Implementasi Data Mining K-means Clustering Tunggakan Rekening Listrik Pascabayar (Studi Kasus : PT PLN Persero Ulp Tualang)

Maipasha, Balqis (2022) Implementasi Data Mining K-means Clustering Tunggakan Rekening Listrik Pascabayar (Studi Kasus : PT PLN Persero Ulp Tualang). Other thesis, Universitas Islam Riau.

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Abstract

The large number of people who still use postpaid electricity, especially in the Tualang sub-district, so that the amount of postpaid arrears is a problem, especially at PT PLN Persero Tualang. Many factors affect the amount of arrears, such as the lack of public awareness, the small income of the people during the pandemic, the amount of electricity consumption. One way to deal with these problems is to conduct an evaluation stage of the people who are in arrears. With the implementation of K-Means Clustering Data Mining, it is expected to be able to assist PLN officers in making the right decisions in providing actions and sanctions with the K-Means Clustering method. Based on the results of the analysis and design of the data, they are grouped based on tariffs, namely household tariffs, social tariffs and business tariffs. The system is able to classify data into 3 clusters, which are followed up, monitored and safe with accuracy, namely household rates of 83%, social rates of 88% and business tariffs of 95% so that it can be concluded that this system can be implemented.

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