Performance Optimization of Support Vector Machine with SMOTE for Multiclass Stunting Prediction in Sumedang District, Indonesia

Authors

  • Irfan Fadil Informatics, Universitas Sebelas April, Indonesia
  • Ramdani Surya Manggala Informatics, Universitas Sebelas April, Indonesia
  • Esa Firmansyah Information and Communication Technology, School of Science and Technology Asia e University, Subang Jaya, Malaysia
  • Muhammad Agreindra Helmiawan Information and Communication Technology, School of Science and Technology Asia e University, Subang Jaya, Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.4.4843

Keywords:

Classifying, Confusion Matrix, RapidMiner, Stunting Treatment

Abstract

The percentage of stunting toddlers in Sumedang Regency is the highest compared to other nutritional problems. Stunting imposes a significant risk to the future quality of human resources. This study explores the performance of the Support Vector Machine (SVM) algorithm in predicting the stunting status of toddlers in Tanjungmedar Subdistrict, the region with the highest incidence of stunting cases in Sumedang Regency in 2020. The testing uses RapidMiner software and applies the Synthetic Minority Oversampling Technique (SMOTE) to overcome the imbalanced dataset so that the resulting performance can be optimized. Accuracy, precision, recall, and F1-score are measured in performance evaluation using a confusion matrix. The findings demonstrate that SMOTE might adjust the distribution of target classes in the dataset to maximize the SVM algorithm's performance. At the start of the test, the SVM model produced an accuracy of 85.10%. After applying SMOTE, the accuracy of the SVM model increased to 89.08%. The F1-score also increased for each class, except for the Normal class, which decreased slightly. These results demonstrate the suitability of SVM combined with SMOTE for health-related multiclass classification tasks, especially in imbalanced public health datasets, contributing to the advancement of applied machine learning in healthcare informatics.

Downloads

Download data is not yet available.

References

N. N. Fadilah, K. Dianta, and A. Pratama, “Impact of Covid-19 on Indonesia’s Education System,” in International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 2023, pp. 724–731. Accessed: Sept. 22, 2025. [Online]. Available: http://103.8.12.212:33180/unj/index.php/isc-beam/article/view/42678

S. Susilawati, R. Falefi, and A. Purwoko, “Impact of COVID-19’s Pandemic on the Economy of Indonesia,” Bp. Int. Res. Crit. Inst.-J. BIRCI-J., vol. 3, no. 2, pp. 1147–1156, 2020.

Z. Abidin and T. Tobibatussa’adah, “The impact of covid-19 pandemic on education and judicial practice in indonesia,” Riayah J. Sos. Dan Keagamaan, vol. 5, no. 02, pp. 122–130, 2020.

L. Octavia and R. Rachmalina, “Child malnutrition during the COVID-19 pandemic in Indonesia,” Pediatr. Gastroenterol. Hepatol. Nutr., vol. 25, no. 4, p. 347, 2022.

A. Y. Perdana, R. Latuconsina, and A. Dinimaharawati, “Prediksi Stunting Pada Balita Dengan Algoritma Random Forest,” EProceedings Eng., vol. 8, no. 5, 2021.

I. W. Pranata et al., “Prevention of stunting through improving maternal parenting and early detection of pregnancy risk factors,” J. Pengabdi. Masy. Bestari, vol. 1, no. 9, pp. 1025–1034, 2022.

L. H. Kusumawardani, U. Rachmawati, M. Jauhar, and I. G. A. P. D. Rohana, “Community-based stunting intervention strategies: Literature review,” Dunia Keperawatan J. Keperawatan Dan Kesehat., vol. 8, no. 2, pp. 259–268, 2020.

R. Setiyabudi, “Stunting, risk factor, effect and prevention,” MEDISAINS J. Ilm. Ilmu-Ilmu Kesehat., vol. 17, no. 2, pp. 24–25, 2019.

S. Widari, N. Bachtiar, and E. Primayesa, “Faktor Penentu Stunting: Analisis Komparasi Masa Millenium Development Goals (MDGs) dan Sustainable Development Goals (SDGs) di Indonesia,” J. Ilm. Univ. Batanghari Jambi, vol. 21, no. 3, pp. 1338–1346, 2021.

