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Optimization of Classification Accuracy Using K-Means and Genetic Algorithm by Integrating C4.5 Algorithm for Diagnosis Breast Cancer Disease

Journal of Advances in Information Systems and Technology · Vol. 3 No. 1 (2021) · hlm. 1–8

DOI
10.15294/jaist.v3i1.49011
ISSN
2715-999X, 2714-9714
Terbit
2021-04-14
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Technological development resulted in data proliferating. The data is processed into valid information for daily needs. Data mining is a technique to convert data into useful information. Data mining has been widely used in performing prediction functions, for example, health and medical science. This study using Wisconsin Diagnostic Breast Cancer dataset taken from UCI Machine Learning Repository. The dataset has 32 attributes with 569 samples. This data has a continuous and high dimensional data type, and it makes the C4.5 algorithm need long computation time and extensive storage. This study aims to improve the accuracy of the C4.5 with a combination of K-Means and Genetic Algorithm. These study results compared the accuracy of the C4.5 algorithm before and after applying the combination of K-Means and the Genetic Algorithm for diagnosing breast cancer. The accuracy of C4.5 is 91,228%. Meanwhile, the accuracy of C4.5 after optimized using the K-Means and Genetic Algorithm is 94,824%, with the average number of features are selected 22 features. Thus, the application of K-Means and Genetic Algorithm on the C4.5 Algorithm can improve the accuracy of diagnosing breast cancer by 3,596%.

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  • State University of Semarang
  • International Islamic University Malaysia
  • Universitas PGRI Palembang
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Rekan penulisOrganisasiArtikel bersama
Endang SugihartiState University of Semarang5
Florentina Yuni AriniState University of Semarang3
Rochmad RochmadInternational Islamic University Malaysia3
Amin SuyitnoState University of Semarang3
Alamsyah AlamsyahState University of Semarang2
Novanka Agnes SekartajiState University of Semarang2
Irfan Fajar MuttaqinState University of Semarang2
Ramadhan Ridho ArrohmanState University of Semarang2
Dwijanto DwijantoState University of Semarang2
Putriaji HendikawatiState University of Semarang2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Stephani Diah PamelasariState University of Semarang1
Arka YanitamaState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Aji SaputraUniversitas PGRI Palembang1
Desy Fitria AstutianingtyasAfiliasi tidak diketahui1
Syafa Nabilah SyahputraState University of Semarang1
Anggyi Trisnawan PutraState University of Semarang1
Athoillah AthoillahState University of Semarang1
Kholiq BudimanAfiliasi tidak diketahui1
Anggyi Trisnawan PutraAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Anggoro Sakti AnindhitoState University of Semarang1
Fajar SujitoAfiliasi tidak diketahui1
Anita Wahyu MelindaAfiliasi tidak diketahui1
Isa AkhlisState University of Semarang1
Achmad Wahyu HidayatAfiliasi tidak diketahui1
Dian Rahman SyahputraAfiliasi tidak diketahui1
Safit FirmansyahAfiliasi tidak diketahui1
Anggil AgustaAfiliasi tidak diketahui1
Margareta Ayu RiantikState University of Semarang1
Dandi Indra WijayaAfiliasi tidak diketahui1
Hanif Nur CahyaniAfiliasi tidak diketahui1
Andhika Seno TamtamaAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/jaist/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-US">Optimization of Classification Accuracy Using K-Means and Genetic Algorithm by Integrating C4.5 Algorithm for Diagnosis Breast Cancer Disease</dc:title>
	<dc:creator>Andoyo, Fachrizal Ahdy</dc:creator>
	<dc:creator>Arifudin, Riza</dc:creator>
	<dc:description xml:lang="en-US">Technological development resulted in data proliferating. The data is processed into valid information for daily needs. Data mining is a technique to convert data into useful information. Data mining has been widely used in performing prediction functions, for example, health and medical science. This study using Wisconsin Diagnostic Breast Cancer dataset taken from UCI Machine Learning Repository. The dataset has 32 attributes with 569 samples. This data has a continuous and high dimensional data type, and it makes the C4.5 algorithm need long computation time and extensive storage. This study aims to improve the accuracy of the C4.5 with a combination of K-Means and Genetic Algorithm. These study results compared the accuracy of the C4.5 algorithm before and after applying the combination of K-Means and the Genetic Algorithm for diagnosing breast cancer. The accuracy of C4.5 is 91,228%. Meanwhile, the accuracy of C4.5 after optimized using the K-Means and Genetic Algorithm is 94,824%, with the average number of features are selected 22 features. Thus, the application of K-Means and Genetic Algorithm on the C4.5 Algorithm can improve the accuracy of diagnosing breast cancer by 3,596%.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2021-04-14</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-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/49011</dc:identifier>
	<dc:identifier>10.15294/jaist.v3i1.49011</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 3 No 1 (2021): April; 1-8</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v3i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/49011/19766</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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