Classification of School Students Lifestyle Risks Based on Smoking Behavior Using Naïve Bayes
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Oktaria Dwi Cahyani
*
Universitas Sriwijaya, Palembang,
Indonesia
- Deltari Balka Universitas Sriwijaya, Palembang, Indonesia
- Dinni Rezky Amelia Universitas Sriwijaya, Palembang, Indonesia
- Rainda Cintari Aulya Universitas Sriwijaya, Palembang, Indonesia
- Ken Ditha Tania Universitas Sriwijaya, Palembang, Indonesia
- Allsela Meiriza Universitas Sriwijaya, Palembang, Indonesia
- Zaqqi Yamani Universitas Sriwijaya, Palembang, Indonesia (*) Corresponding Author
Abstract
This study aims to classify students' lifestyle risks based on smoking behavior using the Naïve Bayes algorithm within a knowledge management framework. The research was conducted on students at a vocational high school within the coverage area of a local community health center. The dataset consisted of 277 valid records after undergoing data selection, cleaning, and transformation stages. The modeling process was carried out using RapidMiner software with an 80:20 data split for training (221 students) and testing (56 students). The evaluation metrics used included accuracy, precision, recall, and confusion matrix. The experimental results demonstrate that the Naïve Bayes model achieved an accuracy of 85.92%, precision of 86.12%, and recall of 92.86% for the unhealthy class. Furthermore, the classification results were integrated into a knowledge management framework to support decision-making processes in schools and community health centers. This study contributes to the application of predictive data mining in adolescent health and demonstrates how classification models can serve as effective tools for early detection, preventive interventions, and evidence-based policy formulation in educational and health settings.
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References
F. Ramadhan, D. Herlambang, A. P. Dipta, U. Bina, and S. Informatika, “Prediction of Health Status Based on Lifestyle Using Decision Tree and Feature Importance," RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 4, pp. 9616–9623, 2026, doi : 10.31004/riggs.v4i4.5246.
B. Artini, Intiyaswati, and M. N. Pandeirot, "Perilaku Merokok pada Remaja di SMK Kota Surabaya," Jurnal Pengabdian Masyarakat, vol. 5, no. 1, pp. 27–32, 2023, doi: 10.47560/pengabmas.v5i1.609.
I. M. D. Maysanjaya, Buku Ajar. Data Mining. Undiksha Press, 2022.
A. F. Riany, G. Testiana, S. S. Informasi, “Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes,” Jurnal Teknologi Informasi, vol. 9, pp. 42–54, 2023, Accessed: 12 March 2026. [Online]. Available: https://jurnal.univpgri-palembang.ac.id/index.php/JurnalTeknologiInformasi.
T. S. Kumar, Introduction to Data Mining 1st ed. Pearson Education, 2006, Accessed: 12 March 2026. [Online]. Available: https://www.amazon.ca/dp/9332571406/
A. Pratama, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Cuaca,” Jurnal Informatika dan Teknologi, vol. 8, no. 2, pp. 1637–1642, 2024, doi: 10.36040/jati.v8i2.8967.
F. Sirait et al., “Penerapan Naive Bayes untuk Identifikasi Keterlambatan Perkembangan Anak Berdasarkan Data Kesehatan pada Program Studi Kebidanan” Jurnal Media Informatika (JUMIN), vol. 6, no. 2, pp. 739–745, 2024, doi :10.62027/sevaka.v2i4.525.
M. Samuel. Idmi, and Triyono, “Analisis perbandingan model naïve bayes dan c4.5 untuk prediksi stroke berdasarkan riwayat data medis dengan pendekatan matriks korelasi,” Jurnal Ilmiah Informatika, vol. 10, no. 4, pp. 3749–3759, 2025, doi:10.29100/jipi.v10i4.8653.
A. Muttakin, Rusmana, and Ramadhani, "Komparasi Algoritma Decision Tree, Random Forest, SVM, dan KNN untuk Prediksi Penyakit Jantung," Jurnal Informatika, vol. 1, no. 2, pp. 35 42, 2025, doi: 10.15294/eduel.v13i1.22163.
