Komparasi Algoritma Machine Learning untuk Prediksi Risiko Stunting Berbasis Data P3KE Provinsi Gorontalo
Keywords:
Machine Learning, Prediksi Stunting, XGBoost, SHAP, Data P3KE, SMOTE, GorontaloAbstract
Stunting merupakan permasalahan kesehatan masyarakat yang serius di Provinsi Gorontalo dengan prevalensi mencapai 23,8% pada tahun 2024, masih jauh di atas target nasional sebesar 14%. Penelitian ini bertujuan mengembangkan model prediksi risiko Stunting menggunakan pendekatan Machine learning berbasis Data Pensasaran Percepatan Penghapusan Kemiskinan Ekstrem (P3KE). Dataset yang digunakan mencakup 87.902 rumah tangga di Provinsi Gorontalo. Lima algoritma dibandingkan, yaitu Decision Tree, K-Nearest Neighbors, Support Vector Machine, Random Forest, dan XGBoost. Evaluasi dilakukan pada dua skenario, yaitu tanpa dan dengan penerapan Synthetic Minority Oversampling Technique (SMOTE). Optimasi hyperparameter dilakukan menggunakan RandomizedSearchCV dengan 3-fold cross-validation. Hasil eksperimen menunjukkan bahwa XGBoost tanpa SMOTE merupakan model terbaik dengan F1-score sebesar 90,39%, accuracy 90,48%, precision 99,91%, recall 82,52%, dan AUC-ROC 94,09%. Penerapan SMOTE tidak memberikan peningkatan kinerja yang signifikan, dengan selisih F1-score yang hanya mencapai 0,0067 poin. Sebaliknya, teknik tersebut meningkatkan waktu komputasi hingga 65%. Temuan ini mengonfirmasi bahwa distribusi kelas yang relatif seimbang, yaitu 54,2% : 45,8%, tidak memerlukan proses oversampling. Analisis SHAP mengidentifikasi BAB: Milik sendiri dan Indeks Sanitasi sebagai fitur yang paling berpengaruh dalam prediksi risiko Stunting. Hasil penelitian menunjukkan bahwa data registrasi sosial P3KE tanpa melibatkan data antropometri tetap mampu menghasilkan model prediksi risiko Stunting dengan kinerja yang kompetitif, yang ditunjukkan oleh nilai F1-score di atas 90%.
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