1 representing high consumption, Cluster 2 representing low consumption, and Cluster 3 representing fluctuating consumption. Evaluation yielded a Silhouette Score of 0.5095 and a Davies-Bouldin Index of 0.6868, indicating fairly good cluster quality. The SVM model achieved an accuracy of 99.23% with high precision, recall, and F1-score values. These results demonstrate that the hybrid approach is effective in improving analysis performance and producing a reliable model for classifying new electricity consumption data
a Design of an IoT-Based Control and Monitoring System for a Biogas Power Generation Plant Using POME Waste from Palm Oil Mills: a Design of an IoT-Based Control and Monitoring System for a Biogas Power Generation Plant Using POME Waste from Palm Oil Mills Asminar Asminar; Rifki Rifki; Akhyar Muchtar; Abdul Djohar; Agustinus Lolok; Mustarum Musaruddin; Hasmina Tari Mokui; Adhi Setiawan Samsul
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1054

Abstract

Palm Oil Mill Effluent (POME) contains a high organic load that can be converted into biogas through anaerobic digestion and subsequently utilized for electricity generation. However, maintaining stable digester conditions requires an integrated monitoring and control system capable of observing critical operating parameters. This study presents a conceptual design and theoretical assessment of an Internet of Things (IoT)-based monitoring and control system for a POME-based biogas power plant. The proposed architecture integrates an anaerobic digester, gas holder, biogas generator, ESP32 microcontroller, temperature, pressure, methane concentration, and gas flow meter, cloud communication through MQTT/HTTP protocols, and web- and mobile-based dashboards. A threshold-based control strategy is proposed to regulate gas pressure and digester temperature. This study did not include physical system implementation or experimental field testing. Based on the adopted design assumptions, processing 300 metric tons of fresh fruit bunches generates approximately 240 m³ of POME and 6,000 m³ of biogas. Using a methane fraction of 60%, a methane calorific value of 35.7 MJ/m³, and a fixed generator efficiency of 35%, the theoretical electrical energy output is estimated at 12,495 kWh per production cycle. The proposed design provides an integrative framework for future prototype development and experimental validation of IoT-based monitoring and control in POME biogas power plants.
Co-Authors Abdul Djohar Agustinus Lolok Akhyar Muchtar Anshari Nur, Muh. Nadzirin Asminar Asminar Fika Saputri Hasmina Tari Mokui Mustarum Musaruddin Rifki Rifki Tambi Tambi