PREDICTION OF STUNTING IN TODDLERS COMBINING THE NAIVE BAYES METHOD AND THE C4.5 ALGORITHM

SRI MELYANI, NPM 2009100070 (2024) PREDICTION OF STUNTING IN TODDLERS COMBINING THE NAIVE BAYES METHOD AND THE C4.5 ALGORITHM. Tugas_Akhir (Artikel) Sinkron : Jurnal dan Penelitian Teknik Informatika, 8 (2). pp. 1160-1168. ISSN 2541-2019 (e-ISSN) / 2541-044X (p-ISSN)

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Abstract

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations. Keywords : C4.5 Algorithm; Classification; Data Mining; Metode Decision Tree; Metode Naïve Bayes

Item Type:	Article
Uncontrolled Keywords:	C4.5 Algorithm; Classification; Data Mining; Metode Decision Tree; Metode Naïve Bayes
Subjects:	Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	31 May 2024 08:44
Last Modified:	31 May 2024 09:17
URI:	http://repository.ulb.ac.id/id/eprint/710

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