Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City)
Abstract
School majors conducted in high school are based on interests and these have a goal to provide opportunities for learners to develop the competence of attitudes, skills competence of learners in accordance with interests, talents, and academic ability in a group of scientific subjects.In this research, the researcher uses two algorithm models that is a comparison between the C4.5 algorithm and also the Naive Bayes algorithm. In this study, the data used is the results of school entrance test data and also the data from psychological results for students who have been declared passed the entrance test school SMAN 2 Bekasi City academic year 2018/2019. By comparison of two data mining classification algorithm, can be proved with accuracy result and AUC value from each algorithm that is for Naive Bayes accuracy = 76,43% and AUC value = 0,846, while for algorithm C4.5 accuracy = 70,29% and AUC value = 0.738.
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Aprilla Dennis. (2013). Belajar Data Mining dengan RapidMiner. Innovation and Knowledge Management in Business Globalization: Theory & Practice, Vols 1 and 2, 5(4), 1–5. https://doi.org/10.1007/s13398-014-0173-7.2
Asroni, A., Fitri, H., & Prasetyo, E. (2018). Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik). Semesta Teknika, 21(1), 60–64. https://doi.org/10.18196/st.211211
Bahar. (2011). Penentuan Jurusan Sekolah Menengah Atas Dengan Algoritma Fuzzy C-Means. Universitas Dian Nuswatoro Semarang.
Gorunescu, F. (2011). Data Mining: Concepts, Model and Techniques. https://doi.org/10.1007/978-3-642-19721-5
Hastuti, K. (2012). Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Mahasiswa Non Aktif. Semantik, 241–249.
Hermanto, Mustopa, A., & Kuntoro, A. Y.
(2019). Hasil Akhir Penelitian Mandiri: Algoritma Klasifikasi Naive Bayes Dan Support Vector Machine Dalam Layanan Komplain Mahasiswa. Jakarta, Indonesia
Hertyana, H. (2018). Analisa Penentuan Jurusan Pada Sma. Kartika Viii-1 Menggunakan Metode Fuzzy Inference System Mamdani. Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, 3(2), 119–126. Retrieved from http://ejournal.nusamandiri.ac.id/ejurnal/index.php/jitk/article/view/699/409
Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining: Second Edition. In Discovering Knowledge in Data: An Introduction to Data Mining: Second Edition (Vol. 9780470908). https://doi.org/10.1002/9781118874059
Maimon, O., & Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook. In Springer Handbook of Geographic Information (Second). https://doi.org/10.1007/978-0-387-09823-4
North, M. (2012). Data Mining for the Masses. In Computer. Retrieved from http://1xltkxylmzx3z8gd647akcdvov.wpengine.netdna-cdn.com/wp-content/uploads/2013/10/DataMiningForTheMasses.pdf%5Cnhttps://sites.google.com/site/dataminingforthemasses/
Nugroho, Y. S. (2015). Klasifikasi dan Klastering Penjurusan Siswa SMA Negeri 3 Boyolali. Khazanah Informatika: Jurnal Ilmu Komputer Dan Informatika, 1(1), 1. https://doi.org/10.23917/khif.v1i1.1175
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical machine learning tool and techniques (third Edit). Morgan Kaufmann.
DOI: https://doi.org/10.18196/st.v23i1.7381
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