STMIK Palangkaraya - Indonesia
PERBANDINGAN METODE ALGORITMA C4.5 DAN NAÃÂVE BAYES UNTUK MEMPREDIKSI KELULUSAN MAHASISWA
This study was conducted to compare the accuracy of two algorithm methods, namely the C4.5 algorithm and Naïve Bayes algorithm on a number of datasets. The sources of datasets used are student data of the Informatics Engineering Study Program (STMIK Palangkaraya) where each dataset has a different amount of data (instances) and number of attributes. Based on the results of the comparison study of the C4.5 and Naïve Bayes Algorithm for Predicting the On-time Graduation of STMIK Palangkaraya Students, the results obtained from the accuracy of the two algorithms show that the accuracy of the C4.5 Algorithm is 90% better than the Naïve Bayes algorithm, which is only 85%.  The C4.5 algorithm also gives values on recall and precision of 92% and 94% better than the Naïve Bayes algorithm with values of 86% and 93%, respectively.
Keywords: C4.5 algorithm, Naïve Bayes, Graduation Prediction, STMIK Palangkaraya
- Anam, C., & Santoso, H. B. (2018). Perbandingan Kinerja Algoritma C4.5 dan Naive Bayes untuk Klasifikasi Penerima Beasiswa. Jurnal Energy, 13-19.
- Bahri, S., Midyanti, D. M., & Hidayati, R. (2018). Perbandingan Algoritma Naive Bayes dan C4.5 Untuk Klasifikasi Penyakit Anak. Seminar Nasional Aplikasi Teknologi Informasi (SNATi) (hal. 24-31). Yogyakarta: Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia.
- Fitriani, E. (2020). Perbandingan Algoritma C4.5 dan Naïve Bayes Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan. Jurnal Sistemasi, 103-115.
- Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers.
- Rusdiana, L. (2017). Perbandingan Metode K-Nearest Neighbor dan Fuzzy C-Means dalam Menentukan Predikat Kelulusan Mahasiswa. PROSIDING SNSebatik (hal. 21-26). Samarinda: STMIK Widya Cipta Dharma Samarinda.
- Rusdiana, L., & Rosmiati. (2016). Aplikasi Berbasis Fuzzy C-Means Dalam Penentuan Predikat Kelulusan Mahasiswa. Jurnal Ilmu Komputer, 1-9.
- Rusdiana, Lili. (2018). K-Means Algorithm to Group Students’ Academic Status at STMIK Palangka Raya. CSRID (Computer Science Research and Its Development Journal), 124-134.
- Rusdiana, Lili; Sam'ani. (2017). Pemodelan K-Means Pada Penentuan Predikat Kelulusan Mahasiswa STMIK Palangka Raya. Jurnal Saintekom, 1-15.
- Wati, R. (2016). Penerapan Algoritma Genetika Untuk Seleksi Fitur Pada Analisis Sentimen Review Jasa Maskapai Penerbangan Menggunakan Naive Bayes. Jurnal Evolusi, 4(1), 25-31.
- Yahya, N., & Jananto, A. (2019). Komparasi Kinerja Algoritma C.45 Dan Naïve Bayes Untuk Prediksi Kegiatan Penerimaan Mahasiswa Baru. Seminar Nasional Multi Disiplin Ilmu (hal. 221-228). Semarang: Proceeding SENDI_U.
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