Model Optimasi SVM-GSBE dalam Menangani High Dimensional Data Stunting Kota Samarinda
DOI:
https://doi.org/10.32493/jtsi.v7i3.41545Keywords:
Stunting; SVM; Grid Search; Backward Elimination; High Dimensional DataAbstract
Stunting has become a widely discussed health issue in Indonesia, par-ticularly in Samarinda City, which recorded a prevalence of 12.7% in 2023, making it the highest in East Kalimantan Province. The use of data mining techniques becomes crucial in overcoming the challenges of high dimensional data, such as computational complexity, the risk of overfitting, and visualization difficulties. This study aims to enhance the accuracy of Support Vector Machine optimization models using Grid Search and Backward Elimination feature selection (SVM-GSBE) to handle high-dimensional data related to stunting in Samarinda City. The dataset used is sourced from Samarinda City Health Office in 2023, covering 26 community health centers with 21 attributes and a total of 150,466 records. The research methodology includes data collection, pre-processing, data partitioning using K-Fold Cross Validation, feature selection using Backward Elimination, and SVM model optimization with Grid Search. Features such as BB/U, ZS TB/U, ZS BB/U, ZS BB/TB, Height, and LiLA have proven to increase accuracy in stunting data classification. Evaluation results show that Grid Search successfully increased accuracy for Linear from 99.59% to 99.78%, Polynomial from 90.92% to 99.40%, RBF from 89.80% to 98.36%, and Sigmoid from 75.29% to 86.84%. This indicates that the SVM-GSBE model can effectively be used as a tool for early detection of stunting and to support health policies in Samarinda City.
References
Ali, I., Kurnia, D. A., Pratama, M. A., & Al Ma’ruf, F. (2021). Klasifikasi Status Stunting Balita Di Desa Slangit Menggunakan Metode K-Nearest Neighbor. KOPERTIP: Scientific Journal of Informatics Management and Computer, 5(3), 35–39.
Amirudin, M., & Wowor, A. D. (2023). Analisis Perbandingan Klasifikasi Balita Beresiko Stunting Menggunakan Metode Support Vector Machine dan Decission Tree. Proceedings of the National Conference on Electrical Engineering, Informatics, Industrial Technology, and Creative Media, 3(1), 581–591.
Andriyani, S. Y., Lydia, M. S., & Efendi, S. (2023). Optimization of Support Vector Machine Algorithm Using Stunting Data Classification. Prisma Sains : Jurnal Pengkajian Ilmu Dan Pembelajaran Matematika Dan IPA IKIP Mataram, 11(1), 164. https://doi.org/10.33394/j-ps.v11i1.6619
Arisandi, R. R. R., Warsito, B., & Hakim, A. R. (2022). Aplikasi naïve bayes classifier (nbc) pada klasifikasi status gizi balita stunting dengan pengujian k-fold cross validation. Jurnal Gaussian, 11(1), 130–139.
Asad, M., & Zouq, A. (2024). A Machine Learning Approach for Predicting Stunting in Under Five Children: The Case of Pakistan Demographic and Health Survey. https://doi.org/10.21203/rs.3.rs-2907309/v1
Athoillah, M., & Putri, R. K. (2019). Handwritten Arabic Numeral Character Recognition Using Multi Kernel Support Vector Machine. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 99–106. https://doi.org/10.22219/kinetik.v4i2.724
Awalullaili, F. O., Ispriyanti, D., & Widiharih, T. (2023). Klasifikasi Penyakit Hipertensi Menggunakan Metode Svm Grid Search Dan Svm Genetic Algorithm (GA). Jurnal Gaussian, 11(4), 488–498. https://doi.org/10.14710/j.gauss.11.4.488-498
Bappeda Kaltim 2023. (n.d.). Rembug Stunting Tingkat Kota Samarinda. Retrieved April 20, 2024, from https://bappeda.kaltimprov.go.id/postingan/rembug-stunting-tingkat-kota-samarinda#
C. M. Annur. (2023). Calon Ibu Kota Baru, Bagaimana Angka Balita Stunting di Wilayah di Kalimantan Timur? https://databoks.katadata.co.id/datapublish/2023/02/27/calon-ibu-kotabaru-bagaimana-angka-balita-stunting-di-wilayah-di-kalimantan-timur
Dwi Astuti, D., Benya Adriani, R., Widyastuti Handayani, T., Keperawatan, J., & Kemenkes Surakarta, P. (2020). Pemberdayaan Masyarakat Dalam Rangka Stop Generasi Stunting. 4(2), 156–162. https://doi.org/10.31764/jmm.v4i2.1910
Gebeye, L. G., Dessie, E. Y., & Yimam, J. A. (2024). Predictors of micronutrient deficiency among children aged 6–23 months in Ethiopia: a machine learning approach. Frontiers in Nutrition, 10, 1277048.
Hakimah, M., Prabiantissa, C. N., Rozi, N. F., Yamani, L. N., & Puspitasari, I. (2022). Determination of Relevant Feature Combinations For Detection Stunting Status of Toddlers. 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 324–329.
