Optimasi Algoritma SVM dengan Teknik SMOTE dan Tuning Parameter pada Klasifikasi Balita Stunting
- Muhammad Al Ghorizmi Muttaqin Universitas Dian Nuswantoro, Semarang, Indonesia
-
Gustina Alfa Trisnapradika
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
(*) Corresponding Author
Abstract
Stunting in toddlers is a chronic nutritional problem that has long-term impacts on human resource quality, including cognitive development and vulnerability to diseases. Brebes Regency is one of the priority areas for stunting management in Indonesia. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm in classifying stunting status among toddlers by addressing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and parameter tuning. A total of 9,598 anthropometric samples collected from several community health center in Brebes were processed through stages of data cleaning, label encoding, outlier handling, standardization, and class splitting, and then divided into training data (80%) and testing data (20%). Two models were compared: the baseline SVM model and the optimized SVM model, which integrates SMOTE and parameter tuning through GridSearchCV. The results showed that the baseline model achieved an accuracy of 98.31%, but the recall for the stunting class was only 89.19%. After applying SMOTE and parameter tuning, the model’s performance improved, achieving an accuracy of 99.78% and a recall for the stunting class of 98.46%. This improvement demonstrates that the use of SMOTE and parameter tuning is highly effective in enhancing the model’s sensitivity toward the minority class. Therefore, this study shows that a comprehensive optimization approach can effectively support early detection of stunting, making it a valuable tool for more targeted health intervention planning.
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References
N. Norfai dan A. Abdullah, “Determinan Kejadian Stunting pada Balita di Wilayah Kerja Puskesmas Pekauman Kota Banjarmasin,” Jurnal Akademika Baiturrahim Jambi, vol. 10, no. 1, hlm. 131–144, Mar 2021, doi: 10.36565/jab.v10i1.292.
M. L. Tobing, M. Pane, dan E. Harianja, “Pola Asuh Ibu dengan Kejadian Stunting pada Anak Usia 24-59 Bulan di Wilayah Kerja Puskesmas Kelurahan Sekupang Kota Batam,” PREPOTIF, vol. 5, no. 1, hlm. 448–465, Apr 2021, doi: 10.31004/prepotif.v5i1.1630.
W. H. Yusuf, “Faktor Resiko Stunting di Nusa Tenggara Barat (NTB), Indonesia,” Religion, Culture, and State Journal, vol. 2, no. 1, hlm. 34–45, Apr 2022.
D. T. Rahayu, “Anemia In Pregnancy With Stunting In Gayam Village District Gurah Kediri,” midwiferia, vol. 7, no. 1, hlm. 81–94, Jun 2021, doi: 10.21070/midwiferia.v7i1.1319.
BKPK, “Buku Saku Hasil Survei Status Gizi Indonesia (SSGI) 2022.” Diakses: 6 Agustus 2025. [Daring]. Tersedia pada: https://repository.badankebijakan.kemkes.go.id/id/eprint/4855/
BKPK, Survei Status Gizi Indonesia (SSGI) 2024 Dalam Angka. 2025. Diakses: 6 Agustus 2025. [Daring]. Tersedia pada: https://repository.badankebijakan.kemkes.go.id/id/eprint/5861/
H. BKPK, “SSGI 2024: Prevalensi Stunting Nasional Turun Menjadi 19,8%,” Badan Kebijakan Pembangunan Kesehatan | BKPK Kemenkes. Diakses: 6 Agustus 2025. [Daring]. Tersedia pada: https://www.badankebijakan.kemkes.go.id/ssgi-2024-prevalensi-stunting-nasional-turun-menjadi-198/
S. U. Janah, M. Azinar, dan H. Koesyanto, “Tatanan Keluarga dengan Kejadian Stunting Balita Usia 24-59 Bulan di Dataran Tinggi dan Dataran Rendah,” HIGEIA (Journal of Public Health Research and Development), vol. 7, no. 2, Art. no. 2, Apr 2023, doi: 10.15294/higeia.v7i2.64203.
A. Aseegaf, “Evaluasi Program Percepatan Penurunan Stunting di Kecamatan Bulakamba Kabupaten Brebes,” Public Policy and Management Inquiry, vol. 8, no. 1, Art. no. 1, Mei 2024, doi: 10.20884/1.ppmi.2024.8.1.10982.
