Abstract-The poverty rate that was recorded high in Indonesia becomes main priority the government to find a solution to poverty rate was below 10%. Initial identification the potential poverty becomes a very important thing to anticipate the amount of the poverty rate. Naive Bayes Classifier (NBC) algorithm was one of data mining algorithms that can be used to perform classifications the family poor with 11 indicators with three classifications. This study using sample data of poor families a total of 219 data. A system that built use Java programming compared to the result of Weka software with accuracy the results of classification of 93%. The results of classification data of poor families mapped by adding latitudelongitude data and a photograph of the house of the condition of poor families. Based on the results of mapping classifications using NBC can help the government in Kabupaten Bantul in examining the potential of poor people.

Status kemiskinan penduduk di Kecamatan Bantul diklasifikasikan  melalui  11 aspek. Jumlah nilai dari keseluruhan aspek akan menentukan kelas kemiskinan diantaranya kelas miskin, sangat miskin dan rawan miskin. Klasifikasi dengan model tersebut membuat hasil pengelompokan kurang akurat sehingga perlu dicoba klasifikasi dengan model yang lain. Analisis performa klasifikasi data penduduk miskin pada penelitian ini dikerjakan menggunakan metode klasifikasi K-NN dan C4.5. Kedua algoritma klasifikasi akan dibandingkan performanya melalui uji akurasi, precision dan recall.Hasil analisis perbandingan performa algoritma K-NN dengan parameter setting k=1 memiliki performa yang paling baik dibandingkan dengan nilai k=10, 100, 1000 maupun algoritma C4.5. Hasil nilai Accuracy sebesar 94,71%, precision sebesar  84,96% dan recall sebesar 83,6%.