Analisis Pola Konsumsi Energi Listrik Pelanggan Rumah Tangga Menggunakan Alogaritma K-Means Clustering

Hilmi Mubarok; Bambang Irawan

doi:10.31850/jsilog.v6i1.4296

Hilmi Mubarok Teknik Informatika, Universitas Muhadi Setiabudi
Bambang Irawan Teknik Informatika, Universitas Muhadi Setiabudi

DOI: https://doi.org/10.31850/jsilog.v6i1.4296

Keywords: Electricity Consumption, Data Mining, K-Means Clustering, Davies-Bouldin Index, Elbow Method

Abstract

The increase in household electricity consumption is one of the main challenges in national energy management. Diverse electricity usage patterns are influenced by social, economic, and behavioral characteristics of consumers. This study aims to analyze and cluster household electricity consumption patterns using the K-Means Clustering algorithm. The dataset consists of secondary data from 1,200 household customers with attributes including installed power capacity, monthly electricity consumption (kWh), peak usage time, and average daily load. The research stages include data cleaning, normalization using StandardScaler, determination of the optimal number of clusters using the Elbow Method, clustering with K-Means, and evaluation using the Davies-Bouldin Index (DBI). The results indicate that the optimal number of clusters is three, representing low, medium, and high electricity consumption groups. A DBI value of 0.71 indicates good clustering quality. These findings can support electricity providers in designing energy efficiency policies and household load management strategies.

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