B. Sapriatin dan F. A. Sianturi, “Penerapan Teorema Bayes Mendeteksi Stunting Pada Balita,” Jurnal Media Informatika, vol. 3, no. 1, hlm. 24–37, Des 2021, doi: 10.55338/jumin.v3i1.
S. Lonang, A. Yudhana, dan M. K. Biddinika, “Rancangan Sistem Klasifikasi Kekurangan Gizi Balita Dengan Metode K-Nearest Neighbor,” JINRPL, vol. 5, no. 1, hlm. 73, Mar 2023, doi: 10.36499/jinrpl.v5i1.7834.
A. I. Putri, Y. Syarif, P. Jayadi, F. Arrazak, dan F. N. Salisah, “Implementasi Algoritma Decision Tree dan Support Vector Machine (SVM) untuk Prediksi Risiko Stunting pada Keluarga: Implementation of Decision Tree and Support Vector Machine (SVM) Algorithm for Stunting Risk Prediction,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, Art. no. 2, 2023, doi: 10.57152/malcom.v3i2.1228.
M. I. Anugrah, J. Zeniarja, dan D. S. Setiawan, “Peningkatan Performa Model Hard Voting Classifier dengan Teknik Oversampling ADASYN pada Penyakit Diabetes,” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 1, Art. no. 1, Jun 2024, doi: 10.29408/edumatic.v8i1.25838.
T. Yulian dan E. R. Susanto, “Comparative Analysis of Oversampling and SMOTEENN Techniques in Machine Learning Algorithms for Breast Cancer Prediction,” Sistemasi: Jurnal Sistem Informasi, vol. 14, no. 3, Art. no. 3, Mei 2025, doi: 10.32520/stmsi.v14i3.5146.
A. Jalil, A. Homaidi, dan Z. Fatah, “Implementasi Algoritma Support Vector Machine Untuk Klasifikasi Status Stunting Pada Balita,” G-Tech, vol. 8, no. 3, hlm. 2070–2079, Jul 2024, doi: 10.33379/gtech.v8i3.4811.
T. R. Dewanti, R. Prathivi, dan S. Susanto, “Implementasi Metode SVM untuk Klasifikasi Penyakit Stunting Bayi,” Jurnal Informatika Teknologi dan Sains (Jinteks), vol. 7, no. 1, Art. no. 1, Feb 2025, doi: 10.51401/jinteks.v7i1.5070.
L. Pasiolo, I. Afrianty, E. Budianita, dan R. Abdillah, “Penerapan Teknik SMOTE pada Klasifikasi Penyakit Stroke dengan Algoritma Support Vector Machine,” Jurnal Sistem Informasi, vol. 7, no. 1, 2025.
N. Fitriyani, D. R. Amalia, H. H. Handayani, dan A. F. N. Masruriyah, “Aplikasi Berbasis Web Berdasarkan Model Klasifikasi Algoritma SVM dan Logistic Regression Terhadap Data Diabetes,” REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, vol. 7, no. 4, Art. no. 4, Okt 2023, doi: 10.33395/remik.v7i4.13001.
G. Rahmawati, S. A. Sanmas, E. Nudyawati, N. D. Syaharani, M. R. Annas, dan F. Fauzi, “Studi Perbandingan Performa: Prediksi Status Stunting Pada Anak Berdasarkan Data Antropometri Menggunakan Algoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN),” PROSIDING SEMINAR NASIONAL SAINS DATA, vol. 4, no. 1, Art. no. 1, Okt 2024, doi: 10.33005/senada.v4i1.330.
T. Z. Jasman, E. Hasmin, Sunardi, C. Susanto, dan W. Musu, “Perbandingan Logistic Regression, Random Forest, dan Perceptron pada Klasifikasi Pasien Gagal Jantung,” CSRID (Computer Science Research and Its Development Journal), vol. 14, no. 3, hlm. 271–286, Des 2022, doi: 10.22303/csrid-.14.3.2022.271-286.
N. Maulidah, R. Supriyadi, D. Y. Utami, F. N. Hasan, A. Fauzi, dan A. Christian, “Prediksi Penyakit Diabetes Melitus Menggunakan Metode Support Vector Machine dan Naive Bayes,” Indonesian Journal on Software Engineering (IJSE), vol. 7, no. 1, hlm. 63–68, Jun 2021, doi: 10.31294/ijse.v7i1.10279.
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