Penerapan Particle Swarm Optimization (PSO) untuk Klasifikasi dan Analisis Kredit dengan Menggunakan Algoritma C4.5

Authors

  • Saprudin Saprudin Universitas Pamulang

DOI:

https://doi.org/10.32493/informatika.v2i4.1488

Keywords:

Klasifikasi, Optimasi, Algoritma C4.5

Abstract

Teknik klasifikasi saat ini sudah banyak digunakan untuk mengatasi permasalahan yang terkait dengan penggolongan data.  Implementasi teknik klasifikasi ini dapat diterapkan pada berbagai bidang. Tujuan utama dari metode klasifikasi adalah proses penemuan model (fungsi)  yang menggambarkan dan membedakan  kelas data. Salah satu metode yang paling baik untuk klasifikasi adalah algoritma C4.5. Untuk itu penulis menggunakan metode klasifikasi C4.5; dan untuk dapat meningkatkan nilai akurasinya dioptimasi dengan PSO (Particle Swarm Optimization) dimana data uji yang digunakan adalah Data German Credit. Dari hasil percobaan yang dilakukan model algoritma C4.5 berbasis Particle Swarm Optimization (PSO) mendapatkan hasil terbaik yaitu 70%; sedangkan model algoritma C4.5 tanpa Particle Swarm Optimization ( PSO)   hanya 68;6%;  sehingga selisih untuk nilai akurasi sebesar 1;4%. Dapat disimpulkan bahwa penerapan teknik optimasi particle swarm optimization dapat meningkatkan nilai akurasi pada algoritma C4.5.
Jurnal Informatika Universitas Pamulang Vol. 2 No. 4 Desember 2017

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Published

2017-12-25

Issue

Vol. 2 No. 4 (2017): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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