Ensemble Bagging Support Vector Machine–Kernel Discriminant Analysis Model for Stunting Potential Classification

Alfiyah Hanun Nasywa, Solimun Solimun, Achmad Efendi, Adji Achmad Rinaldo Fernandes, Celia Sianipar, Fachira Haneinanda Junianto

Abstract


Considering the maternal knowledge, economic status, and maternal nutritional status, the current study created an optimal risk assessment model to detect childhood stunting risk. At the same time, these variables are unbalanced and interrelated in non-linear fashion. Then, to these ends, an Ensemble Bagging model consisting of Support Vector Machine and Kernel Discriminant was trained by voting on the aggregation of the majority of 100 bootstrapped samples, which countered variance and overfitting reducing, hence improving generalization. The primary data were sourced from the mothers of toddlers in the Wajak District. The model predicators were 3 out of the primary ones accounting for the stunting risk. The model also recorded an accuracy of 95%, sensitivity level of 80%, as well as a 100% specificity score. Non-linear relationships were detected and the variance was also reduced, supporting the study to place itself in the realms of novelty by being the first research to fuse the Ensemble Bagging with Kernel methods for Detected stunting risk, The model, hence, fits best as a decision Support System for detecting stunting risk at an early stage.

Keywords


; Ensemble Bagging; Kernel Discriminant Analysis; Stunting; Support Vector Machine

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References


A. Migni, D. Bartolini, G. Marcantonini, A. Tognoloni, M. R. Ceccarini, and F. Galli, “Multivariate Data Analysis Methods and Their Application in Environmental Science,” Mass Spectrom. Rev., 2024, doi: 10.1002/mas.220017.

X. Gao et al., “A Comprehensive Survey on Imbalanced Data Learning,” arXiv Prepr., vol. arXiv:2502, 2025, [Online]. Available: https://arxiv.org/abs/2502.08960

J. Shao, X. Liu, and W. He, “Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification,” Mathematics, vol. 9, no. 9, p. 936, 2021, doi: 10.3390/math9090936.

R. Ran, T. Wang, Z. Li, and et al., “Polynomial linear discriminant analysis,” J. Supercomput., vol. 80, pp. 413–434, 2024, doi: 10.1007/s11227-023-05485-9.

Y. Jin, X. Zhang, and A. J. Molstad, “Kernelized Discriminant Analysis for Joint Modeling of Multivariate Categorical Responses,” J. Comput. Graph. Stat., pp. 1–12, 2025, doi: 10.1080/10618600.2025.2526412.

L. Qu and Y. Pei, “A Comprehensive Review on Discriminant Analysis for Addressing Challenges of Class-Level Limitations, Small Sample Size, and Robustness,” Processes, vol. 12, no. 7, p. 1382, 2024, doi: 10.3390/pr12071382.

P. M. Putri, A. S. Shafira, and G. S. Mahardhika, “Stunting reduction strategy in Indonesia: Maternal knowledge aspects,” Indones. J. Public Heal., vol. 19, no. 2, pp. 329–343, 2024, doi: https://doi.org/10.20473/Ijph.v19i2.2024.329-343.

of H. of the R. of I. Ministry, SSGI 2024: Survey of Indonesia’s Nutritional Status in Numbers. Jakarta: Ministry of Health of the Republic of Indonesia, 2025.

F. Prasetiyowati, N. Fitriyah, A. Setiawan, and A. S. Nugroho, “Comparative Analysis of Machine Learning Algorithms for COVID-19 Case Classification Using Polynomial Kernel SVM,” IEEE, IEEE. doi: 10.1109/ICICoS57375.2023.10132269.

H. Apriyani and K. Kurniati, “Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus,” J. Inf. Technol. Ampera, vol. 1, no. 3, pp. 133–143, 2020, doi: 10.51519/journalita.volume1.isssue3.year2020.page133-143.

A. Araveeporn and A. Kangtunyakarn, “An Enhanced Discriminant Analysis Approach for Multi-Classification with Integrated Machine Learning-Based Missing Data Imputation,” Mathematics, vol. 13, no. 21, p. 3392, 2025, doi: 10.3390/math13213392.

H. A. Shiddiqi, K. E. Setiawan, and R. Fredyan, “Leveraging Support Vector Machines and ensemble learning for early diabetes risk assessment: A comparative study,” J. EMACS (Engineering, Math. Comput. Sci., vol. 7, no. 1, pp. 1–6, 2025, doi: 10.21512/emacsjournal.v7i1.12846.

Solimun and A. A. R. Fernandes, “Ensemble bagging discriminant and logistic regression in classification analysis,” New Math. Nat. Comput., vol. 21, no. 1, pp. 91–111, 2025, doi: 10.1142/S1793005725500061.

W. H. O. (WHO), Nutrition landscape information system (NLiS) country profile indicators: Interpretation guide. World Health Organization, 2020.

Pemenkes, Regulation of the Minister of Health of the Republic of Indonesia Number 2 of 2020 concerning Indonesian Child Anthropometric Standards. Ministry of Health of the Republic of Indonesia, 2020.

A. D. Laksono, N. E. W. Sukoco, T. Rachmawati, and R. D. Wulandari, “Factors Related to Stunting Incidence in Toddlers with Working Mothers in Indonesia,” Int. J. Environ. Res. Public Health, vol. 19, no. 17, p. 10654, 2022, doi: 10.3390/ijerph191710654.

