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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Research in Vocational Studies (IJRVOCAS) Leges Privatae
Muhammad Insan Kamil, Muhammad Insan
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Implementation of the Naive Bayes Classifier Algorithm for Classifying Toddler Nutritional Status Kamil, Muhammad Insan; Wibowo, Adityo Permana
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8669

Abstract

This research addresses the pressing issue of malnutrition among toddlers in Indonesia, aiming to classify their nutritional status using the Naive Bayes Classifier (NBC). The study utilizes a dataset comprising 958 records from Puskesmas Cilandak and categorizes nutritional status into six class labels: good nutrition, at risk of excess nutrition, excess nutrition, obesity, undernutrition, and severe malnutrition. The methodology includes data preprocessing techniques such as class weighting to tackle class imbalance and Principal Component Analysis (PCA) for effective feature extraction. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, achieving an impressive accuracy of 85.76% when class weighting is applied, which significantly enhances the recall and F1 scores for minority classes. The findings highlight the critical importance of robust preprocessing and evaluation metrics in improving machine learning models for public health applications. Furthermore, they suggest that further exploration of alternative algorithms and dataset expansion could yield more comprehensive insights into the classification of toddler nutritional status.
Co-Authors Adityo Permana Wibowo Fatahul Arifin, Fatahul Helen Sastypratiwi, Helen Hengky Anra Tresna Dewi