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All Journal Jurnal Teknik Informatika C.I.T. Medicom
Devin Cantona
Universitas Prima Indonesia, Medan, Indonesia
Computer Science & IT

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Comparison of data mining algorithms (random forest, C4.5, catboost) based on adaptive boosting in predicting diabetes mellitus Yennimar Yennimar; William Leonardi; Harris Weide; Devin Cantona; Gani Mores Hutagalung
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.730.pp1-12

Abstract

This research aims to evaluate the performance of three algorithms data mining, namely C4.5, Random Forest, and Catboost Classifier, which are strengthened by Adaptive Boosting in predicting diabetes mellitus in humans. Through analysis, it was found that the C4.5 algorithm is based on Adaptive Boosting obtained an average accuracy of 73.74%, precision of 61.39%, and recall amounting to 69.00%. Random Forest algorithm based on Adaptive Boosting shows an average accuracy of 73.52%, precision of 65.79%, and recall amounting to 65.06%. Meanwhile, the Catboost Classifier algorithm is Adaptive based Boosting has an average accuracy of 73.67%, precision of 61.19%, and recall was 69.18%. Thus, although all three algorithms shows similar performance, the C4.5 algorithm based on Adaptive Boosting stands out with better performance in terms of accuracy, precision and recall. The implication of this research is that the use of the C4.5 algorithm is based Adaptive Boosting can be a more effective approach to support early detection of diabetes mellitus in humans
Co-Authors Gani Mores Hutagalung Harris Weide William Leonardi Yennimar Yennimar