THE IMPLEMENTATION OF NAÏVE BAYES AND SUPPORT VECTOR MACHINE (SVM) ALGORITHM , IN DETERMINING ACHIEVING STUDENTS IN SMP NEGERI 8 CIMAHI
Abstract
Classification is a technique in data mining to classify data based on data attachment to sample data. In this study, we conducted a comparison of classification techniques to classify students who excel in the dataset of SMP Negeri 8 CIMAHI. Comparison of classification techniques is carried out to see which models in the classification technique are most effective for classifying targets in the dataset of SMP Negeri 8 CIMAHI. The classification technique used is the Support Vector Machine and Naïve Bayes. The classification process begins with preprocessing data to remove missing values and select features in the dataset. After testing, it was found that the accurate classification results were obtained by the Support Vector Machine model with an accuracy value of 93%. Whereas for the Naïve Bayes model the accuracy results are 88%. for this case the Support Vector Machine was chosen as the model that has the best accuracy and the resulting visualization results are clearer to classify outstanding students in the dataset of SMP Negeri 8 CIMAHI.
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References
J. I. Kartika, E. Santoso, and Sutrisno, “Penentuan Siswa Berprestasi Menggunakan Metode K-Nearest Neighbor dan Weighted Product (Studi Kasus: SMP Negeri 3 Mejayan),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 5, pp. 352–360, 2017.
N. Jannah and T. Yulianto, “Mengelompokkan Siswa Berprestasi Akademik dengan Menggunakan Metode K Means Kelas VII MT,” Zeta - Math J., vol. 2, no. 2, pp. 41–45, 2016.
Y. Angraini, S. Fauziah, and J. L. Putra, “Analisis Kinerja Algoritma C4.5 Dan Naïve Bayes Dalam Memprediksi Keberhasilan Sekolah Menghadapi UN,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 2, pp. 285–290, 2020.
M. Marlina, W. Yusnaeni, and N. Indriyani, “Sistem Pendukung Keputusan Pemilihan Siswa Yang Berhak Mendapatkan Beasiswa Dengan Metode Topsis,” J. Techno Nusa Mandiri, vol. 14, no. 2, pp. 147–152, 2017.
A. Topadang and R. T. Tulili, “Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Di Jemaat Moria Samarinda Seberang Dengan Metode Simple Additive Weigthting,” J. Sains Terap. Teknol. Inf., vol. 10, no. 2, p. 122, 2018.
M. L. Sibuea and A. Safta, “Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustring,” Jurteksi, vol. 4, no. 1, pp. 85–92, 2017.
F. Rahman, D. Muhammad, and I. Firdaus, “Penerapan Data Mining Metode Naïve Bayes Untuk Prediksi Hasil Belajar Siswa Sekolah Menengah Pertama (Smp),” Al Ulum Sains dan Teknol., vol. 1, no. 2, pp. 76–78, 2016.
A. Saifudin, “Metode Data Mining Untuk Seleksi Calon Mahasiswa,” J. Teknol., vol. 10, no. 1, pp. 25–36, 2018.
H. Sulistiani, “Penerapan Algoritma Klasifikasi Sebagai Pendukung Keputusan Pemberian Beasiswa Mahasiswa,” pp. 300–305, 2018.
P. A. Octaviani, Y. Wilandari, and D. ISpriyanti, “Penerapan Metode Klasifikasi Support Vector Machine (SVM) Pada Data Akreditasi Sekolah Dasar (SD) Di Kabupaten Magelang,” J. Gaussian, vol. 3, pp. 811–820, 2014.
M. A. Sembiring, M. F. L. Sibuea, and A. Sapta, “Analisa Kinerja Algoritma C.45 Dalam Memprediksi Hasil Belajar,” J. Sci. Soc. Res., vol. 1, no. 1, pp. 73–79, 2018.
T. Setiyorini and R. T. Asmono, “Penerapan Metode K-Nearest Neighbor Dan Information Gain Pada Klasifikasi Kinerja Siswa,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 1, pp. 7–14, 2019.
A. Noviriandini and N. Nurajijah, “Analisis Kinerja Algoritma C4.5 Dan Naïve Bayes Untuk Memprediksi Prestasi Siswa Sekolah Menengah Kejuruan,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 1, pp. 23–28, 2019.
A. Adhitiawarman, D. Hartanto, A. Sudradjat, and R. Sari4, “Laporan Akhir Penelitian Mandiri 2021,” Jakarta, 2021.
Suhardjono, W. Ganda, and H. Abdul, “Prediksi Kellusan Menggunakan SVM Berbasis PSO,” Bianglala Inform., vol. 7, no. 2, pp. 97–101, 2019.
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