Penerapan Algoritma Naive Bayes untuk Klasifikasi Prediksi Penerimaan Siswa Baru
Implementation of Naïve Bayes Algorithm for Prediction Classification of New Student Admissions
-
Sulaiman Sinaga
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
- Rahmat W Sembiring Politeknik Negeri Medan, Medan, Indonesia
- S. Sumarno STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Keywords:
Naive Bayes; Klasifikasi; Pembelajaran Mesin; Siswa Baru; Prediction; Student Admissions; Rapid Miner
Abstract
The number of prospective new students who enroll in several private schools, especially in Vocational High Schools (SMK), varies wildly, depending on the quality of the school and the interests of the prospective students themselves, also contributing to influencing the number of students. In addition, the private status usually makes private vocational schools the second alternative after the state, so the number of new students enrolling in private vocational schools is difficult to predict. The same is true for the Children of the Nation Private Vocational School. Therefore, this study aims to predict new students who will be accepted in the form of classification at the Anak Bangsa private vocational school using the Naive Bayes algorithm, a machine learning method. This research data is sourced from the Anak Bangsa Private Vocational School from 2018 to 2021, with a total sample of 110 student data, from 162 students. Based on testing as many as 30 testing data processed using Rapid Miner, this study obtained an accuracy rate of 76.67%. Namely, 26 students were accepted, and four were not accepted. The conclusion is that the prediction process in the form of classification using Naive Bayes can be faster and more accurate and produces a high level of accuracy.
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A. Akrim, Menjadi Generasi Pemimpin Apa Yang Dilakukan Sekolah? 2019.
Samsir et al., “Naives Bayes Algorithm for Twitter Sentiment Analysis,” J. Phys. Conf. Ser., vol. 1933, no. 1, p. 012019, 2021, doi: 10.1088/1742-6596/1933/1/012019.
J. E. Falabitsa and L. Lelah, “Pengembangan Sistem Seleksi Penerimaan Calon Siswa Baru Dengan Metode Simple Additive Weighting,” Jurnal Ilmiah Informatika (JIF), vol. 8, no. 2, pp. 113–119, 2020.
A. Yusnita, S. Lailiyah, and K. Saumahudi, “Penerapan Algoritma Naïve Bayes Untuk Penerimaan Peserta Didik Baru,” Jurnal Informatika Wicida, vol. 10, no. 1, pp. 11–16, 2021.
S. R. D. Wibowo, D. M. Midyanti, and R. Hidayati, “Penerapan Metode Grey Relational Analysis Pada Penerimaan Pengajar Yayasan Pendidikan Sekolah Bruder Kota Pontianak,” Coding : Jurnal Komputer dan Aplikasi, vol. 08, no. 01, pp. 102–111, 2020.
A. Wanto, D. Suhendro, and A. P. Windarto, “Pelatihan dan Bimbingan dalam Pemanfaatan Internet yang Baik dan Aman bagi Pelajar SMK Anak Bangsa Desa Bandar Siantar Kabupaten Simalungun,” E-Dimas: Jurnal Pengabdian kepada Masyarakat, vol. 9, no. 2, pp. 149–157, 2018.
R. Watrianthos, S. Suryadi, D. Irmayani, M. Nasution, and E. F. S. Simanjorang, “Sentiment Analysis Of Traveloka App Using Naïve Bayes Classifier Method,” Int. J. Sci. Technol. Res., vol. 8, no. 07, pp. 786–788, 2019
T. H. Apandi and C. A. Sugianto, “Analisis Komparasi Machine Learning Pada Data Spam Sms,” Jurnal TEDC, vol. 12, no. 1, pp. 58–62, 2018.
D. Y. Utami, E. Nurlelah, and N. Hikmah, “Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease,” Journal of Informatics and Telecommunication Engineering, vol. 4, no. 1, pp. 76–85, 2020.
A. R. Damanik et al., “Prediksi Tingkat Kematian di Indonesia Akibat Covid-19 Menggunakan Algoritma Naïve Bayes,” SISTEMASI: Jurnal Sistem Informasi, vol. 11, no. 1, pp. 73–83, 2022.
R. Watrianthos, E. F. S. Simanjorang, M. Syaifullah, and I. R. Munthe, “Penerapan Metode Promethee Sebagai Sistem Pendukung Keputusan Pemeringkatan Siswa,” J. MEDIA Inform. BUDIDARMA, vol. 3, no. 4, pp. 381–386, 2019, doi: 10.30865/mib.v3i4.1546
T. H. Apandi and C. A. Sugianto, “Algoritma Naive Bayes untuk Prediksi Kepuasan Pelayanan Perekaman e-KTP ( Naive Bayes Algorithm for Satisfaction Prediction of e-ID,” JUITA (Jurnal Informatika) UMP, vol. 7, no. 2, pp. 125–128, 2019.
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E. Indrayuni, “Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes,” Jurnal Khatulistiwa Informatika, vol. 7, no. 1, pp. 29–36, 2019.
R. Bahtiar, M. D. S. T, A. Setiawan, and P. Rosyani, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),” Jurnal Kreativitas Mahasiswa Informatika, vol. 1, no. 2, pp. 99–103, 2020.
S. E. Y. Putri, Saniati, and A. Surahman, “Penerapan Model Naive Bayes Untuk Memprediksi Potensi Pendaftaran Siswa di SMK Taman Siswa Teluk Belitung Berbasis Web,” Jurnal JECSIT, vol. 1, no. 1, pp. 82–87, 2021.
amik tunas bangsa and S. Utara, “Teknik Data Mining Dalam Prediksi Jumlah Siswa Baru Dengan Algoritma Naive Bayes,” Kesatria: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen, vol. 2, no. 2, pp. 108–117, 2021.
D. Alita, I. Sari, and A. Rahman Isnain, “Penerapan Naïve Bayes Classifier Untuk Pendukung Keputusan Penerima Beasiswa,” Jurnal Data Mining dan Sistem Informasi (JDMSI), vol. 2, no. 1, pp. 17–23, 2021.
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Published
2022-02-01
How to Cite
[1]
S. Sinaga, R. W. Sembiring, and S. Sumarno, “Penerapan Algoritma Naive Bayes untuk Klasifikasi Prediksi Penerimaan Siswa Baru”, J. Mach Learn. Data Anal., vol. 1, no. 1, pp. 55-64, Feb. 2022.
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