Open AccessOPTIMASI DECISION TREE MENGGUNAKAN PARTICLE SWARM OPTIMIZATION PADA DATA SISWA PUTUS SEKOLAH
Author(s) -
Mirza Yogy Kurniawan,
Muhammad Edya Rosadi
Publication year - 2017
Publication title -
jtiulm (jurnal teknologi informasi universitas lambung mangkurat)
Language(s) - English
Resource type - Journals
eISSN - 2528-2514
pISSN - 2527-5399
DOI - 10.20527/jtiulm.v2i1.13
Subject(s) - id3 , decision tree , particle swarm optimization , id3 algorithm , drop out , government (linguistics) , computer science , obstacle , tree (set theory) , dropout (neural networks) , machine learning , decision tree learning , artificial intelligence , incremental decision tree , mathematics , geography , economics , mathematical analysis , linguistics , philosophy , archaeology , demographic economics
Education is the right of every citizen, even government makes program to promote the compulsory education of 12 years. Drop out of school has become an obstacle to the government program where the dropout is caused by many factors, including economic factors, geographical conditions, and students' own desires. ID3 is able to generate a decision tree from a very large data set. This decision tree can be used as a reference for possible drop out of students. In order to be a good reference then the resulting classification must have a high accuracy. PSO is known to increase the accuracy of various kinds of data mining classification. ID3 in this study yielded 72.5% accuracy while after optimized with PSO then ID3 will yield 85% accuracy.