Classification of Internet Addiction Levels in Students Using the Naïve Bayes Algorithm

Authors

  • Fakhriyah Zulfah Parinduri STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Rafika Dewi STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Susiani Susiani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i3.965

Keywords:

Data Mining, Classification, Naive Bayes, Internet Addiction, Student

Abstract

The presence of the internet on students has a big influence on science and technology which makes the internet as an additional insight to find the information needed, apart from being a source of information, students also access the internet as a means of entertainment. So that it makes students last longer in front of gadgets or computers continuously. The purpose of this study is to determine whether students are indicated by internet addiction and provide input to STIKOM Tunas Bangsa to make policies that use the internet as a learning process so that internet addiction does not occur excessively. Because it is very influential in the learning process to add insight about science and technology to students. The subjects carried out by this study were students who were studying at STIKOM Tunas Bangsa. Therefore, the research was conducted using the Naïve Bayes algorithm classification, in which the data was obtained using a questionnaire distributed to students. The subjects carried out by this study were students who were studying at STIKOM Tunas Bangsa. Therefore, the research was conducted using the Naïve Bayes algorithm classification, in which the data was obtained using a questionnaire distributed to students. It is hoped that this research can be information for students to be able to maintain self-control in utilizing various entertainments on the internet.

References

N. Fakhri and A. Ridfah, “Shyness Dan Kecanduan Internet Pada Mahasiswa,” Jurnal Psikologi Talenta Mahasiswa, vol. 1, no. 1, pp. 68–78, 2021.

O. R. Wilhelmus, “Kampus Merdeka Untuk Melahirkan Manusia Unggul Dalam Semangat Gotong Royong,” JPAK: Jurnal Pendidikan Agama Katolik, vol. 20, no. 2, pp. 48–63, 2020.

C. Juditha, “Pemanfaatan Teknologi Informasi Komunikasi Terhadap Perubahan Sosial Masyarakat Desa,” Jurnal Penelitian Komunikasi dan Opini Publik, vol. 24, no. 1, pp. 16–30, 2020.

V. V. Sianipar, A. Wanto, and M. Safii, “Decision Support System for Determination of Village Fund Allocation Using AHP Method,” The IJICS (International Journal of Informatics and Computer Science) ISSN, vol. 4, no. 1, pp. 20–28, 2020.

R. Simarmata, R. W. Sembiring, R. Dewi, A. Wanto, and E. Desiana, “Penentuan Masyarakat Penerima Bantuan Perbaikan Rumah di Kecamatan Siantar Barat Menggunakan Metode ELECTRE,” Journal of Computer System and Informatics (JoSYC), vol. 1, no. 2, pp. 68–75, 2020.

R. Watrianthos, W. A. Ritonga, A. Rengganis, A. Wanto, and M. Isa Indrawan, “Implementation of PROMETHEE-GAIA Method for Lecturer Performance Evaluation,” Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012067, 2021.

S. R. Ningsih, D. Hartama, A. Wanto, I. Parlina, and Solikhun, “Penerapan Sistem Pendukung Keputusan Pada Pemilihan Objek Wisata di Simalungun,” in Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), 2019, pp. 731–735.

N. Nasution, G. W. Bhawika, A. Wanto, N. L. W. S. R. Ginantra, and T. Afriliansyah, “Smart City Recommendations Using the TOPSIS Method,” IOP Conference Series: Materials Science and Engineering, vol. 846, no. 1, pp. 1–6, 2020.

R. A. Hutasoit, S. Solikhun, and A. Wanto, “Analisa Pemilihan Barista dengan Menggunakan Metode TOPSIS (Studi Kasus: Mo Coffee),” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 2, no. 1, pp. 256–262, 2018.

I. M. Muhamad, S. A. Wardana, A. Wanto, and A. P. Windarto, “Algoritma Machine Learning untuk penentuan Model Prediksi Produksi Telur Ayam Petelur di Sumatera,” Journal of Informatics, Electrical and Electronics Engineering, vol. 1, no. 4, pp. 126–134, 2022.

M. Mahendra, R. C. Telaumbanua, A. Wanto, and A. P. Windarto, “Akurasi Prediksi Ekspor Tanaman Obat , Aromatik dan Rempah-Rempah Menggunakan Machine Learning,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 6, pp. 207–215, 2022.

R. Puspadini, A. Wanto, and N. Arminarahmah, “Penerapan ML dengan Teknik Bayesian Regulation untuk Peramalan,” Journal of Computer System and Informatics (JoSYC), vol. 3, no. 3, pp. 147–155, 2022.

