Clustering pengelompokan data pelanggan PLN Pascabayar Helvetia berdasarkan kombinasi nilai daya dan kode pembaca meter menggunakan metode k-means

AlfanSori (Universitas Islam Negeri Sumatera Utara, Medan)
Langgi (Universitas Islam Negeri Sumatera Utara, Medan)

Scientica: Jurnal Ilmiah Sains dan Teknologi
Vol. 1 No. 3 (2023): Scientica: Jurnal Ilmiah Sains dan Teknologi

In the era where electricity has become a primary necessity, the clustering of PLN Pascabayar Helvetia's customer data using the k-means clustering method has been conducted. This research aims to divide customers into three groups based on the combination of power value and meter reading code. Customer complaint data was analyzed using the k-means clustering algorithm, resulting in three clusters with characteristics of high, medium, and low power. Data processing was carried out using the RapidMiner software. The findings of this study are expected to assist PLN in preparing strategies to enhance Pascabayar customer service. The research method involved direct interviews with a data source from PT PLN Persero ULP Helvetia. Additionally, clustering, data mining, and the k-means algorithm were utilized in this study. The research outcomes contribute to understanding customer behavior patterns and can serve as a basis for more effective management decision-making for companies.