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Random Forest Algorithm Optimization using K-Nearest Neighborand SMOTE on Diabetes Disease

Recursive Journal of Informatics · Vol. 3 No. 1 (2025) · hlm. 43–50

DOI
10.15294/rji.v3i1.1576
ISSN
2986-6588, 2963-5551
Terbit
2025-03-28
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Abstract. Diabetes is a chronic disease that can cause long-term damage, dysfunction and failure of various organs in the body. Diabetes occurs due to an increase in blood sugar (glucose) levels exceeding normal values. Early diagnosis of diseases is crucial for addressing them, especially in the case of diabetes, which is one of the chronic illnesses. Purpose: This study aims to find out how the implementation of the K-Nearest Neighbor algorithm with the Synthetic Minority Oversampling Technique (SMOTE) in optimizing Random Forest algorithm for diabetes disease prediction. Methods/Study design/approach: This study uses the Pima Indian Diabetes Dataset, the random forest algorithm for the classification, k-nearest neighbor for optimization, and SMOTE for the minority class oversampling. Result/Findings: The prediction accuracy of the model using SMOTE and k-nearest neighbor is 92,86%. Meanwhile, the model that does not use SMOTE and k-nearest neighbor obtains an accuracy of 83,03%. Novelty/Originality/Value: This research shows that the use of random forest algorithm with k-nearest neighbor and SMOTE gives better accuracy than without using k-nearest neighbor and SMOTE.

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  • State University of Semarang
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Rekan penulisOrganisasiArtikel bersama
Arif WidiyatmokoState University of Semarang3
Muhamad TaufiqState University of Semarang2
Melissa Salma DarmawanState University of Semarang2
Indah Urwatin WusqoState University of Semarang2
Rima Ayu CahyaniState University of Semarang2
Dela Rista DamayantiState University of Semarang2
Renita Arianti HapsariState University of Semarang2
Fathan ArsyadaniState University of Semarang2
Wahyu HardyantoState University of Semarang2
Epafraditus MemorianoState University of Semarang1
Agung LaksonoState University of Semarang1
Alfat MutoriqState University of Semarang1
Alfandy Himawan Bagus RafliState University of Semarang1
Kholiq BudimanState University of Semarang1
Fitri Arum SasiState University of Semarang1
Nila Prasetya AryaniAfiliasi tidak diketahui1
Defin AndestianAfiliasi tidak diketahui1
SugiyantoAfiliasi tidak diketahui1
Wahyu SetyaningrumAfiliasi tidak diketahui1
M Fadil MardiansyahAfiliasi tidak diketahui1
Muhamad Anbiya Nur IslamAfiliasi tidak diketahui1

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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)
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	<dc:title xml:lang="en">Random Forest Algorithm Optimization using K-Nearest Neighborand SMOTE on Diabetes Disease</dc:title>
	<dc:creator xml:lang="en">Krishandhie, Syuja Zhafran Rakha</dc:creator>
	<dc:creator xml:lang="en">Purwinarko, Aji</dc:creator>
	<dc:subject xml:lang="en">Diabetes Disease, Random Forest, K-Nearest Neighbor, SMOTE</dc:subject>
	<dc:description xml:lang="en">Abstract. Diabetes is a chronic disease that can cause long-term damage, dysfunction and failure of various organs in the body. Diabetes occurs due to an increase in blood sugar (glucose) levels exceeding normal values. Early diagnosis of diseases is crucial for addressing them, especially in the case of diabetes, which is one of the chronic illnesses.
Purpose: This study aims to find out how the implementation of the K-Nearest Neighbor algorithm with the Synthetic Minority Oversampling Technique (SMOTE) in optimizing Random Forest algorithm for diabetes disease prediction.
Methods/Study design/approach: This study uses the Pima Indian Diabetes Dataset, the random forest algorithm for the classification, k-nearest neighbor for optimization, and SMOTE for the minority class oversampling.
Result/Findings: The prediction accuracy of the model using SMOTE and k-nearest neighbor is 92,86%. Meanwhile, the model that does not use SMOTE and k-nearest neighbor obtains an accuracy of 83,03%.
Novelty/Originality/Value: This research shows that the use of random forest algorithm with k-nearest neighbor and SMOTE gives better accuracy than without using k-nearest neighbor and SMOTE.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-03-28</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/1576</dc:identifier>
	<dc:identifier>10.15294/rji.v3i1.1576</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 3 No. 1 (2025): March 2025; 43-50</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/rji.v3i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/1576/3434</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Recursive Journal of Informatics</dc:rights>
</oai_dc:dc>