J. R. Khan, J. H. Tomal, and E. Raheem, “Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh,” Inform. Health Soc. Care, vol. 46, no. 4, pp. 425–442, Dec. 2021, doi: 10.1080/17538157.2021.1904938.

M. S. Haris, A. N. Khudori, and W. T. Kusuma, “Perbandingan metode supervised machine learning untuk prediksi prevalensi stunting di Provinsi Jawa Timur,” J. Teknol. Inf. Dan Ilmu Komput. JTIIK, vol. 9, no. 7, pp. 1571–1576, 2022.

M. S. Haris, M. Anshori, and A. N. Khudori, “Prediction of stunting prevalence in east java province with random forest algorithm,” J. Tek. Inform. Jutif, vol. 4, no. 1, pp. 11–13, 2023.

W. A. Amelia, “Implementasi Jaringan Syaraf Tiruan Sebagai Sistem Diagnosis Stunting Balita Menggunakan Metode Backpropagation Di Puskesmas Brebes,” J. Minfo Polgan, vol. 12, no. 2, pp. 1874–1883, 2023.

I. K. A. Wiraguna, E. Setyati, and E. Pramana, “Prediksi Anak Stunting Berdasarkan Kondisi Orang Tua Dengan Metode Support Vector Machine Dengan Study Kasus Di Kabupaten Tabanan-Bali,” SMATIKA J. STIKI Inform. J., vol. 12, no. 01, pp. 47–54, 2022.

M. Amirudin and A. D. Wowor, “Analisis Perbandingan Klasifikasi Balita Beresiko Stunting Menggunakan Metode Support Vector Machine dan Decission Tree,” in Proceedings of the National Conference on Electrical Engineering, Informatics, Industrial Technology, and Creative Media, 2023, pp. 581–591. Accessed: Sept. 22, 2025. [Online]. Available: https://conferences.ittelkom-pwt.ac.id/index.php/centive/article/download/217/146

A. Y. Labolo, S. Mooduto, A. Bode, and I. C. R. Drajana, “Penerapan Algoritma Spport Vector Machine dan K-Nearest Neighbor Menggunkan Feature Selection Backward Elimination Untuk Prediksi Status Penderita Stunting Pada Balita,” J. Tecnoscienza, vol. 6, no. 2, pp. 374–388, 2022.

H. G. Pangestu, R. Y. Sinaga, F. Z. Ulya, U. Athiyah, A. W. Muhammad, and F. Alameka, “Analisis Efisiensi Metode K-Nearest Neighbor dan Forward Chaining Untuk Prediksi Stunting Pada Balita,” Inf. Mulawarman J Ilm Ilmu Komput, vol. 18, no. 2, p. 78, 2023.

M. Yunus, M. K. Biddinika, and A. Fadlil, “Classification of Stunting in Children Using the C4. 5 Algorithm,” J. Online Inform., vol. 8, no. 1, pp. 99–106, 2023.

S. Melyani, S. Z. Harahap, and I. Irmayanti, “Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4. 5 Algorithm,” Sink. J. Dan Penelit. Tek. Inform., vol. 8, no. 2, pp. 1160–1168, 2024.

R. B. Abiyyi, E. R. Subhiyakto, and F. T. Sabilillah, “Centing: Aplikasi Cegah Stunting Anak berbasis Android menggunakan TensorFlow Lite,” Edumatic J. Pendidik. Inform., vol. 8, no. 2, pp. 625–634, 2024.

X. Gong and Z.-Z. Rao, “Construction of a model for analyzing the trend of suicidal tendency among college students based on mental health monitoring,” Appl. Math. Nonlinear Sci., vol. 9, no. 1, 2024, doi: 10.2478/amns-2024-3479.

M. H. Tito et al., “Advancing vector-borne disease prediction through functional classifier integration: A novel approach for enhanced modeling,” Lett. Anim. Biol., 2024, doi: 10.62310/liab.v4i1.135.