L. H. Y. Arini, Solimun, A. Efendi, and A. A. R. Fernandes, “Ensemble bagging with ordinal logistic regression to classify toddler nutritional status,” BAREKENG J. Math. Its Appl., vol. 19, no. 1, pp. 1–12, 2025, doi: 10.30598/barekengvol19iss1pp1-12.

Y. B. Prasetyo, P. Permatasari, and H. D. Susanti, “The effect of mothers’ nutritional education and knowledge on children’s nutritional status: a systematic review,” Int. J. Child Care Educ. Policy, vol. 17, p. 11, 2023, doi: 10.1186/s40723-023-00114-7.

M. H. A. Syahroni, N. Astuti, V. Indrawati, and R. Ismawati, “Factors that affect the eating habits of preschool-age children (4–6 years) are reviewed from the achievement of balanced nutrition,” J. Culin. Arts, vol. 10, no. 1, pp. 12–22, 2021, doi: https://ejournal.unesa.ac.id/index.php/jurnal-tata-boga/.

K. Patiran, T. Siswati, and E. Yuliati, “Relationship between Maternal Knowledge and Nutritional Status of Children in Teluk Patipi, Fakfak,” JPK J. Prot. Kesehat., vol. 13, no. 1, pp. 72–76, 2024, doi: 10.36929/jpk.v13i1.816.

H. W. Falah and Syafri, “Determination of Economic Growth in Indonesia,” J. Econ. Trisakti, vol. 3, no. 2, pp. 2309–2318, 2023, doi: https://doi.org/10.25105/jet.v3i2.16541.

H. Rohmawati, N. L. M. Puspita, A. Awatiszahro, and A. Nugroho, “The Relationship Between Family Economic Level and the Incidence of Anemia in Pregnant Women,” Str. J. Ilm. Kesehat., vol. 13, no. 1, pp. 31–37, 2024, doi: 10.30994/sjik.v13i1.1107.

I. P. Sari, Y. Ardillah, and A. Rahmiwati, “Berat bayi lahir dan kejadian stunting pada anak usia 6-59 bulan di Kecamatan Seberang Ulu I Palembang,” J. Gizi Indones., vol. 8, no. 2, pp. 110–118, 2020, doi: 10.14710/jgi.8.2.110-118.

R. D. Aisyah, S. Suparni, and F. Fitriyani, “Effect of Counseling Packages on The Diet of Pregnant Women With Chronic Energy Deficiency,” Str. J. Ilm. Kesehat., vol. 9, no. 2, pp. 944–949, 2020, doi: 10.30994/sjik.v9i2.399.

M. W. Talakua and B. P. Tomasouw, “Design of KIP Kuliah selection system and recipient determination using Support Vector Machine (SVM),” BAREKENG J. Math. Its Appl., vol. 17, no. 3, pp. 1803–1814, 2023, doi: 10.30598/barekengvol17iss3pp1803-1814.

A. S. Nugroho, D. Handoko, and A. B. Witarto, Support Vector Machine – Theory and Its Application in Bioinformatics. 2003.

D. Mustafa Abdullah and A. Mohsin Abdulazeez, “Machine Learning Applications based on SVM Classification A Review,” Qubahan Acad. J., vol. 1, no. 2, pp. 81–90, 2021, doi: 10.48161/qaj.v1n2a50.

D. R. Utari, “Application of the support vector machine (SVM) method in classification of hypertension,” BAREKENG J. Math. Its Appl., vol. 17, no. 4, pp. 3523–3532, 2023, doi: https://doi.org/10.30598/barekengvol17iss4pp2263-2272.

L. H. Y. Arini, A. Solimun, A. Efendi, and M. O. Ullah, “CART classification on ordinal scale data with unbalanced proportions using ensemble bagging approach,” JTAM (Jurnal Teor. dan Apl. Mat., vol. 8, no. 2, pp. 441–453, 2024, doi: 10.31764/jtam.v8i2.20201.

L. M. Cendani and A. Wibowo, “Perbandingan Metode Ensemble Learning pada Klasifikasi Penyakit Diabetes,” J. Masy. Inform., vol. 13, no. 1, pp. 33–44, 2022, doi: 10.14710/jmasif.13.1.42912.

R. Qiu, “Func-Bagging: An Ensemble Learning Strategy for Imbalanced Classification,” Appl. Sci., vol. 15, no. 2, p. 905, 2025, doi: https://doi.org/10.3390/app15020905.

Z. Liu, Y. Li, N. Chen, Q. Wang, B. Hooi, and B. He, “Class-Imbalanced Learning on Graphs: A Survey,” ACM Comput. Surv., 2023, doi: 10.1145/3718734.

B. Rao, M. Rashid, M. G. Hasan, and G. Thunga, “Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data,” Int. J. Environ. Res. Public Health, vol. 22, no. 3, p. 449, 2025, doi: 10.3390/ijerph22030449.

R. Aristiyani and M. Mustajab, “The relationship between family economic level and the incidence of stunting in toddlers,” J. Nutr. Public Heal., vol. 12, no. 1, pp. 45–53, 2023.

J. Amann et al., “To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems,” PLOS Digit. Heal., vol. 1, no. 2, p. e0000016, 2022, doi: 10.1371/journal.pdig.0000016.




DOI: http://dx.doi.org/10.30829/zero.v9i3.26818

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