Perbandingan 4 Algoritma Berbasis Particle Swarm Optimization (PSO) Untuk Prediksi Kelulusan Tepat Waktu Mahasiswa

Authors

  • Moh. Zainuddin STMIK

Keywords:

Algoritma Naive Bayes, Decision Tree(C4.5), k-Nearest Neighbor(k-NN), Neural Network, Particle Swarm Optimization(PSO), keakurasian, Area Under the Curve(AUC)

Abstract

The purpose of this study was to find the best algorithm in making predictions of students' graduation from 4 algorithms: Naive Bayes Algorithm, Decision Tree (C4.5), k-Nearest Neighbor (kNN), Neural Network based Particle Swarm Optimization (PSO) as references to make policies and academic acts (BAAK) in reducing students who graduated late and did not pass. The results show that PSO-k-Nearest Neighbor (k-NN) algorithm based on k-optimum = 19 has the best performance of 4 algorithms, with Accuracy = 74,08% and Area Under the Curve (AUC) = 0,788. The addition of the Particle Swarm Optimization (PSO) feature always increases the accuracy value, where the highest accuracy value lies in the Decision Tree Algorithm (C4.5) of 5.21%, the lowest on the Naive Bayes Algorithm of 2.13%.

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References

Anuradha,C., T.Velmurugan. (July 2015). A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance. Indian Journal of Science and Technology, Vol 8(15), DOI: 10.17485/ijst/2015/v8i15/74555, ISSN (Print) : 0974-6846. ISSN (Online) : 0974-5645,.
Asif R., Agathe M., Mahmood KP. (2015). Predicting Student Academic Performance at Degree Level: A Case Study. I.J. Intelligent Systems and Applications, 01, 49-61. Published Online December 2014 in MECS (http://www.mecs-press.org/). DOI: 10.5815/ijisa.2015.01.05.
Ayu, Mutiara B.,H.Irwan Budiman and Andi Farmadi. (September 2015). Penerapan K-Optimal Pada Algoritma k-NN untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer FMIPA UNLAM Berdasarkan IP Sampai Dengan Semester 4. Kumpulan jurnaL Ilmu Komputer (KLIK) ISSN: 2406-7857. Volume 02, No.02.
Handjaratie,Lillyan. (2015). Prediction And Data Mapping of Students Of Engineering Faculty, Universitas Negeri Gorontalo Using Data Mining.
Hanief Muhamad M., Metri Annisa, Narendi Muhandri and Kadarsyah Suryadi. (2009). Prediksi Masa Studi Sarjana Dengan Artificial Neural Network. Internetworking Indonesia Journal. Vol.1/No.2.
Hartanto,David H.,Seng Hansun. (Juni 2014). Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa. ULTIMATICS, Vol. VI, No. 1 | ISSN 2085-4552.
Mu’aris Khoirul. (2015). Komparasi Pemodelan Data Menggunakan C4.5 Dan C4.5 Berbasis Particle Swarm Optimization Untuk Memprediksi Kelulusan Mahasiswa.
Nursalim, Suprapedi and H.Himawan. (April 2014). Klasifikasi Bidang Kerja Lulusan Menggunakan Algoritma K-Nearest Neighbor. Jurnal Teknologi Informasi, ISSN 1414-9999. Volume 10 Nomor 1.
Nuqson Masykur Huda. (2010). “Aplikasi Data Mining Untuk Menampilkan Informasi Tingkat Kelulusan Mahasiswa”, Semarang.
Prabowo. (2012). Aneka Teknik, Piranti dan Penerapan Data Mining : Studi Kasus Peramalan Harga Saham Industri Telekomunikasi Berbasis Jaringan Saraf Tiruan. Modul Perkuliahan Universitas Budi Luhur.
Ricky, Ade Rozzaqi. (Juni 2015). Naïve Bayes dan Filtering Feature Selection Information Gain untuk Prediksi Ketepatan Kelulusan Mahasiswa. Jurnal Informatika UPGRIS Volume 1.

Published

2018-10-31