Illiteracy Classification Using K Means-Naïve Bayes Algorithm

JOIV: International Journal on Informatics Visualization

Aji Prasetya Wibawa¹  Triyanna Widiyaningtyas¹  Muhammad Firman Aji Saputra¹ 

[1] State University of Malang

关键词: Illiteracy;    Clustering;    K means;    Classification;    Naïve Bayes;   

DOI  :  10.30630/joiv.2.3.129

学科分类：数学（综合）

来源: Politeknik Negeri Padang

【 摘 要 】

Illiteracy is an inability to recognize characters, both in order to read and write. It is a significant problem for countries all around the world including Indonesia. In Indonesia, illiteracy rate is generally set as an indicator to see whether or not education in Indonesia is successful. If this problem is not going to be overcome, it will affect people’s prosperity. One system that has been used to overcome this problem is prioritizing the treatment from areas with the highest illiteracy rate and followed by areas with lower illiteracy rate. The method is going to be a way easier to be applied if it is supported by classification process. Since the classification process needs a class, and there has not been any fine classification of illiteracy rate, there is needed a clustering process before classification process. This research is aimed to get optimal number of classes through clustering process and know the result of illiteracy classification process. The clustering process is conducted by using k means algorithm, and for the classification process is conducted by using Naïve Bayes algorithm. The testing method used to assess the success of classification process is 10-fold method. Based on the research result, it can be concluded that the optimal illiteracy classes are three classes with the classification accuracy value of 96.4912% and error rate value of 3.5088%. Whereas the classification with two classes get the accuracy value of 93.8596% and error rate value of 6.1404%. And for the classification with five classes get the accuracy value of 90.3509% and error rate value of 9.6491%.

【 授权许可】

CC BY-SA