Klasifikasi Penggunaan Daya Listrik Rumah Tangga dengan Menggunakan Metode Naive Bayes - An Nisa Ziah Putri; Dodo Zaenal Abidin; Errissya Rasywir; Athallah, Ibni Faiq Athallah (Asosiasi Riset Ilmu Teknik Indonesia, 2025)
Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.