OPTIMASI DECISION TREE MENGGUNAKAN PARTICLE SWARM OPTIMIZATION PADA DATA SISWA PUTUS SEKOLAH

Mirza Yogy Kurniawan
Muhammad Edya Rosadi

JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Vol. 2 No. 1 (2017)

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.