Perbandingan Performa Algoritma Klasifikasi pada Data Intensitas Penggunaan Listrik Rumah Tangga

Abstract

The pattern of electricity consumption is one thing that is important to be known by a household, so it is essential to identify the type of intensity of electricity usage from a household daily life. It can help determine how much electricity consumption of equipment so that efforts can be made to optimize electricity consumption further while saving costs. To achieve this, the classification method that is included in the category of supervised learning is used. In this study, we compared several types of classification methods to determine the kind of electricity usage patterns in a household's daily life on Household Electric Power Consumption data obtained from Kaggle. The classification methods being compared are kNN, SVM, Decision Tree, Logistic Regression, and K-Means. The accuracy of all methods is analyzed to find which method is best in identifying the intensity of electricity usage. From the results of this study, it was found that the Logistic Regression method is the most accurate in classifying the type of intensity of electricity consumption with an average accuracy value of 99.8%.