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fadillah, m
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Computer Science & IT

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MODEL PREDIKSI STUNTING PADA BALITA MENGGUNAKAN ALGORITMA NAÏVE BAYES fadillah, m; Gusti Firmansyah, Mulia; Khairuddin, M.; Rahmaddeni; Efrizoni, Lusiana
Jurnal Dinamika Informatika Vol. 13 No. 1 (2024): Jurnal Dinamika Informatika
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v13i1.292

Abstract

This study investigates the use of Naive Bayes algorithm for child stunting classification based on health and nutrition data. This study aims to identify factors that influence the risk of stunting and develop a predictive model that can assist in stunting prevention and intervention. The research methodology includes initial data processing, division of the dataset into training and testing sets, model training using the Naive Bayes algorithm, and evaluation of model performance through metrics such as accuracy, precision, and recall. The results showed that the Naive Bayes model achieved an accuracy of 72.49% for training data and 81.25% for testing data. Confusion matrix analysis shows a precision value of 0.911 and recall of 0.710 for training data; for testing data, the precision value is 0.914 and recall is 0.842. The results show that the Naive Bayes model is able to perform stunting classification quite well, although there are some limitations, such as the conditional independence assumption that may not be met at all times. This research provides insight into how classification models can be used in public health, particularly in efforts to detect and prevent stunting. The results are promising, but further evaluation is needed to optimize the model and ensure that it can be used effectively in the real world.
Perbandingan Algoritma Naive Bayes dan Decission Tree untuk Prediksi Penyakit Kanker Paru-Paru Gusti Firmansyah, Mulia; Khairuddin, M.; fadillah, M; Efrizoni, Lusiana; Rahmaddeni , Rahmaddeni
Jurnal Dinamika Informatika Vol. 13 No. 1 (2024): Jurnal Dinamika Informatika
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v13i1.309

Abstract

In this study, we compared the performance of two machine learning algorithms, Naïve Bayes and Decission Tree, for diagnosing lung diseases using patient health datasets. The main objective of this study is to evaluate the accuracy, precision, recall, and F1 score of the two algorithms to determine which method is more effective in predicting lung diseases. The results showed that the tree classification algorithm outperformed Naïve Bayes in terms of accuracy, reaching 95% in an 80:20 split, compared to the 78% accuracy achieved by Naïve Bayes on the same data. Further analysis showed that most patients in this dataset were high risk with 365 patients, followed by risk with 332 patients, and low risk with 303 patients. The decision tree structure proved to be more effective in handling the complexity of the data and produced more accurate predictions, improving efficiency by creating a new "Risk_Score". These results show that decision trees are a better method than Naïve Bayes for diagnosing lung diseases and can provide a solid foundation for developing accurate machine learning models for future health research.
Co-Authors Efrizoni, Lusiana Gusti Firmansyah, Mulia Khairuddin, M. Rahmaddeni , Rahmaddeni Rahmaddeni Rahmaddeni