Comparative Analysis of SMOTE, WMOTE, and ADASYN Oversampling Methods on Multinomial Naive Bayes Performance in Classifying Toddlers Nutritional Status

Authors

  • Dewi Sartika Universitas Sriwijaya https://orcid.org/0000-0001-6659-3572
  • Naretha Kawadha Pasemah Gumay Universitas Sriwijaya
  • Rendra Gustriansyah Universitas Indo Global Mandiri
  • Yesinta Florensia Universitas Sriwijaya
  • Miftahul Falah Universitas Sriwijaya

DOI:

https://doi.org/10.26905/jtmi.v12i1.16438

Keywords:

ADASYN, SMOTE, WMOTE, Multinomial Naive Bayes

Abstract

Class imbalance in toddler nutritional status data often reduces the ability of classification models, especially in predicting minority classes. This study aims to analyze the impact of three oversampling techniques, namely SMOTE, WMOTE, and ADASYN, on improving the performance of the Multinomial Naive Bayes (MNB) algorithm. A dataset of 243 data was processed through a preprocessing stage by converting categorical variables using numeric labels. To meet the MNB algorithm's requirement for non-negative data, continuous numeric features (such as birth weight, birth height, weight, height, and age) were normalized using the Min-Max Scaler to the range [0, 1]. This process discretizes continuous values onto a probability scale to ensure feature compatibility with the Multinomial distribution. Data balancing was performed only on the training dataset, where the SMOTE method produced 374 data, ADASYN produced 375 data, and WMOTE produced 373 data. The evaluation results show that although all three oversampling methods experienced a slight decrease in global accuracy, the model's ability to detect minority classes improved, as evidenced by increases in G-Means and Balanced Accuracy. The test results concluded that MNB-ADASYN was the best model for prioritizing high sensitivity to all class labels, while MNB-WMOTE provided the most consistent global accuracy stability while maintaining performance on minority classes.

Downloads

Download data is not yet available.

References

[1] K. Rahmadhita, “Permasalahan Stunting dan Pencegahannya Stunting Problems and Prevention,” Jurnal ilmiah Kesehatan Sandi Husada, vol. 11, no. 1, pp. 225–229, 2020, doi: https://doi.org/10.35816/jiskh.v11i1.253

[2] C. R. Titaley, I. Ariawan, D. Hapsari, A. Muasyaroh, and M. J. Dibley, “Determinants of the stunting of children under two years old in Indonesia: A multilevel analysis of the 2013 Indonesia basic health survey,” Nutrients, vol. 11, no. 5, pp. 1–13, May 2019, doi: https://doi.org/10.3390/nu11051106

[3] Kementerian Kesehatan Republik Indonesia. (2023). Stunting di Indonesia dan Determinannya. [Online]. Available: repository.badankebijakan.kemkes.go.id factsheet PDF. [Accessed: Aug. 7, 2026].

[4] E. R. Arumi, S. A. Subrata, and A. Rahmawati, “Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City,” Jurnal RESTI, vol. 7, no. 2, pp. 201–207, Apr. 2023, doi: https://doi.org/10.29207/resti.v7i2.4438

[5] D. A. Kristiyanti, A. H. Umam, M. Wahyudi, R. Amin, and L. Marlinda, “Comparison of SVM Naïve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, Institute of Electrical and Electronics Engineers Inc., Mar. 2018. doi: https://doi.org/10.1109/CITSM.2018.8674352

[6] E. Apriliyani and Y. Salim, “Analisis performa metode klasifikasi Naïve Bayes Classifier pada Unbalanced Dataset,” Indonesian Journal of Data and Science (IJODAS), vol. 3, no. 2, pp. 47–54, 2022, doi: https://doi.org/10.56705/ijodas.v3i2.45

[7] D. Sartika, Y. Florensia, and M. Utari, “The Effect of the SMOTE Method on the Classification of Toddler Nutritional Status Using the Naive Bayes Method,” SISFOKOM Jurnal (Computer and Information System), vol. 14, no. 3, pp. 324–329, 2025, doi: https://doi.org/10.32736/sisfokom.v14i3.2381

[8] A. Surya Firmansyah, A. Aziz, and M. Ahsan, “Optimasi K-Nearest Neighbor Menggunakan Algoritma SMOTE Untuk Mengatasi Imbalance Class Pada Klasifikasi Analisis Sentimen,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 6, pp. 3341–3347, 2023, doi: https://doi.org/10.36040/jati.v7i6.7257

[9] R. Siringoringo, “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE Dan K-Nearest Neighbor,” Jurnal ISD, vol. 3, no. 1, pp. 2528–5114, 2018.

