Sebuah Penerapan Metode Naïve Bayes dalam Klasifikasi Masyarakat Miskin pada Desa Tanjungsari - Tundo Tundo; Mesra Betty Yel; Veri Arinal; Bobby Arvian James; Andi Saidah (Jurnal Informatika dan Rekayasa Perangkat Lunak, 2024)
The main problem in efforts to reduce poverty today is related to the fact that economic growth is not spread evenly. The research will carry out classification based on data on poor residents obtained from Tanjungsari Village, Kajen District using data mining techniques. The attributes that will be used in classifying residents are Education, Occupation, Income, Dependents, Electricity Power, Home Ownership Status. The method that will be used is the Naïve Bayes Classifier method, which is one of the classification techniques in data mining. The expected result of this research is to obtain information/data regarding determining poverty in the Tanjungsari Village community which can be used by the district government to design strategies to improve community welfare. The classification system for the poor population of Tanjungsari Village is based on the results of confusion matrix testing, using the Naïve Bayes classification method based on test data taken from the research object, obtaining an accuracy rate of 83%, a recall value of 100%, a precision of 83%, and an error rate of 17%.