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C4.5 Algorithm Optimization and Support Vector Machine by Applying Particle Swarm Optimization for Chronic Kidney Disease Diagnosis

Recursive Journal of Informatics · Vol. 1 No. 1 (2023) · hlm. 18–26

DOI
10.15294/cdnb8v88
ISSN
2986-6588, 2963-5551
Terbit
2023-03-27
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Abstract. Kidneys are one of the organs of the body that have a very important function in life. The main function of the kidneys is to excrete metabolic waste products. Chronic kidney disease is a result of the gradual loss of kidney function. Chronic kidney disease occurs when the kidneys are unable to maintain an internal environment consistent with life and the restoration of useless functions. Data mining is one of the fastest growing technologies in biomedical science and research. Purpose: In the field of medicine, data mining can improve hospital information management and telemedicine development. In the first stage of data mining process, data processing is done with pre-processing by handling missing values ​​and data transformation. Then, the feature selection stage is carried out using the Particle Swarm Optimization algorithm to find the best attributes. Next, it is done by classifying the dataset. Methods/Study design/approach: The algorithm used for classification is the C4.5 Algorithm and the Support Vector Machine. Both classifications are known as algorithms that have a fairly good level of accuracy. This study uses the chronic kidney disease dataset from the UCI Machine Learning Repository. Result/Findings: This research increases the accuracy by 100% for the C4.5 Algorithm and 98.75% for the Support Vector Machine by using 24 attributes and 1 class attribute. Novelty/Originality/Value: The purpose of this study was to determine the level of accuracy of the comparison between the C4.5 Algorithm and the Support Vector Machine after applying the Particle Swarm Optimization algorithm.

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  • Institut Teknologi Kalimantan
  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Alamsyah AlamsyahInstitut Teknologi Kalimantan7
Much Aziz MuslimTun Hussein Onn University of Malaysia3
Endang SugihartiState University of Semarang3
Amalia Ika WulandariAfiliasi tidak diketahui3
Riza ArifudinState University of Semarang2
Chris Jeremy Verian SitorusInstitut Teknologi Kalimantan2
Muhammad Uswah PawaraInstitut Teknologi Kalimantan2
Rodlian Jamal IkhwaniInstitut Teknologi Kalimantan2
Suardi SuardiAfiliasi tidak diketahui2
Amalia Ika WulandariInstitut Teknologi Kalimantan2
Suardi SuardiInstitut Teknologi Kalimantan2
Muhamad Faishol HakimAfiliasi tidak diketahui2
Meidika Bagus SaputroAfiliasi tidak diketahui2
Diah Siti Fatimah AzzahrahAfiliasi tidak diketahui2
Budi PrasetiyoState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Charles Sambera KalaAfiliasi tidak diketahui1
Niti Gede KumandangInstitut Teknologi Kalimantan1
Arif Nur IskandarAfiliasi tidak diketahui1
Taufik HidayatAfiliasi tidak diketahui1
Taufik HidayatInstitut Teknologi Kalimantan1
Arif Nur IskandarInstitut Teknologi Kalimantan1
Abrari Noor HasmiAfiliasi tidak diketahui1
Abrari Noor HasmiInstitut Teknologi Kalimantan1
Asram Dio PratamaInstitut Teknologi Kalimantan1
Rodlian Jamal IkhwaniAfiliasi tidak diketahui1
Irvan SetiawanInstitut Teknologi Kalimantan1
Destyariani Liana PutriAfiliasi tidak diketahui1
Alan Dicky Swans SaragihAfiliasi tidak diketahui1
Anggoronadhi DianiswaraAfiliasi tidak diketahui1
Destyariani Liana PutriInstitut Teknologi Kalimantan1
Alan Dicky Swans SaragihInstitut Teknologi Kalimantan1
Anggoronadhi DianiswaraInstitut Teknologi Kalimantan1
Wira SetiawanAfiliasi tidak diketahui1
Muhammad Uswah PawaraAfiliasi tidak diketahui1
Andi Mursid Nugraha ArifuddinAfiliasi tidak diketahui1
Samsu Dlukha NurcholiqAfiliasi 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 6 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">C4.5 Algorithm Optimization and Support Vector Machine by Applying Particle Swarm Optimization for Chronic Kidney Disease Diagnosis</dc:title>
	<dc:creator xml:lang="en">Ariyanti, Lisa</dc:creator>
	<dc:creator xml:lang="en">Alamsyah, Alamsyah</dc:creator>
	<dc:subject xml:lang="en">Chronic Kidney Disease</dc:subject>
	<dc:subject xml:lang="en">C4.5 Algorithm</dc:subject>
	<dc:subject xml:lang="en">Support Vector Machine</dc:subject>
	<dc:subject xml:lang="en">Particle Swarm Optimization</dc:subject>
	<dc:description xml:lang="en">Abstract. Kidneys are one of the organs of the body that have a very important function in life. The main function of the kidneys is to excrete metabolic waste products. Chronic kidney disease is a result of the gradual loss of kidney function. Chronic kidney disease occurs when the kidneys are unable to maintain an internal environment consistent with life and the restoration of useless functions. Data mining is one of the fastest growing technologies in biomedical science and research.
Purpose: In the field of medicine, data mining can improve hospital information management and telemedicine development. In the first stage of data mining process, data processing is done with pre-processing by handling missing values ​​and data transformation. Then, the feature selection stage is carried out using the Particle Swarm Optimization algorithm to find the best attributes. Next, it is done by classifying the dataset.
Methods/Study design/approach: The algorithm used for classification is the C4.5 Algorithm and the Support Vector Machine. Both classifications are known as algorithms that have a fairly good level of accuracy. This study uses the chronic kidney disease dataset from the UCI Machine Learning Repository.
Result/Findings: This research increases the accuracy by 100% for the C4.5 Algorithm and 98.75% for the Support Vector Machine by using 24 attributes and 1 class attribute.
Novelty/Originality/Value:&amp;nbsp;The purpose of this study was to determine the level of accuracy of the comparison between the C4.5 Algorithm and the Support Vector Machine after applying the Particle Swarm Optimization algorithm.&amp;nbsp;</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2023-03-27</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/35234</dc:identifier>
	<dc:identifier>10.15294/cdnb8v88</dc:identifier>
	<dc:source xml:lang="en">Recursive Journal of Informatics; Vol. 1 No. 1 (2023): March 2023; 18-26</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/nth9v310</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/35234/6452</dc:relation>
</oai_dc:dc>