Y. D. Amritha, N. Luh, P. Ika, and W. P. Dananjaya, “Model Machine Learning yang Dioptimalkan untuk Prediksi Penyakit Jantung Menggunakan R Shiny,” Jurnal Komputer dan Sains Terapan, vol. 8, no. 01, pp. 1–10, 2026, doi: 10.53863/kst.v8i01.1994
A. Wantoro et al., “Analisis Komparatif Strategi Penanganan Imbalanced Data pada Klasifikasi Penyakit Diabetes Menggunakan Data Mining," Jurnal Simpul Inovasi, no. 2, 2025, doi: 10.20884/1.jsi.2025.2.1.16198
M. Husaini, Priyanto, and Martono, "Analisis Sentimen Kinerja Tenaga Medis Indonesia Menggunakan Modeling RoBERTa dan Metode Machine Learning," Jurnal Edukasi Elektro, vol. 13, no. 1, pp. 1–8, 2026. accessed: 13 march 2026. [Online]. Available: https://journal.unnes.ac.id/journals/eduel/index.
F. Itsnani et al., “Klasifikasi Risiko Kesehatan Berbasis Data Perilaku Remaja,” Jurnal Riset Teknik Komputer, vol. 2, no. 4, pp. 55–63, 2025, doi : 10.69714/q0zapc82.
P. Widodo, “Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data pada Pasien Tuberkulosis Berbasis Data Mining,” Jurnal Online Gita Berbasis Teknologi dan Cara, vol. 5, no. 1, pp. 75–81, 2025, doi: 10.47065/jogtc.v5i1.8999.
W. Fadri, “Klasifikasi Penyakit Hati dengan Menggunakan Metode Naive Bayes,” Jurnal Informasi dan Teknologi, vol. 5, no. 1, pp. 32–36, 2023, doi: 10.37034/jidt.v5i1.230.
F. A. Sumantri and Y. H. Chrisnanto, “Prediksi Risiko Kesehatan Mental Mahasiswa Menggunakan Klasifikasi Naive Bayes,” Jurnal Ilmiah Komputasi, vol. 12, no. 3, pp. 383–393, 2025, doi: 10.30865/jurikom.v12i3.8648.
I. T. Monowati and R. Setyadi, “Penerapan Algoritma Naïve Bayes Dalam Memprediksi Pengusulan Penghapusan Peralatan dan Mesin Kantor,” Journal of Software Engineering, Information and Communication Technology, vol. 4, no. 2, pp. 483–491, 2023, doi: 10.47065/josh.v4i2.2674.
P. Rahmawati and A. Larasati, “Pengembangan Model Persetujuan Kredit Nasabah Bank Dengan Algoritma Klasifikasi Naïve Bayes , Decision Tree , Dan Artificial Neural Network,” Jurnal Sistem Informasi, vol. 17, no. 1, pp. 1–12, 2022, doi: 10.14710/jati.1.1.1-12.
D. Florencia, “Prediksi Jenis Kesehatan Kejiwaan Berdasarkan Usia Menggunakan Metode Naïve Bayes Berbasis Website,” Seminar Nasional Teknologi Informasi, vol. 8, pp. 15030–15040, 2024, Accessed: 12 March 2026. [Online]. Available: https://paperity.org/p/358147184. v
D. R. Andriyani, M. Afdal, and S. Monalisa, “Analisis Sentimen Masyarakat Terhadap Penghapusan Honorer Berdasarkan Opini Dari Twitter Menggunakan Naïve Bayes Classifier,” Building of Informatics, Technology and Science (BITS), vol. 5, no. 1, pp. 49–58, 2023, doi: 10.47065/bits.v5i1.3541.
J. P. Tanjung, F. C. Tampubolon, A. W. Panggabean, and M. Anjas, “Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection,” Jurnal dan Penelitian Teknik Informatika, vol. 7, no. 1, pp. 584–589, 2023, doi: 10.33395/sinkron.v8i1.12182.
S. T. Utami, S. Lestari, and H. W. Nugroho, “Prediction Of Anemia Using The Particle Swarm Optimization ( PSO ) And Naïve Bayes Algorithm,” Computer Engineering and Informatics Journal, vol. 3321, no. X, pp. 1–8, 2024, doi: 10.24014/coreit.v10i1.28428.
S. Andriyanto and M. S. Hasibuan, “Application of Nave Bayes Algorithm for SMS Spam Classification Using Orange,” International Journal of Artificial Intelligence and Software Computing Applications, vol. 1, 2022, doi: 10.47679/ijasca.v1i1.3.
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Copyright (c) 2026 Oktaria Dwi Cahyani, Deltari Balka, Dinni Rezky Amelia, Rainda Cintari Aulya, Ken Ditha Tania, Allsela Meiriza, Zaqqi Yamani
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