Kusumaningrum, R., Indihatmoko, T. A., Juwita, S. R., Hanifah, A. F., Khadijah, K., & Surarso, B. (2020). Benchmarking of multi-class algorithms for classifying documents related to stunting. Applied Sciences (Switzerland), 10(23), 1–13. https://doi.org/10.3390/app10238621
Maulidina, F., Rustam, Z., Hartini, S., Wibowo, V. V. P., Wirasati, I., & Sadewo, W. (2021). Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification. Journal of Physics: Conference Series, 1821(1). https://doi.org/10.1088/1742-6596/1821/1/012006
Meisya, T., Aulia, P., Arifin, N., & Mayasari, R. (n.d.). Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi COVID-19. https://doi.org/10.31598
Muliawati, A., & Nurramdhani Irmanda, H. (2022). Penerapan Borderline-SMOTE dan Grid Search pada Bagging-SVM untuk Klasifikasi Penyakit Diabetes.
Ndagijimana, S., Kabano, I. H., Masabo, E., & Ntaganda, J. M. (2023). Prediction of stunting among under-5 children in Rwanda using machine learning techniques. Journal of Preventive Medicine and Public Health, 56(1), 41.
Nirvan Adam Pramudhyta, & Muhammad Syaifur Rohman. (2023). Perbandingan Optimasi Metode Grid Search dan Random Search dalam Algoritma XGBoost untuk Klasifikasi Stunting. JURNAL MEDIA INFORMATIKA BUDIDARMA, 8, 19–29. http://dx.doi.org/10.30865/mib.v8i1.6965
Nugroho, H., Yuliastuti, G. E., & Firman, A. (n.d.). Klasifikasi Diagnosis Diabetes Melitus Menggunakan Metode Naïve Bayes Dengan Seleksi Fitur Backward Elimination Diabetes Melitus Diagnosis Classification Using The Naive Bayes Method With Feature Selection Backward Elimination. In Jurnal Ilmiah NERO (Vol. 8, Issue 2).
Rahman, S. M. J., Ahmed, N. A. M. F., Abedin, M. M., Ahammed, B., Ali, M., Rahman, M. J., & Maniruzzaman, M. (2021). Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach. PLoS ONE, 16(6 June 2021). https://doi.org/10.1371/journal.pone.0253172
Rahmi, I., Susanti, M., Yozza, H., & Wulandari, F. (2022). Classification Of Stunting In Children Under Five Years In Padang City Using Support Vector Machine. Barekeng: Jurnal Ilmu Matematika Dan Terapan, 16(3), 771–778. https://doi.org/10.30598/barekengvol16iss3pp771-778
Reza, A. A. R., & Rohman, M. S. (2024). Prediction Stunting Analysis Using Random Forest Algorithm and Random Search Optimization. Journal Of Informatics And Telecommunication Engineering, 7(2), 534–544.
Sahamony, N. F., Terttiaavini, T., & Rianto, H. (2024). Analisis Perbandingan Kinerja Model Machine Learning untuk Memprediksi Risiko Stunting pada Pertumbuhan Anak. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(2), 413–422. https://doi.org/10.57152/malcom.v4i2.1210
Syahrial, Rosmin Ilham, Zulaika F Asikin, & St. Surya Indah Nurdin. (2022). Stunting Classification in Children’s Measurement Data Using Machine Learning Models. JOURNAL LA MULTIAPP, 3(Vol. 3 No. 2 (2022): Journal La Multiapp), 1–9. https://doi.org/10.37899/journallamultiapp.v3i2.614
Taghfirul Azhima Yoga Siswa, S. K. M. K. (2023). Data Mining - Mengupas Tuntas Analisis Data Dengan Metode Klasifikasi Hingga Deployment Aplikasi Menggunakan Python. In Data Mining: Mengupas Tuntas Analisis Data Dengan Metode Klasifikasi Hingga Deployment Aplikasi Menggunakan Python (pp. 1–258). UMKT PRESS Universitas Muhammadiyah Kalimantan Timur Jl. Ir. H. Juanda No 15 Samarinda, Kalimantan Timur Fax. 0541-766832 Email: ppi@umkt.ac.id.
Titimeidara, M. Y., & Hadikurniawati, W. (n.d.). Monica Yoshe Titimeidara Implementasi Metode Naive Bayes Implementasi Metode Naive Bayes Classifier Untuk Klasifikasi Status Gizi Stunting Pada Balita. In Tri Lomba Juang (Vol. 50241, Issue 1).
Wali, N., Agho, K., & Renzaho, A. M. N. (2019). Past drivers of and priorities for child undernutrition in South Asia: a mixed methods systematic review protocol. Systematic Reviews, 8(1), 189. https://doi.org/10.1186/s13643-019-1112-7
Wiraguna, I. K. A., Setyati, E., & Pramana, E. (2022). Prediksi Anak Stunting Berdasarkan Kondisi Orang Tua Dengan Metode Support Vector Machine Dengan Study Kasus Di Kabupaten Tabanan-Bali. SMATIKA JURNAL, 12(01), 47–54. https://doi.org/10.32664/smatika.v12i01.662
Yunus Muhajir, Biddinika Kunta Muhammad, & Fadlil Abdul. (2023). Optimasi Algoritma Naïve Bayes Menggunakan Fitur Seleksi Backward Elimination Untuk Klasifikasi Prevalensi Stunting. Decode: Jurnal Pendidikan Teknologi Informasi, 3, 1–8. https://doi.org/10.51454/decode.v3i2.188
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Siti Muawwanah, Taghfirul Azhima Yoga Siswa, Wawan Joko Pranoto
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License
YOU ARE FREE 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.
- The licensor cannot revoke these freedoms as long as you follow the license terms
_2020_-_7(2)_2024_-_Thumbnail.png)
