Machine Learning Approaches for Household Smoking Classification in West Java : A Comparison between Encoded Logistic Regression and XGBoost
Keywords:
Classification, Encoded Logistic Regression, Machine Learning, Weight of Evidence, XGBoost
Abstract
Smoking behavior within households is a significant public health issue, particularly in densely populated regions such as West Java. This study aims to classify household smoking status using two modeling approaches, namely Logistic Regression and XGBoost, under several data-handling schemes including baseline models, One-Hot Encoding, and Weight of Evidence (WoE). Model performance is evaluated using accuracy, sensitivity, specificity, and Area Under the Curve (AUC) metrics. The evaluation results show that XGBoost with WoE Encoding provides the best performance, achieving an accuracy of 0.739 and sensitivity of 0.782, making it more effective in detecting smoking households compared to other methods. Feature importance analysis identifies the Food–Non-Food Expenditure Ratio (X1) as the most influential predictor. The dominance of this expenditure ratio confirms that household expenditure structure, as an indicator of economic condition and welfare, plays a crucial role in distinguishing smoking households. These findings support the hypothesis that poorer households tend to have a higher probability of smoking.
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[2] Kemenkes, "Perokok Aktif di Indonesia Tembus 70 Juta Orang, Mayoritas Anak Muda," 29 May 2024. [Online]. Available: https://kemkes.go.id/id/perokok-aktif-di-indonesia-tembus-70-juta-orang-mayoritas-anak-muda.
[3] A. Asyary and M. Veruswati, "Smoking Behavior and Cigarette Expenditure in a Household: Evidence for Smoke‑Free Houses Initiation in Indonesia," International Journal of Preventive Medicine, p. 1, 2023
[4] N. D. Rahmawati, A. Ahsan and H. Andriani, "78 Persen Rumah Tangga Indonesia Teracuni Asap Rokok dari Perokok Aktif, Berdampak pada Kesehatan Bayi," 18 November 2021. [Online]. Available: https://scholar.ui.ac.id/en/clippings/78-persen-rumah-tangga-indonesia-teracuni-asap-rokok-dari-perokok-2/
[5] BPS, "Persentase Penduduk Berumur 15 Tahun ke Atas yang Merokok Tembakau selama Sebulan Terakhir Menurut Provinsi (Persen), 2024," 2 December 2024. [Online]. Available: Persentase Penduduk Berumur 15 Tahun ke Atas yang Merokok Tembakau selama Sebulan Terakhir Menurut Provinsi - Tabel Statistik - Badan Pusat Statistik.
[6] BPS, "Rilis Kemiskinan dan Ketimpangan," January 1 2025. [Online]. Available: https://jabar.bps.go.id/id/news/2025/01/15/729/rilis-kemiskinan-dan-ketimpangan.html
[7] E. R. Amrullah, H. Mutmainah, S. Yuniarti, I. Hidayah and A. Rusyiana, "Konsumsi Tembakau dan Implikasinya terhadap Pengeluaran Pangan Rumah Tangga: Pendekatan Fraksional Logit," BMJ Jurnal, vol. 4, p. 44, 2022
[8] V. A. B. A. Syahbana, Y. H. Darundiati and N. A. Y. Dewanti, "Paparan Asap Rokok sebagai Faktor Risiko Kejadian Berat Bayi Lahir Rendah (BBLR) : Kajian Literatur Sistematik," Jurnal Kesehatan Masyarakat , vol. 10, p. 312, 2022.
[9] N. Siddiqi, Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons, 2017, ISBN: 978-1-119-27915-0.
[10] R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proc. 14th Int. Joint Conf. Artif. Intell. (IJCAI), 1995, pp. 1137–1145.
[11] B. Santoso, H. Wijayanto, K. A. Notodiputro, and B. Sartono, “Synthetic over-sampling methods for handling class imbalanced problems: A review,” IOP Conference Series: Earth and Environmental Science, vol. 58, no. 1, Art. no. 012031, 2017, doi: 10.1088/1755-1315/58/1/012031.
[12] D. W. Hosmer, S. Lemeshow, and R. Sturdivant, Applied Logistic Regression. Hoboken, NJ: Wiley, 2013.
[13] N. Siddiqi, Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards. Hoboken, NJ: Wiley, 2017.
[14] G. Zeng, “On the analytical properties of category encodings in logistic regression,” Commun. Stat. Theory Methods, vol. 52, no. 6, pp. 1870–1887, 2023, doi: 10.1080/03610926.2021.1939382.
[15] S. Lessmann, B. Baesens, H. V. Seow, and L. C. Thomas, “Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research,” Eur. J. Oper. Res., vol. 247, no. 1, pp. 124–136, 2015.
[16] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794, doi: 10.1145/2939672.2939785.
[17] L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: unbiased boosting with categorical features,” arXiv preprint arXiv:1706.09516, 2019, doi: 10.48550/arXiv.1706.09516.
[18] Badan Pusat Statistik, Survei Sosial Ekonomi Nasional (SUSENAS) 2024: Keterangan Konsumsi/Pengeluaran Makanan & Bukan Makanan, dan Pendapatan/Penerimaan Rumah Tangga. Jakarta, Indonesia: BPS, 2024.
[19] A. Swarnata, F. Z. Kamilah, G. Melinda, and V. Adrison, “The impoverishing effect of tobacco use in Indonesia,” Nicotine & Tobacco Research, vol. 26, no. 10, pp. 1331–1338, 2024, doi: 10.1093/ntr/ntae088.
[20] G. Heydari et al., “Smoking and diet in healthy adults: a cross-sectional study in Tehran, Iran, 2010,” Iranian Journal of Public Health, vol. 43, no. 4, pp. 485–491, 2014.
[21] R. I. Mardhiyyah, Pemodelan Faktor Sosial Ekonomi dan Demografi terhadap Status Merokok Penduduk Jawa Barat Menggunakan Regresi Logistik Biner, Master’s thesis, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, 2023.
[22] S. R. Nadhiroh et al., “Socioeconomic characteristics, paternal smoking and secondhand tobacco smoke exposure among infants in Jakarta, Indonesia,” Tobacco Induced Diseases, vol. 18, p. 38, May 2020.
[23] R. Amelia and A. Mu’nim, “Regresi Logistik Biner dengan Proses Resampling dalam Menduga Faktor Determinan Merokok Remaja,” in Seminar Nasional Official Statistics, 2022.
[24] A. Katainen, “Smoking and workers’ autonomy: a qualitative study on smoking practices in manual work,” Health, vol. 16, no. 2, pp. 134–150, 2012, doi: 10.1177/1363459311403944.
[25] N. Kusumawardani, I. Tarigan, Suparmi, and A. Schlotheuber, “Socioeconomic, demographic and geographic correlates of cigarette smoking among Indonesian adults,” Journal of Preventive Medicine and Public Health, 2018.
Published
2026-09-11
Section
Articles
Copyright (c) 2026 Jasmin Nur Hanifa, Boy Riansyah, Aulia Rizki Firdawanti, Bagus Sartono, Wildatul Maulidiyah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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