Implementasi Teknik Sampling untuk Mengatasi Imbalanced Data pada Penentuan Status Gizi Balita dengan Menggunakan Learning Vector Quantization
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Abstract
Balita memerlukan suatu pengawasan khusus karena pada masa-masa tersebut balita rentan terhadap serangan penyakit dan kekurangan gizi. Untuk itu penelitian ini bertujuan untuk menerapkan metode Learning Vector Quantization (LVQ) dalam proses klasifikasi status gizi balita ke dalam gizi lebih, gizi baik, gizi rentan, dan gizi kurang. Data yang digunakan dalam penelitian ini adalah data gizi balita sebanyak 612, terdiri dari 38 data gizi lebih, 491 data gizi baik, 63 gizi rentan, dan 20 data gizi kurang. Data tersebut disebut sebagai data tidak seimbang. Selanjutnya, penelitian ini menerapkan teknik undersampling dan oversampling untuk mengatasi permasalahan tersebut. Hasil penelitian menunjukkan bahwa penerapan metode LVQ terhadap data tak seimbang menghasilkan nilai akurasi sebesar 84.15 %, tetapi nilai overall accuracy sebesar 43.27%. Sedangkan penerapan metode LVQ terhadap data yang seimbang menghasilkan nilai akurasi dan overall accuracy yang sama yakni sebesar 74.38%.
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