ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES DALAM MEMPREDIKSI KEBERHASILAN SEKOLAH MENGHADAPI UN
Abstract
The national exam (UN) is one of the determinants of student graduation, both elementary school, junior high school and even high school. There are many businesses that are carried out by schools to prepare their students to face national examinations. In fact almost all schools provide material deepening to their students for subjects tested at the national examination. Therefore, this study was conducted to determine the level of success of the school in preparing students in facing national examinations. The method used is a decision tree with C4.5 algorithm and naïve Bayes algorithm. From the results of the study, the results of the accuracy of the naïve bayes algorithm were as big as 95,50% , while accuracy using the c4.5 algorithm is equal to 78,50%. Then it can be concluded that the predictions generated from the naïve bayes algorithm are better compared to the c4.5 algorithm .
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References
Angraini Yeni, Fauziah Siti, L. P. J. (2020). Laporan Akhir Penelitian Mandiri:Analisis Kinerja Algoritma C4.5 Dan Naïve Bayes Dalam Memprediksi Keberhasilan Sekolah Menghadapi UN. Jakarta.
Bustami. (2014). Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi. Jurnal Informatika (Yogyakarta), 8(1), 884–898. https://doi.org/10.26555/jifo.v8i1.a2086
Imelda A.Muis & Muhammad Affandes, M. . (2015). Penerapan Metode Support Vector Machine ( SVM ) Menggunakan Kernel Radial Basis Function ( RBF ) Pada Klasifikasi Tweet. Sains, Teknologi Dan Industri.UIN Sultan Syarif Kasim Riau, 12(2), 189–197. Retrieved from http://103.193.19.206/index.php/sitekin/article/view/1010
Nawawi, H. M., Purnama, J. J., Hikmah, A. B., Komputer, S. I., Informasi, S. S., Bina, U., & Informatika, S. (2019). KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES, 15(2), 189–194. https://doi.org/10.33480/pilar.v15i2.669
Pang-Ning, T., Steinbach, M., & Kumar, V. (2006). Introduction to data mining: Solution Manual. Library of Congress, 796. https://doi.org/10.1016/0022-4405(81)90007-8
Rahman, F., Muhammad, D., & Firdaus, I. (2016). Penerapan Data Mining Metode Naïve Bayes Untuk Prediksi Hasil Belajar Siswa Sekolah Menengah Pertama (Smp). Al Ulum Sains Dan Teknologi, 1(2), 76–78. Retrieved from http://ojs.uniska-bjm.ac.id/index.php/JST/article/view/436
Ridwan, M. (2017). SISTEM REKOMENDASI PROSES KELULUSAN MAHASISWA BERBASIS ALGORITMA KLASIFIKASI C4.5. Jurnal Ilmiah Informatika, 2(1), 105–111. https://doi.org/10.5281/JIMI.V2I1.27
Sabilla, W. I., & Putri, T. E. (2017). Prediksi Ketepatan Waktu Lulus Mahasiswa dengan k-Nearest Neighbor dan Naïve Bayes Classifier. Jurnal Komputer Terapan, 3(2), 233–240. Retrieved from https://jurnal.pcr.ac.id/index.php/jkt/article/view/1544
Saleh, A. (2015). Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga. Citec Journal, 2(3), 207–217. https://doi.org/doi.org/10.24076/citec.2015v2i3.49
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