Optimalisasi Hyperparameter Random Forest Menggunakan Random Search untuk Klasifikasi Risiko Stunting Pada Balita
- Putri Windari Universitas Muhammadiyah Bima, Bima, Indonesia
- Khairunnas Khairunnas Universitas Muhammadiyah Bima, Bima, Indonesia
-
Irma Eryanti Putri
*
Universitas Muhammadiyah Bima, Bima,
Indonesia
(*) Corresponding Author
Abstract
Stunting is a condition of impaired child growth resulting from chronic nutritional deficiency over an extended period. This condition not only hinders physical growth but also impedes learning abilities and increases the risk of various future diseases. The high prevalence of stunting in Bima City highlights the need to utilize Machine Learning methods to analyze stunting risk factors more accurately. This study aims to optimize the performance of the Random Forest algorithm using the Random Search method to classify stunting risk among children under five in Sambinae Urban Village, Bima City. The dataset comprises records for 1,162 children under five, featuring 20 attributes obtained from the Mpunda Community Health Center (Puskesmas) in Bima City. The research stages include data collection, preprocessing, data splitting, construction of a baseline Random Forest model, hyperparameter optimization using Random Search, evaluation via a Confusion Matrix (based on Accuracy, Precision, Recall, and F1-Score), and feature importance analysis. Prior to optimization, the baseline Random Forest model yielded an accuracy of 70,39%, precision of 56,67%, recall of 62,96%, and an F1-score of 59,65%. Following optimization with Random Search, model performance improved to an accuracy of 71,67%, precision of 58,43%, recall of 64,20%, and an F1-score of 61,18%. The results demonstrate that hyperparameter optimization using Random Search effectively enhances the Random Forest model's performance in classifying stunting risk. The study contributes a stunting risk classification model based on the Random Forest algorithm, optimized via Random Search to achieve a more effective hyperparameter combination than the default settings. Furthermore, the study provides a comparative performance analysis before and after optimization, along with insights into the variables that most significantly influence stunting risk classification. These findings are expected to assist healthcare professionals and local government authorities in identifying stunting risks more rapidly and accurately, thereby serving as a foundation for formulating more targeted prevention strategies.
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