Klasifikasi Masyarkat Miskin Menggunakan Metode Naïve Bayes Di Desa Jati Mulya - Dede, Dede Latipah; Mochamad Arief Mardiansah; Esthi Adityarini (POLITEKNIK NEGERI JAKARTA, 2024)
Abstract - Poverty is a major problem in Indonesia, including in Jati Mulya Village, East Sepatan District. Various efforts have been made to overcome poverty, such as social assistance from the government which aims to meet basic needs and improve living standards. However, the technical distribution of this assistance program is not quite evenly distributed. This research uses the Naive Bayes method to carry out classification with the help of the Orange application. The data used is data on poor residents from Jati Mulya Village RT 002 RW 004, using data mining techniques. The 18 data sets can be classified from training data and testing data into two classes, namely the poor and non-poor categories. Attributes used for population classification include age, education, employment, income, dependents, and marital status. The research results show 96% accuracy from manual calculations and the Orange application. Based on these results, the classification system developed can be used as a reference for decision makers. Keywords: Poverty level, Data Mining, Classification, Naive Bayes