IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MENENTUKAN TINGKAT KEDISIPLINAN SISWA

  • Sidik Rahmatullah STMIK Dian Cipta Cendikia Kotabumi
  • Supriyanto Supriyanto
  • Rustam Rustam
  • Merri Parida
  • Pakarti Riswanto
  • Iko Prastiyo
Keywords: data mining, discipline level, Naïve Bayes, vb.net, waterfall.

Abstract

Senior High School is a school that has a mission to prepare qualified and reliable students by accommodating a variety of backgrounds and different student personalities. With these differences encourage students to commit acts of violation in school. These violations cause delays in learning activities in schools, and reduce the quality of schools. To help and minimize the occurrence of violations in schools, this research was conducted using data mining techniques with the naïve Bayes classifier method and the system development method is waterfall. The implementation will be applied to the vb.net program with the 2019 visual studio tool and using the MYSQLi database. The attributes that will be used are gender, type of residence, school origin, distance of the house, father's education, father's occupation, father's income, mother's education, mother's occupation, and mother's income. The application of this system aims to assist schools in classifying the level of student discipline and produce outputs of grouping levels of student discipline with high and low classes. From the test results of testing data which amounted to 8 records with 10 variables produce an accuracy of 63% and an error of 38%, so it can be concluded that this system is good to be seen from the data obtained based on its suitability.

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References

[1] Arsaf, N. A. (2015). Faktor penyebab pelanggaran tata tertib (studi pada siswa di sma negeri 18 makassar). Jurnal Sosialisasi Pendidikan Sosiologi-FIS UNM Peserta, 02(1), 1–5.
[2] Diagram alir - Wikipedia bahasa Indonesia, ensiklopedia bebas. (n.d.). Retrieved March 30, 2020, from https://id.wikipedia.org/wiki/Diagram_alir
[3] Hasibuan, N. A., Silalahi, N., Nasution, S. D., Sutiksno, D. U., Nurdiyanto, H., Buulolo, E., Ambon, P. N., Pendahuluan, I., & Mining, A. D. (2017). Implementasi Data Mining Untuk Pengaturan Layout. 4(4), 6–11.
[4] Hayuningtyas, R. Y. (2019). Penerapan Algoritma Naïve Bayes untuk Rekomendasi Pakaian Wanita. Jurnal Informatika, 6(1), 18–22. https://doi.org/10.31311/ji.v6i1.4685
[5] Iskandar, D., & K. Suprapto, Y. (2015). Perbandingan Akurasi Klasifikasi Tingkat. Jurnal Ilmiah NERO, 2(1), 37–43. https://doi.org/10.21107/NERO.V2I1.42
[6] Kasus, S., Dehasen, U., Haryati, S., Sudarsono, A., & Suryana, E. (2015). IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MASA STUDI MAHASISWA MENGGUNAKAN ALGORITMA C4 . 5. 11(2), 130–138.
[7] Kurniawan, H., Apriliah, W., Kurniawan, I., Firmansyah, D., Informasi, S., & Pinjam, S. (2020). Penerapan Metode Waterfall Dalam Perancangan Sistem Informasi Penggajian Pada Smk Bina Karya Karawang. Jurnal Rosma, 14(4), 13–23.
[8] Nurabadi, A., Malang, U. N., & Malang, J. S. (2020). Hubungan keikutsertaan ekstrakurikuler pramuka dengan tingkat kedisiplinan siswa. 3, 11–18.
[9] Pangkalan data - Wikipedia bahasa Indonesia, ensiklopedia bebas. (n.d.). Retrieved March 30, 2020, from https://id.wikipedia.org/wiki/Pangkalan_data
[10] Sarkawi, D., & Sekretari, P. S. (2018). PERANCANGAN APLIKASI PENJUALAN DENGAN METODE WATERFALL. Jurnal PETIR, 11(1), 9–24.
[11] Sholichin, A. (2016). Pemrograman Web dengan PHP dan MySQ. 14. https://books.google.co.id/books?id=kcD4BQAAQBAJ&lpg=PA1&dq=php&pg=PA1#v=onepage&q=php&f=false
[12] Siregar, A. M., Kom, S., Puspabhuana, M. K. D. A. N. A., Kom, S., & Kom, M. (2017). DATA MINING: Pengolahan Data Menjadi Informasi dengan RapidMiner. CV Kekata Group.
[13] Studi, P., Informasi, S., & Bayes, N. (2020). Implementasi Naïve Bayes Untuk Memprediksi Waktu Tunggu Alumni Dalam Memperoleh Pekerjaan. 131–134.
[14] Visual Basic .NET - Wikipedia bahasa Indonesia, ensiklopedia bebas. (n.d.). Retrieved March 29, 2020, from https://id.wikipedia.org/wiki/Visual_Basic_.NET
[15] Wirantasa, U. (2017). Pengaruh Kedisiplinan Siswa Terhadap Prestasi Belajar Matematika. Formatif: Jurnal Ilmiah Pendidikan MIPA, 7(1), 83–95. https://doi.org/10.30998/formatif.v7i1.1272
Published
2021-04-18
How to Cite
[1]
S. Rahmatullah, S. Supriyanto, R. Rustam, M. Parida, P. Riswanto, and I. Prastiyo, “IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MENENTUKAN TINGKAT KEDISIPLINAN SISWA”, Jurnal Informasi dan Komputer, vol. 9, no. 1, pp. 32-44, Apr. 2021.

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