Prediksi Keputusan Memulai Diet Pada Usia Pemuda di Jakarta Pendekatan Machine Learning
DOI:
https://doi.org/10.32493/sm.v8i2.54171Keywords:
Diet, Machine Learning, XGBoostAbstract
Obesitas merupakan permasalahan global yang juga dihadapi oleh banyak negara berkembang, termasuk Indonesia. Tingginya prevalensi obesitas sentral pada kelompok usia muda (16–30 tahun), khususnya di DKI Jakarta yang mencatatkan tingkat obesitas sentral tertinggi secara nasional, menunjukkan adanya paradoks. Secara teoritis, kelompok usia muda di wilayah perkotaan seperti Jakarta seharusnya lebih aktif secara fisik. Sehingga penelitian ini bertujuan untuk memprediksi keputusan pemuda di Jakarta pada tahun 2024 untuk memulai diet dengan menggunakan pendekatan machine learning. Tiga algoritma yang dibandingkan dalam penelitian ini adalah Naive Bayes, XGBoost dan regresi logistic sebagai baseline. Hasil analisis menunjukkan bahwa model XGBoost memiliki performa terbaik dalam memprediksi keputusan pemuda untuk memulai diet, ditunjukkan oleh nilai AUC tertinggi. Berdasarkan analisis nilai SHAP, empat variabel yang memberikan kontribusi terbesar terhadap prediksi adalah umur, jenis kelamin, komentar negatif, dan pergaulan sehat. Nilai SHAP positif pada variabel pergaulan sehat menunjukkan bahwa lingkungan keluarga atau teman yang menerapkan pola hidup sehat meningkatkan kemungkinan prediksi pemuda tersebut untuk memulai diet dari 205 responden penelitian.
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