Naive Bayes Classifier Algorithm Approach for Mapping Poor Families Potential
Sri Redjeki, Mohammad Guntara, Pius Dian Widi Anggoro · INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE · 2015
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 latitude-longitude 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.