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All Journal INTEGER: Journal of Information Technology JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Aprilia, Yunita Nur
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Computer Science & IT Education

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Klarifikasi Status Penderita Gizi Sunting Pada Balita Menggunakan Metode Random Forest Aprilia, Yunita Nur; Sani, Dian Ahkam; Anggadimas, Nanda Martyan
INTEGER: Journal of Information Technology Vol 9, No 2: September 2024
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v9i2.6080

Abstract

Stunting in children, as in this case, is characterized by lower-than-average body growth. This is caused by a mismatch between long-term nutrient intake and the body's needs. Possible impacts include delayed cognitive development, impairments in learning ability, as well as an increased risk of metabolic syndrome. To overcome these problems, a structured and data-based system is needed with one of the agreements used, namely the Random Forest Method on the system using stunting nutrition data for toddlers as the basis for the classification process. In developing the system that was built to help track the health of young children, especially stunting by using several indicators to support innovation, provide a classification model for toddlers suffering from stunting nutrition, and measure and evaluate the performance results of the Random Forest Method against the data variables used. From this study, it can be shown that the results of this study are that this system has successfully made a classification model and is very effective in measuring and evaluating the performance results of the Random Forest Method in the Status Classification of Stunting Nutritional Patients in Toddlers by using a dataset of 300 data so that it produces an average accuracy of 81%, an average result of 76%, an average recall result of 69%, and the average F1 score result is 72%.
Prediction of Stunting Nutritional Status in Toddlers Using Naïve Bayes Classifier Algorithm Hariyanto, Rudi; Sarwani, Mohammad Zoqi; Aprilia, Yunita Nur
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.5930

Abstract

Stunting is a chronic nutritional problem in toddlers that affects children's physical growth and cognitive development. Early identification and prediction of toddlers' nutritional status are crucial for timely intervention. This study aims to predict the nutritional status of stunting in toddlers using the Naïve Bayes Classifier algorithm. The data used in this study is derived from community health surveys with variables such as age, weight, height, and parental nutritional status. The research process began with data collection and pre-processing to ensure high-quality data. Subsequently, the data was trained using the Naïve Bayes Classifier algorithm, known for its simplicity and efficiency in data classification. Prediction results were then evaluated using metrics of accuracy, precision, recall, and F1-score to measure the model's performance. The study results indicate that the Naïve Bayes Classifier algorithm has high accuracy in predicting stunting status in toddlers, with an accuracy rate of 85%. Precision and recall also showed satisfactory results, at 82% and 87%, respectively. This model can be used as a tool for health workers to identify toddlers at risk of stunting, enabling earlier preventive measures. In conclusion, the use of the Naïve Bayes Classifier algorithm is proven effective in predicting the nutritional status of stunting in toddlers. The implementation of this model is expected to support child health programs and accelerate the reduction of stunting prevalence in the community.
Co-Authors Anggadimas, Nanda Martyan Mohammad Zoqi Sarwani, Mohammad Zoqi Rudi Hariyanto Sani, Dian Ahkam