Application of K-Means Algorithm for Performance Segmentation of P2TL Officers as Decision Support at PT PLN (Persero) UP3 Kebon Jeruk

Widi Handoko; Arief Wibowo

doi:10.63922/ijebir.v5i04.4207

Widi Handoko Master of Management Study Program, Budi Luhur University
Arief Wibowo Lecturer, Master of Management Study Program, Budi Luhur University

DOI: https://doi.org/10.63922/ijebir.v5i04.4207

Keywords: Keywords: K-Means, Clustering, Data Mining, Performance Segmentation, P2TL.

Abstract

PT PLN (Persero) as the national electricity provider has a Electricity Usage Control (P2TL) activity that plays an important role in controlling violations of electricity use and minimizing potential company losses. In its implementation, the performance of P2TL officers shows different levels of productivity based on the number of inspections, P2TL findings, and performance assessments. However, the conventional performance evaluation process has prevented officer performance segmentation from being carried out optimally as a basis for managerial decision-making.This study aims to apply the K-Means algorithm to segment the performance of P2TL officers at PT PLN (Persero) UP3 Kebon Jeruk. The study uses a quantitative approach with the data mining method. Variables used include the number of inspections, P2TL findings, and officer performance assessments. Data processing was carried out using RapidMiner Studio through preprocessing, normalization, clustering, and cluster quality evaluation using the Davies Bouldin Index (DBI). The results show that the K-Means algorithm successfully grouped officers into three main clusters: high-performance, medium-performance, and low-performance clusters. Evaluation using the Davies Bouldin Index (DBI) obtained the best value of -0.609 at K=3, making three clusters the most optimal number for this study. The resulting segmentation can provide objective grouping of officers based on their performance characteristics. These findings can be used as a basis for developing coaching strategies, competency development, supervision, and more effective, measurable, and data-driven improvement of P2TL officer performance.

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