Perbandingan Efektivitas Random Forest, XGBoost, SVM, dan ANN dalam Mengidentifikasi Stunting pada Anak Balita

Rando Rando, Darmawan Darmawan, La Ode Reza Apriliyanto, Alkanza Patih R

Abstract


Penelitian ini membandingkan performa machine learning dan deep learning dalam klasifikasi status gizi anak berdasarkan data UPTD Puskesmas Lakologuo dan gabungan dengan dataset Kaggle. Percobaan dilakukan dalam empat skenario, yaitu klasifikasi 2 class dan multiple class pada dataset UPTD serta kombinasi dengan Kaggle. Model yang diuji meliputi Random Forest, XGBoost, SVM, LSTM, dan ANN, dengan evaluasi menggunakan akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa Random Forest dan XGBoost memiliki akurasi tertinggi (100%) dan tetap stabil setelah penggabungan dataset. SVM bekerja baik pada 2 kelas, tetapi menurun dalam multiple class. Model deep learning, terutama ANN, memiliki performa terendah (37% dalam multiple class). Secara keseluruhan, Random Forest dan XGBoost lebih direkomendasikan, sementara ANN dan LSTM lebih cocok untuk klasifikasi 2 kelas jika didukung dengan data lebih besar dan teknik pelatihan lanjutan.


Keywords


machine learning; deep learning; Random Forest; XGBoost,

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DOI: https://doi.org/10.15548/jostech.v5i2.11112

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