Model Optimasi SVM-GSBE dalam Menangani High Dimensional Data Stunting Kota Samarinda
DOI:
https://doi.org/10.32493/jtsi.v7i3.41545Kata Kunci:
Stunting; SVM; Grid Search; Backward Elimination; Data Berdimensi TinggiAbstrak
Stunting telah menjadi topik kesehatan yang mendapat perhatian luas di Indonesia, terutama di Kota Samarinda yang mencatat prevalensi sebesar 12,7% pada tahun 2023, hal ini menyebabkan kasus stunting di Kota tersebut menjadi yang tertinggi di provinsi Kalimantan Timur. Penggunaan teknik data mining menjadi krusial dalam mengatasi tantangan data berdimensi tinggi seperti kompleksitas perhitungan, resiko overfitting, dan kesulitan visualisasi. Penelitian ini bertujuan untuk meningkatkan akurasi model optimasi Support Vector Machine menggunakan Grid Search dan seleksi fitur Backward Elimination (SVM-GSBE) guna menangani data berdimensi tinggi terkait penyakit stunting di Kota Samarinda. Dataset yang digunakan berasal dari Dinas kesehatan Kota Samarinda Tahun 2023, yang meliputi 26 Puskesmas dengan 21 atribut dan total 150.466 record. Metode penelitian mencakup pengumpulan data, pre-processing, pembagian data dengan K-Fold Cross Validation, seleksi fitur menggunakan Backward Elimination, dan optimasi model SVM dengan Grid Search. Fitur-fitur seperti BB/U, ZS TB/U, ZS BB/U, ZS BB/TB, Tinggi, dan LiLA terbukti mampu memberikan kenaikan akurasi pada klasifikasi data stunting. Hasil evaluasi menunjukkan bahwa Grid Search berhasil meningkatkan akurasi Linear dari 99.59% menjadi 99.78%, Polynomial 90.92% menjadi 99.40%, RBF 89.80% menjadi 98.36%, dan Sigmoid 75.29% menjadi 86.84%. Ini menyatakan bahwa model SVM-GSBE dapat digunakan sebagai alat yang efektif untuk deteksi dini stunting dan dapat mendukung kebijakan kesehatan di Kota Samarinda.
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