N. L. W. S. R. Ginantra, A. D. GS, S. Andini, and A. Wanto, “Pemanfaatan Algoritma Fletcher-Reeves untuk Penentuan Model Prediksi Harga Nilai Ekspor Menurut Golongan SITC,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, pp. 679–685, 2022.

N. Arminarahmah, S. D. Rizki, O. A. Putra, U. Islam, K. Muhammad, and A. Al, “Performance Analysis and Model Determination for Forecasting Aluminum Imports Using the Powell-Beale Algorithm,” IJISTECH (International Journal of Information System & Technology), vol. 5, no. 5, pp. 624–632, 2022.

N. L. W. S. R. Ginantra et al., “Performance One-step secant Training Method for Forecasting Cases,” Journal of Physics: Conference Series, vol. 1933, no. 1, pp. 1–8, 2021.

A. Perdana, S. Defit, and A. Wanto, “Optimalisasi Parameter dengan Cross Validation dan Neural Back-propagation Pada Model Prediksi Pertumbuhan Industri Mikro dan Kecil,” Jurnal Sistem Informasi Bisnis, vol. 01, no. 11, pp. 34–42, 2021.

N. L. W. S. R. Ginantra, M. A. Hanafiah, A. Wanto, R. Winanjaya, and H. Okprana, “Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012022, 2021.

A. Wanto, S. Defit, and A. P. Windarto, “Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana,” RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 254–264, 2021.

V. V. Utari, A. Wanto, I. Gunawan, and Z. M. Nasution, “Prediksi Hasil Produksi Kelapa Sawit PTPN IV Bahjambi Menggunakan Algoritma Backpropagation,” Journal of Computer System and Informatics (JoSYC, vol. 2, no. 3, pp. 271–279, 2021.

N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, “Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012018, 2021.

J. Hutagalung, N. L. W. S. R. Ginantra, G. W. Bhawika, W. G. S. Parwita, A. Wanto, and P. D. Panjaitan, “COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm,” Journal of Physics: Conference Series, vol. 1783, no. 1, p. 012027, 2021.

N. A. Febriyati, A. D. GS, and A. Wanto, “GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020.

M. A. Hanafiah and A. Wanto, “Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 315–322, 2020.

T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, and Z. M. Nasution, “Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 9–20, 2021.

A. Wanto et al., Data Mining : Algoritma dan Implementasi. Yayasan Kita Menulis, 2020.

W. T. C. Gultom, A. Wanto, I. Gunawan, M. R. Lubis, and I. O. Kirana, “Application ofThe Levenberg Marquardt Method In Predict The Amount of Criminality in Pematangsiantar City,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 21–29, 2021.

S. Wulandari, W. Saputra, S. Tunas Bangsa Pematangsiantar, and A. A. Tunas Bangsa Pematangsiantar JlJenderal Sudirman Blok No, “Prosiding Seminar Nasional Riset Information Science (SENARIS) Penerapan Metode Naive Bayes dalam Menentukan Pengaruh Penasihat Akademik pada Kelulusan Mahasiswa Tingkat Akhir,” no. September, pp. 661–669, 2019.

E. Manalu, F. A. Sianturi, and M. R. Manalu, “Penerapan Algoritma Naive Bayes Untuk Memprediksi Jumlah Produksi Barang Berdasarkan Data Persediaan dan Jumlah Pemesanan Pada CV. Papadan Mama Pastries,” Jurnal Mantik Penusa, vol. 1, no. 2, pp. 16–21, 2017.

F. S. A. Z. Farhannah and S. Solikhun, “Penerapan Algoritma Naive Bayes Dalam Menentukan Konsentrasi Siswa Terhadap Proses Belajar Mengajar Di Smp Taman Asuhan,” Jurnal RESISTOR (Rekayasa Sistem Komputer), vol. 4, no. 2, pp. 142–155, 2021.

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Published

2022-10-18

How to Cite

Parinduri, F. Z., Dewi, R., & Susiani, S. (2022). Classification of Internet Addiction Levels in Students Using the Naïve Bayes Algorithm. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(3), 257–264. https://doi.org/10.55123/jomlai.v1i3.965

Issue

Vol. 1 No. 3 (2022): September

Section

Articles

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Copyright (c) 2022 Fakhriyah Zulfah Parinduri, Rafika Dewi, Susiani Susiani

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