Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier


Authors

  • Omar Pahlevi Universitas Bina Sarana Informatika, Jakarta, Indonesia
  • Dewi Ayu Nur Wulandari Universitas Bina Sarana Informatika, Jakarta, Indonesia
  • Luci Kanti Rahayu Universitas Bina Sarana Informatika, Jakarta, Indonesia
  • Henny Leidiyana Universitas Bina Sarana Informatika, Jakarta, Indonesia
  • Yopi Handrianto Universitas Bina Sarana Informatika, Jakarta, Indonesia

DOI:

https://doi.org/10.47065/bulletincsr.v4i6.373

Keywords:

CatBoost Classifie; Machine Learning; Model Klasifikasi; Risiko Stunting; Variabel Kategorikal

Abstract

Stunting is a significant health issue in Indonesia, affecting the growth and development of young children and influenced by various complex risk factors such as nutrition, environment, and access to healthcare services. The manual process of identifying stunting risks often requires considerable time, resources, and specialized expertise from medical professionals. This study aims to develop a stunting risk classification model for young children using machine learning through the CatBoost Classifier algorithm. This algorithm was chosen for its advantages in handling categorical variables without requiring complex encoding processes and its ability to manage imbalanced data, ultimately improving prediction accuracy. In the conducted case study, the model's prediction updates were illustrated by increasing the initial prediction from 0.25 to 0.27 after accounting for residual corrections in the first iteration, with a learning rate of 0.1. This process demonstrates CatBoost's iterative mechanism for improving model predictions through gradual updates. Evaluation results showed that the developed model achieved an accuracy of 98.47% and a ROC-AUC score of 1.00 for several classes, indicating a high capability in accurately classifying stunting risks. These findings suggest that the CatBoost algorithm is effective for stunting risk classification, capable of handling data complexity, and expected to contribute significantly to supporting stunting prevention efforts through improved early detection.

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References

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Published: 2024-10-30

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How to Cite

Pahlevi, O., Wulandari, D. A. N., Rahayu , L. K., Leidiyana, H., & Handrianto, Y. (2024). Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier. Bulletin of Computer Science Research, 4(6), 414-421. https://doi.org/10.47065/bulletincsr.v4i6.373

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