Open AccessComparation of K-Nearest Neighboor (K-NN) and Naive Bayes Algorithm for the Classification of the Poor in Recipients of Social Assistance
Author(s) -
Elly Firasari,
Nurul Khasanah,
Umi Khultsum,
Desiaur Kholifah,
Rachman Komarudin,
Wiwiek Widyastuty
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012077
Subject(s) - naive bayes classifier , bayes' theorem , poverty , agency (philosophy) , computer science , java , population , artificial intelligence , machine learning , government (linguistics) , identification (biology) , data collection , process (computing) , algorithm , statistics , mathematics , support vector machine , sociology , demography , economic growth , bayesian probability , economics , social science , linguistics , philosophy , botany , biology , programming language , operating system
Poverty is a problems faced by developing countries, as well as Indonesia. According to data from the Central Statistics Agency in 2018, more than half the distribution of the poor population in Indonesia is in Java, which is 13,340.15 million people. Somokerto village is one of the villages in the district of Magelang, Central Java, which receives government assistance in an effort to reduce poverty. But in the process of classifying citizens who are entitled to receive assistance is still done manually. Manual classification is considered inaccurate in obtaining the results of social assistance recipients. In overcoming this problem, we need a systematic calculation to get accurate results. In this case, the researcher uses data mining classification calculation by comparing 2 calculation methods, namely K-NN and Naïve Bayes. The reseachers use Rapidminer tools. The research stages are identification of problems, data collection, implementation K-NN, Implementation Naive bayes, data testing process to produce accuracy and compare the result. The results obtained are the accuracy of Naïve Bayes higher than K-NN, namely Naïve Bayes 89.04% and K-NN 87.67%. This figure is classified in the category of good classification. From the results of the study it can be concluded the Naïve Bayes algorithm is suitable to be applied in the calculation of recipients of social assistance.