APPLICATION OF THE DICISION TREE ALGORITHM FOR CLASSIFICATION HOUSEHOLD ELECTRIC ENERGY CONSUMPTION USES RAPID MINER

Abstract

The application of the Decision Tree algorithm for classification of household electrical energy consumption using RapidMiner is an innovative approach in energy data analysis. Decision Tree is an effective machine learning technique for modeling the relationship between features and labels in a dataset. By utilizing electricity consumption data that includes variables such as equipment type, time of use, and household characteristics, this algorithm can identify significant patterns in energy consumption. This research aims to implement Decision Tree in RapidMiner to classify energy consumption levels (low, medium, high) in households. The research process includes data collection, preprocessing, data separation into training and test sets, and training models. The performance evaluation model is carried out to measure accuracy and effectiveness classification. The results show that the Decision Tree is able to provide accurate predictions and is useful in understanding the factors that influence energy consumption. It is hoped that this research can provide guidance for the public and policy makers in managing energy consumption more efficiently and sustainably.

Kata kunci: Implementation, Decision Tree Algorithm, Classification, Energy Consumption, Household, RapidMiner, Machine Learning, Preprocessing, Training Set, Test Set, Performance Evaluation, Energy Efficiency, Sustainable.