Spatial Prediction of Stunting Incidents Prevalence Using Support Vector Regression Method
DOI:
https://doi.org/10.56705/ijodas.v4i2.68Keywords:
Stunting, Prediksi, Spasial, Machine Learning, Support Vector MachineAbstract
Stunting in toddlers is a major nutritional problem faced by Indonesia, with a high incidence rate occurring in several provinces across the country. This nutritional issue can occur at any age, starting from the prenatal stage, infancy, childhood, adolescence, adulthood, and even in the elderly. To reduce the prevalence of stunting in affected provinces, prevention efforts are essential, including predicting the spread of stunting incidents in each region. Therefore, this research conducted spatial prediction of the prevalence rate of stunting incidents using Machine Learning, specifically Support Vector Machine based Regression. The results of this study produced a prediction model with an RMSE (Root Mean Square Error) value of 0.008689303 and a multiple correlation coefficient of 0.65912721. Based on these findings, the predictive model utilized demonstrated satisfactory performance in predicting the prevalence rate of stunting incidents in each area
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References
H. Purwanto and S. Dalis, “Perancangan Sistem informasi Pelayanan Kesehatan Peduli Remaja (PKPR) Berbasis Web Pada Puskesmas,” SEMNASTEK, 2017.
Pusat Data dan Informasi, “Situasi Balita Pendek (Stunting) di Indonesia,” Jakarta, 2018.
Kementerian Kesehatan RI, “Hasil Utama RISKESDAS 2018,” Jakarta, 2018.
M. de Onis, M. Blossner, and E. Borghi, “Prevalence and Trends of Stunting,” Public Health Nutr, vol. 15, no. 1, 2020.
P. Anindita, “Hubungan Tingkat Pendidikan Ibu, Pendapatan Keluarga, Kecukupan Protein dan Zinc dengan Stunting (Pendek) Pada Balita Usia 6-35 Bulan di Kecamatan Tembalang Kota Semarang,” Jurnal Kesehatan Masyarakat, vol. 1, no. 2, pp. 617–626, 2012.
A. Y. Labolo, S. Mooduto, A. Bode, and D. I. C. R, “Penerapan Algoritma Spport Vector Machinedan K-Nearest Neighbor Menggunkan Feature Selection Backward Elimination Untuk Prediksi Status Penderita Stunting Pada Balita,” TECNOSCIENZA, vol. 6, no. 2, 2022.
A. Byna and F. N. Anisa, “Backward Elimination Untuk Meningkatkan Akurasi Kejadian Stunting dengan Analisis Algoritma Support Vector Machine,” Dinamika Kesehatan : Jurnal Kebidanan dan Keperawatan, vol. 9, no. 2, 2018.
Direktorat Jenderal Pembangunan dan Pemberdayaan Masyarakat Desa, “Panduan Fasilitasi Konvergensi Pencegahan Stunting di Desa,” Jakarta, 2018.
R. Primartha, Belajar Machine Learning, 1st ed. Bandung: Informatika, 2018.
A. Suárez Sánchez, P. J. García Nieto, P. Riesgo Fernández, J. J. del Coz Díaz, and F. J. Iglesias-Rodríguez, “Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain),” Math Comput Model, vol. 54, no. 5–6, pp. 1453–1466, Sep. 2011, doi: 10.1016/j.mcm.2011.04.017.
T. Trafalis and H. Ince, “Support vector machine for regression and applications to financial forecasting,” in IJCNN, Jul. 2000.
S. Maji, A. C. Berg, and J. Malik, “Efficient Classification for Additive Kernel SVMs,” IEEE Trans Pattern Anal Mach Intell, vol. 35, no. 1, pp. 66–77, Jan. 2013, doi: 10.1109/TPAMI.2012.62.
T. Cheng, J. Wang, and X. Li, “The support vector machine for nonlinear spatio-temporal regression,” in Proc Geocomputation, 2007.
N. Deng, Y. Tian, and C. Zhang, Support Vector Machines Optimization Based Theory, Algorithms, and Extensions, 1st ed. New York : CRC Press Taylor, 2013.
M. Esmaeili, A. Salimi, C. Drebenstedt, M. Abbaszadeh, and A. A. Bazzazi, “Application of PCA, SVR, and ANFIS for modeling of rock fragmentation,” Arabian Journal of Geosciences, vol. 8, no. 9, pp. 6881–6893, 2015.
N. R. Draper and H. Smith, Applied regression analysis, vol. 326. John Wiley & Sons, 2014.
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