[10] S. Barua, M. M. Islam, X. Yao, and K. Murase, “MWMOTE-Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 2, pp. 405–425, Feb. 2014, doi: https://doi.org/10.1109/TKDE.2012.232

[11] M. A. N. M. Yusuf and M. C. Untoro, “Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset,” IT Journal Research and Development (ITJRD), vol. 8, no. 1, pp. 1–13, Aug. 2023, doi: https://doi.org/10.25299/itjrd.2023.12412

[12] J. Wei, H. Huang, L. Yao, Y. Hu, Q. Fan, and D. Huang, “NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems,” Expert Syst. Appl., vol. 158, Nov. 2020, doi: https://doi.org/10.1016/j.eswa.2020.113504

[13] H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in Proceedings of the International Joint Conference on Neural Networks, 2008, pp. 1322–1328. doi: https://doi.org/10.1109/IJCNN.2008.4633969

[14] I. Witten, E. Frank, and F. Azuaje, “Data Mining: Practical Machine Learning Tools and Techniques 2nd edition,” Biomed. Eng. Online, vol. 5, no. 1, Dec. 2006, doi: https://doi.org/10.1186/1475-925X-5-51

[15] I. Tougui, A. Jilbab, and J. El Mhamdi, “Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications,” Healthc. Inform. Res., vol. 27, no. 3, pp. 189–199, Jul. 2021, doi: https://doi.org/10.4258/hir.2021.27.3.189

[16] I. K. Nti, O. Nyarko-Boateng, and J. Aning, “Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation,” International Journal of Information Technology and Computer Science, vol. 13, no. 6, pp. 61–71, Dec. 2021, doi: https://doi.org/10.5815/ijitcs.2021.06.05

[17] F. Ramadhani, A. Satria, and I. P. Sari, “Implementasi Metode Fuzzy K-Nearest Neighbor dalam Klasifikasi Penyakit Demam Berdarah,” Hello World Jurnal Ilmu Komputer, vol. 2, no. 2, pp. 58–62, May 2023, doi: https://doi.org/10.56211/helloworld.v2i2.253

[18] A. Prawita Ningrum, S. Winarno, and V. Praskatama, “Klasifikasi Kualitas Biji Kedelai Menggunakan Transfer Learning Convolutional Neural Network dan SMOTE,” Journal of Applied Computer Science and Technology, vol. 5, no. 2, pp. 155–164, Dec. 2024, doi: https://doi.org/10.52158/jacost.v5i2.1002

[19] T. T. Wong and H. C. Tsai, “Multinomial Naive Bayesian classifier with generalized Dirichlet priors for high-dimensional imbalanced data,” Knowl. Based. Syst., vol. 228, pp. 1–8, Sep. 2021, doi: https://doi.org/10.1016/j.knosys.2021.107288

[20] A. Vabalas, E. Gowen, E. Poliakoff, and A. J. Casson, “Machine learning algorithm validation with a limited sample size,” PLoS One, vol. 14, no. 11, Nov. 2019, doi: https://doi.org/10.1371/journal.pone.0224365

[21] C. Muhamad Sidik Ramdani, A. Rachman Nur, and R. Setiawan, “Comparison of the Multinomial Naive Bayes Algorithm and Decision Tree with the Application of AdaBoost in Sentiment Analysis Reviews PeduliLindungi Application,” International Journal of Information System & Technology, vol. 6, no. 4, pp. 419–430, 2022, doi: https://doi.org/10.30645/ijistech.v6i4.257

[22] W. Irmayani, “Visualisasi Data pada Data Mining Menggunakan Metode Klasifikasi Naive Bayes,” Jurnal Khatulistiwa Informatika, vol. 9, no. 1, pp. 68–72, 2021, doi: https://doi.org/10.31294/jki.v9i1.9593

[23] Y. Pristyanto and A. A. Zein, “Model Balanced Bagging Berbasis Decision Tree Pada Dataset Imbalanced Class,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 1, pp. 9–15, Mar. 2023, doi: https://doi.org/10.32736/sisfokom.v12i1.1399

[24] M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3151048

[25] M. Grandini, E. Bagli, and G. Visani, “Metrics for Multi-Class Classification: an Overview,” A White Paper, pp. 1–17, Aug. 2020, [Online]. Available: http://arxiv.org/abs/2008.05756

[26] M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, Jul. 2009, doi: https://doi.org/10.1016/j.ipm.2009.03.002

Downloads

Published

30-06-2026

How to Cite

Sartika, D., Naretha Kawadha Pasemah Gumay, Gustriansyah , R., Florensia, Y., & Falah, M. (2026). Comparative Analysis of SMOTE, WMOTE, and ADASYN Oversampling Methods on Multinomial Naive Bayes Performance in Classifying Toddlers Nutritional Status. Jurnal Teknologi Dan Manajemen Informatika, 12(1), 10–20. https://doi.org/10.26905/jtmi.v12i1.16438

Issue

Vol. 12 No. 1 (2026): Juni 2026

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

(1) Copyright of the published articles will be transferred to the journal as the publisher of the manuscripts. Therefore, the author confirms that the copyright has been managed by the journal.

(2) Publisher of JTMI: Jurnal Teknologi dan Manajemen Informatika is University of Merdeka Malang.

(3) The copyright follows Creative Commons Attribution–ShareAlike License (CC BY SA): This license allows to Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material, for any purpose, even commercially.