C. Pabitha, V. Kalpana, E. S. Sv, A. Pushpalatha, G. Mahendran, and S. Sivarajan, “Development and Implementation of an Intelligent Health Monitoring System using IoT and Advanced Machine Learning Techniques,” J. Mach. Comput., 2023, doi: 10.53759/7669/jmc202303037.

T. N. Abiodun, D. Okunbor, and V. C. Osamor, “Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework,” Health Technol., vol. 12, no. 2, 2022, doi: 10.1007/s12553-022-00652-z.

T. Sugihartono and R. R. C. Putra, “Penerapan Metode Support Vector Machine Dalam Classifikasi Ulasan Pengguna Aplikasi Mobile JKN,” SKANIKA Sist. Komput. Dan Tek. Inform., vol. 7, no. 2, 2024, doi: 10.36080/skanika.v7i2.3193.

D. Damayanti, D. I. Efendi, D. Solihudin, C. L. Rohmat, and S. E. Permana, “Pemetaan Opini Publik Terhadap Perubahan Kebijakan BPJS Kesehatan Dengan Pendekatan Support Vector Machine(svm) Dalam Analisis Sentimen,” JATI J. Mhs. Tek. Inform., vol. 8, no. 1, 2024, doi: 10.36040/jati.v8i1.8304.

N. Maulida, N. Suarna, and W. Prihartono, “Analisis Ulasan Sentimen Aplikasi Mobile JKN Dengan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization,” JATI J. Mhs. Tek. Inform., vol. 8, no. 2, 2024, doi: 10.36040/jati.v8i2.9105.

R. A. Raharjo, I. M. G. Sunarya, and D. G. H. Divayana, “Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Kasus Analisis Sentimen Terhadap Data Vaksin Covid-19 Di Twitter,” Elkom J. Elektron. Dan Komput., vol. 15, no. 2, 2022, doi: 10.51903/elkom.v15i2.918.

G. Sanhaji, A. Febrianti, and H. Hidayat, “Aplikasi DIATECT Untuk Prediksi Penyakit Diabetes Menggunakan SVM Berbasis Web,” J. Tekno Kompak, vol. 18, no. 1, 2024, doi: 10.33365/jtk.v18i1.3643.

A. Jalil, A. Homaidi, and Z. Fatah, “Implementasi Algoritma Support Vector Machine Untuk Klasifikasi Status Stunting Pada Balita,” G-Tech J. Teknol. Terap., vol. 8, no. 3, pp. 2070–2079, 2024.

A. F. Najwa and P. H. Gunawan, “Enhancing Stunting Prediction for Indonesian Children Using Machine Learning with SMOTE Data Balancing,” J. Comput. Sci. Eng., vol. 18, no. 4, pp. 203–213, 2024.

S. D. Wahyuni and R. H. Kusumodestoni, “Optimalisasi Algoritma Support Vector Machine (SVM) Dalam Klasifikasi Kejadian Data Stunting,” Bull. Inf. Technol. BIT, vol. 5, no. 2, pp. 56–64, 2024.

T. Sugihartono, B. Wijaya, M. Marini, A. P. Alkayess, and H. A. Anugerah, “Optimizing Stunting Detection through SMOTE and Machine Learning: a Comparative Study of XGBoost, Random Forest, SVM, and k-NN,” J. Appl. Data Sci., vol. 6, no. 1, pp. 667–682, 2025.

O. Pahlevi, D. A. N. Wulandari, L. K. Rahayu, H. Leidiyana, and Y. Handrianto, “Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier,” Bull. Comput. Sci. Res., vol. 4, no. 6, pp. 414–421, 2024.

Additional Files

Published

2025-09-27

How to Cite

[1]
I. Fadil, R. . Surya Manggala, E. Firmansyah, and M. A. . Helmiawan, “Performance Optimization of Support Vector Machine with SMOTE for Multiclass Stunting Prediction in Sumedang District, Indonesia”, J. Tek. Inform. (JUTIF), vol. 6, no. 4, pp. 2917–2928, Sep. 2025.

Most read articles by the same author(s)