Optimalisasi Algoritma Support Vector Machine (SVM) Dalam Klasifikasi Kejadian Data Stunting
-
Sonya Dian Wahyuni
*
Universitas Islam Nahdlatul Ulama Jepara, Kota Jepara,
Indonesia
- R. Hadapiningradja Kusumodestoni Universitas Islam Nahdlatul Ulama Jepara, Kota Jepara, Indonesia
Keywords:
Stunting; SVM; Kernel Linier; Kernel Polinomial; Kernel RBF; Kernel Sigmoid
Abstract
Stunting is a condition of inhibited linear growth due to chronic malnutrition, which is a serious concern in Indonesia, especially among toddlers. Stunting identification utilizes the parameter of height-for-age z-score (HAZ), with a value of less than -2 SD as a diagnostic indicator. Factors such as socioeconomic conditions, dietary patterns, infections, and the environment influence stunting, making early prevention and intervention efforts crucial. Short-term effects of stunting include delayed motor development and cognitive impairment, while long-term impacts include the risk of chronic diseases in adulthood. Although the global prevalence of stunting has declined, Indonesia still has a high rate, ranking second in Southeast Asia. Cross-sector collaboration and the application of technology, such as Support Vector Machine (SVM) algorithms, can help identify stunting risk factors. In this study, SVM kernels including linear, polynomial, RBF, and sigmoid were evaluated. RBF SVM kernel proved to be the most effective, achieving an accuracy of over 90% with an AUC of 0.926. Collaboration and the use of technology provide hope for addressing stunting in Indonesia, requiring cooperation between the government, NGOs, and the scientific community
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[5] UNICEF/WHO/WORLD BANK, “Levels and trends in child malnutrition UNICEF / WHO / World Bank Group Joint Child Malnutrition Estimates Key findings of the 2021 edition,” World Heal. Organ., pp. 1–32, 2021, [Online]. Available: https://www.who.int/publications/i/item/9789240025257
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[7] Sukmalalana;, D. Putra;, Hafiz;, and V. Cika, “Percepatan Penurunan Stunting Untuk Mewujudkan Sumber Daya Manusia Indonesia Yang Unggul,” Pus. Kaji. Akuntabilitas Keuang. Negara, vol. 1, no. 1, pp. 1–15, 2022, [Online]. Available: https://berkas.dpr.go.id/puskajiakn/analisis-ringkas-cepat/public-file/analisis-ringkas-cepat-public-52.pdf
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[9] W. Achmad, “Social Reality Stunting Prevention in Cianjur District,” J. EduHealth, vol. 13, no. 02, pp. 467–477, 2022, [Online]. Available: http://ejournal.seaninstitute.or.id/index.php/healt/article/view/575%0Ahttp://ejournal.seaninstitute.or.id/index.php/healt/article/download/575/463
[10] S. M. J. Rahman et al., “Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach,” PLoS One, vol. 16, no. 6 June 2021, pp. 1–11, 2021, doi: 10.1371/journal.pone.0253172.
[11] I. Rahmi, M. Susanti, H. Yozza, and F. Wulandari, “Classification of Stunting in Children Under Five Years in Padang City Using Support Vector Machine,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 3, pp. 771–778, 2022, doi: 10.30598/barekengvol16iss3pp771-778.
[12] R. Kusumaningrum, T. A. Indihatmoko, S. R. Juwita, A. F. Hanifah, K. Khadijah, and B. Surarso, “Benchmarking of multi-class algorithms for classifying documents related to stunting,” Appl. Sci., vol. 10, no. 23, pp. 1–13, 2020, doi: 10.3390/app10238621.
[13] K. K. Kishore and J. V. Suman, “Prediction of malnutrition in newbornInfants using machine learning techniques,” 2023.
[14] A. N. N. Azmi, S. Khairunniza-Bejo, M. Jahari, F. M. Muharram, and I. Yule, “Identification of a suitable machine learning model for detection of asymptomatic Ganoderma boninense infection in oil palm seedlings using hyperspectral data,” Appl. Sci., vol. 11, no. 24, 2021, doi: 10.3390/app112411798.
[15] S. Syahrial, R. Ilham, Z. F. Asikin, and S. S. I. Nurdin, “Stunting Classification in Children’s Measurement Data Using Machine Learning Models,” J. La Multiapp, vol. 3, no. 2, pp. 52–60, 2022, doi: 10.37899/journallamultiapp.v3i2.614.
[16] M. D. Kurnia, “Klasifikasi Customer Relationship Management Perusahaan Telekomunikasi Seluler Dengan Metode Machine Learning,” vol. 1, no. 4, pp. 63–76, 2023.
[17] C. Aldama and M. Nasir, “Klasifikasi Penyakit Diabetes Menggunakan Metode Support Vector Machine Pada Rumah Sakit Umum Prabumulih,” J. Ilm. Betrik, vol. 14, no. 02, pp. 376–383, 2023, [Online]. Available: https://ejournal.pppmitpa.or.id/index.php/betrik/article/view/117
[18] M. Edris Effendi, I. Yuadi, and I. Puspitasari, “Prediksi Guru Kemungkinan Tetap Bekerja di Sekolah Al Uswah Surabaya Menggunakan Machine Learning,” J. Inf. dan Teknol., vol. 5, no. 1, pp. 129–137, 2023, doi: 10.37034/jidt.v5i2.361.
[19] R. Hadapinigradja Kusumodestoni\, “The 2 st Seminar Nasional dan Prosiding Scitech 2023 OCTAGONAL SHOE RACK DESIGN WITH DESIGN IDEAS,” pp. 218–223, 2023.
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[23] A. M. Abdulkadium, R. A. A. Shekan, and H. A. Hussain, “Application of Data Mining and Knowledge Discovery in Medical Databases,” Webology, vol. 19, no. 1, pp. 4912–4924, 2022, doi: 10.14704/web/v19i1/web19329.
[24] A. H. Nasyuha et al., “Frequent pattern growth algorithm for maximizing display items,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 390–396, 2021, doi: 10.12928/TELKOMNIKA.v19i2.16192.
[25] A. Zy and Wahyu Hadikristanto, “Implementasi Algoritma Metode Naive Bayes dan Support Vector Machine Tentang Pembobolan dan Kebocoran Data di Twitter,” Bull. Inf. Technol., vol. 4, no. 1, pp. 49–56, 2023, doi: 10.47065/bit.v4i1.493.
[26] I. Indriyanti, N. Ichsan, H. Fatah, T. Wahyuni, and E. Ermawati, “Implementasi Orange Data Mining Untuk Prediksi Harga Bitcoin,” J. Responsif Ris. Sains dan Inform., vol. 4, no. 2, pp. 118–125, 2022, doi: 10.51977/jti.v4i2.762.
[27] I. S. Al-Mejibli, J. K. Alwan, and D. H. Abd, “The effect of gamma value on support vector machine performance with different kernels,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 5497–5506, 2020, doi: 10.11591/IJECE.V10I5.PP5497-5506.
[28] J. Shawe-Taylor and S. Sun, “Kernel Methods and Support Vector Machines,” pp. 857–881, 2020, doi: 10.1016/b978-0-12-396502-8.00016-4.
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Published
2024-06-26
How to Cite
Dian Wahyuni, S., & Kusumodestoni, R. H. (2024). Optimalisasi Algoritma Support Vector Machine (SVM) Dalam Klasifikasi Kejadian Data Stunting . Bulletin of Information Technology (BIT), 5(2), 56 - 64. https://doi.org/10.47065/bit.v5i2.1247
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