Implementasi Support Vector Machine (SVM) dalam Penentuan Klasifikasi Indeks Khusus Penanganan Stunting di Indonesia - Syafika, Vicky Alfina Nur; Karisma, Ria Dhea Layla Nur (Seminar Nasional Official Statistics, 2023)
Stunting is a health problem that poses a challenge in various countries, including Indonesia. Several government stunting intervention programs can be evaluated based on the stunting-specific intervention index. Accurate evaluation results of stunting intervention programs will facilitate the government in determining the next policy. This study aims to obtain the classification and accuracy level of the stunting-specific intervention index in Indonesia using the Support Vector Machine (SVM) method. The results of the study showed that the best model for stunting-specific intervention index classification using the SVM method was the polynomial kernel with parameters h= 1 and C=100. The resulting classification showed that there were 4 Provinces with low stunting-specific intervention index categories, 21 Provinces with moderate stunting-specific intervention index categories, and 9 Provinces with high stunting-specific intervention index categories. The 100% accuracy level of the stunting-specific intervention index classification in Indonesia using the SVM method indicates that the SVM methods is highly effective in classifying the stunting-specific intervention index in Indonesia.