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All Journal Indonesian Journal of Obstetrics and Gynecology (Majalah Obstetri dan Ginekologi Indonesia) Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pendidikan dan Teknologi Indonesia Journal Software, Hardware and Information Technology Jurnal Teknoif Teknik Informatika Institut Teknologi Padang TECHNOLOGIC
Muhammad Ridha
Politeknik Astra
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Neuroscience Public Health Transportation

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Journal : Jurnal Teknoif Teknik Informatika Institut Teknologi Padang

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COMPARISON OF MACHINE LEARNING CLUSTERING ALGORITHMS FOR ANALYSING ELECTRICITY USAGE PATTERNS IN CAMPUS AREAS Purba, Diya Namira; Muhammad Ridha; Rida Indah Fariani; Harkiapri Yanto
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.87-96

Abstract

Electricity consumption in campus environments varies based on building functions, occupancy patterns, and time-of-day usage. Understanding these variations is essential for efficient energy management. Uncontrolled electricity use often results in high operational costs, highlighting the need for accurate methods to uncover consumption patterns. This study analyzes electricity consumption data from multiple campus buildings at a polytechnic in Jakarta during 2023 and 2024. Each dataset consists of six columns and 365 rows in a year. Since the data is unlabeled, three clustering algorithms: K-Means, Hierarchical Clustering, and DBSCAN are applied to identify usage patterns across campus areas. Pre-processing included imputation and normalization, followed by clustering. Cluster quality was evaluated using the Silhouette Score. A key novelty of this study is the year-to-year comparative analysis, showing that clustering performance can vary significantly depending on data structure and noise. The 2023 dataset (dataset 1) achieved the highest Silhouette Score of 0.48 using DBSCAN, while the 2024 dataset (dataset 2) produced the best result with Hierarchical Clustering at 0.53. These results emphasize the importance of selecting clustering methods based on data characteristics and temporal context. The findings contribute to developing adaptive, data-driven strategies for managing energy use in non-residential settings, particularly in educational institutions like campuses.
Co-Authors Abdur Rohman, Muhammad Kholil Berliana Irianti, Berliana Dewi Nasien Dian Agustin Eka Pandu Cynthia Harkiapri Yanto Harnita Sari, Ria Israyati, Nur Liva Maita, Liva Mahmud Dwi Sulistiyo Malikah, Amiroh Hanan Manayla, Yasmine Muhammad Affandes Muhammad Rakha Muhammad Ridha Pizaini Pizaini Purba, Diya Namira Rida Indah Fariani Sari, Ria Harnita Setiawati, Sandy Shaputra, Deni Handika