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Application of C4.5 Algorithm Using Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO) for Diabetes Prediction

Recursive Journal of Informatics · Vol. 2 No. 1 (2024) · hlm. 18–27

DOI
10.15294/yjy1tw93
ISSN
2986-6588, 2963-5551
Terbit
2024-03-31
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Abstract. Diabetes is the fourth or fifth leading cause of death in most developed countries and an epidemic in many developing countries. Early detection can be a preventive measure that uses a set of existing data to be processed through data mining with a classification process. Purpose: Investigate the efficacy of integrating the C4.5 algorithm with Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO) for improving the accuracy of diabetes prediction models. By employing SMOTE, the study aims to address the class imbalance issue inherent in diabetes datasets, which often contain significantly fewer instances of positive cases (diabetes) than negative cases (non-diabetes). Furthermore, by incorporating PSO, the research seeks to optimize the decision tree construction process within the C4.5 algorithm, enhancing its ability to discern complex patterns and relationships within the data. Methods/Study design/approach: This study proposes the use of the C4.5 classification algorithm by applying the synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO) to overcome problems in the diabetes dataset, namely the Pima Indian Diabetes Database (PIDD). Result/Findings: From the research results, the accuracy obtained in applying the C4.5 algorithm without the preprocessing process is 75.97%, while the results of the SMOTE application of the C4.5 algorithm are 80%. Meanwhile, applying the C4.5 algorithm using SMOTE and PSO produces the highest accuracy, with 82.5%. This indicates an increase of 6.53% from the classification results using the C4.5 algorithm. Novelty/Originality/Value: This research contributes novelty by proposing a hybrid approach that combines the C4.5 decision tree algorithm with two advanced techniques, Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO), for the prediction of diabetes. While previous studies have explored the application of machine learning algorithms for diabetes prediction, few have examined the synergistic effects of integrating SMOTE and PSO with the C4.5 algorithm specifically.

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  • State University of Semarang
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Rekan penulisOrganisasiArtikel bersama
Rima Ayu CahyaniAfiliasi tidak diketahui2
Renita Arianti HapsariAfiliasi tidak diketahui2
Fathan ArsyadaniAfiliasi tidak diketahui2
Arif WidiyatmokoState University of Semarang2
Wahyu HardyantoAfiliasi tidak diketahui2
Ulfatun NiswahAfiliasi tidak diketahui1
Alfat MutoriqAfiliasi tidak diketahui1
Alfandy Himawan Bagus RafliAfiliasi tidak diketahui1
Syuja Zhafran Rakha KrishandhieAfiliasi tidak diketahui1
Kholiq BudimanAfiliasi tidak diketahui1
Fitri Arum SasiAfiliasi tidak diketahui1
SugiyantoAfiliasi tidak diketahui1
Nila Prasetya AryaniAfiliasi tidak diketahui1
M. Fadil MardiansyahAfiliasi tidak diketahui1
Wahyu SetyaningrumAfiliasi tidak diketahui1
Defin AndestianAfiliasi tidak diketahui1
Muhamad Anbiya Nur IslamAfiliasi tidak diketahui1
Bambang Eko SusiloState University of Semarang1
Fianti FiantiAfiliasi tidak diketahui1
Ary Woro KurniasihAfiliasi tidak diketahui1
Muhamad TaufiqState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Indah Urwatin WusqoState University of Semarang1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Application of C4.5 Algorithm Using Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO) for Diabetes Prediction</dc:title>
	<dc:creator xml:lang="en">Damayanti, Dela Rista</dc:creator>
	<dc:creator xml:lang="en">Purwinarko, Aji</dc:creator>
	<dc:subject xml:lang="en">Diabetes</dc:subject>
	<dc:subject xml:lang="en">Data Mining</dc:subject>
	<dc:subject xml:lang="en">C4.5 Algorithm</dc:subject>
	<dc:subject xml:lang="en">SMOTE</dc:subject>
	<dc:subject xml:lang="en">PSO</dc:subject>
	<dc:description xml:lang="en">Abstract.&amp;nbsp;Diabetes is the fourth or fifth leading cause of death in most developed countries and an epidemic in many developing countries. Early detection can be a preventive measure that uses a set of existing data to be processed through data mining with a classification process.
Purpose:&amp;nbsp;Investigate the efficacy of integrating the C4.5 algorithm with Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO) for improving the accuracy of diabetes prediction models. By employing SMOTE, the study aims to address the class imbalance issue inherent in diabetes datasets, which often contain significantly fewer instances of positive cases (diabetes) than negative cases (non-diabetes). Furthermore, by incorporating PSO, the research seeks to optimize the decision tree construction process within the C4.5 algorithm, enhancing its ability to discern complex patterns and relationships within the data.
Methods/Study design/approach:&amp;nbsp;This study proposes the use of the C4.5 classification algorithm by applying the synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO) to overcome problems in the diabetes dataset, namely the Pima Indian Diabetes Database (PIDD).
Result/Findings:&amp;nbsp;From the research results, the accuracy obtained in applying the C4.5 algorithm without the preprocessing process is 75.97%, while the results of the SMOTE application of the C4.5 algorithm are 80%. Meanwhile, applying the C4.5 algorithm using SMOTE and PSO produces the highest accuracy, with 82.5%. This indicates an increase of 6.53% from the classification results using the C4.5 algorithm.
Novelty/Originality/Value:&amp;nbsp;This research contributes novelty by proposing a hybrid approach that combines the C4.5 decision tree algorithm with two advanced techniques, Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO), for the prediction of diabetes. While previous studies have explored the application of machine learning algorithms for diabetes prediction, few have examined the synergistic effects of integrating SMOTE and PSO with the C4.5 algorithm specifically.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-03-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/34939</dc:identifier>
	<dc:identifier>10.15294/yjy1tw93</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 2 No. 1 (2024): March 2024; 18-27</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/krhn0z16</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/34939/6337</dc:relation>
</oai_dc:dc>