Prediction of Drinking Water Facility Conditions Using the Naive Bayes Algorithm - Nur Yulias; Septian Rheno Widianto (Institute of Computer Science (IOCS), 2021)
This study aims to optimize the selection of raw water source options at the location of Pamsimas III program. This analysis will affect the condition of the facilities that will function properly. The data mining method used in this study is a classification model with the Naive Bayes algorithm using the Rapidminer application tool. The data processed is SIM Pamsimas III data with the object of research on the new village drinking water facilities for the 2017-2019 Pamsimas program. This research analyzes the prediction of the condition of drinking water facilities based on the option of raw water sources. So this research can helps to determine the level of potential facilities that will function properly based on the specified raw water source options. Based on the research conducted, predictive analysis using Naive Bayes has an accuracy rate of 85.05%. So that the selection of the raw water source option can predict the condition of the drinking water facilities being built will function properly.