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A Data Mining Approach to Wage Inequality Analysis in Indonesia: A Clustering Study Using Fuzzy C-Means

Unnes Journal of Mathematics · Vol. 13 No. 2 (2025) · hlm. 39–47

DOI
10.15294/ujm.v13i2.25755
ISSN
2460-5859, 2252-6943
Terbit
2025-07-27
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

This study aims to cluster Indonesian provinces based on the average wage structure of workers across 17 economic sectors using the Fuzzy C-Means (FCM) method. The wage data underwent preprocessing steps including missing value imputation using the median, logarithmic transformation to reduce skewness, and Z-Score standardization to ensure uniform data scaling. The evaluation of the number of clusters and fuzziness values was conducted using the Silhouette coefficient and Fuzzy Partition Coefficient (FPC), with the best results achieved at three clusters and a fuzziness value of 1.3. Further analysis using Principal Component Analysis (PCA) provided visualization of the clusters, while radar charts illustrated wage characteristics by sector within each cluster. The clustering results reveal significant economic disparities among provinces: Cluster 1 consists of provinces with the highest wages dominated by high-value-added sectors such as mining and finance; Cluster 0 shows a balanced wage distribution reflecting a transitional economy; and Cluster 2 includes provinces with the lowest wages facing structural challenges. These findings offer a comprehensive overview of regional economic diversity in Indonesia and can serve as a basis for policy-making aimed at more equitable economic development.

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  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Scolastika MarianiState University of Semarang6
Arief AgoestantoAfiliasi tidak diketahui5
Sugiman SugimanAfiliasi tidak diketahui5
Mashuri MashuriAfiliasi tidak diketahui3
Riza ArifudinState University of Semarang2
Nurkaromah DwidayatiAfiliasi tidak diketahui2
Yuninda Diah PratiwiAfiliasi tidak diketahui2
Dwijanto DwijantoAfiliasi tidak diketahui2
Sugeng NugrohoAfiliasi tidak diketahui2
Galih Kusuma WijayaAfiliasi tidak diketahui2
M Irfan Al HakimAfiliasi tidak diketahui1
Tsalis Ainur RofiAfiliasi tidak diketahui1
Taufik Dwi FerdiansyahAfiliasi tidak diketahui1
Riski Cahya AlfiansyahAfiliasi tidak diketahui1
Abdullah Azzam Al MuhtasibAfiliasi tidak diketahui1
Raras Setya AstutiAfiliasi tidak diketahui1
Feri FarkhanAfiliasi tidak diketahui1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Dedy PurnawanAfiliasi tidak diketahui1
Dwi Prisita AnggriningrumAfiliasi tidak diketahui1
Zaenal AbidinAfiliasi tidak diketahui1
Noni NofiyahAfiliasi tidak diketahui1
Yuliyanti KaromahAfiliasi tidak diketahui1
SELY AGUSTINAAfiliasi tidak diketahui1
RIZA SILVIA FAUSTINAAfiliasi tidak diketahui1
Addinul AssidiqAfiliasi tidak diketahui1
Andhina SaraswatiAfiliasi tidak diketahui1
hengky tri ikhsanto hengky tri ikhsantoAfiliasi tidak diketahui1
Muhammad KharisAfiliasi tidak diketahui1
Novia Nilam NurlazuardiniAfiliasi tidak diketahui1
Ruliana RulianaAfiliasi tidak diketahui1
Zaenuri ZaenuriState University of Semarang1
Enggar Niken LarasatiAfiliasi tidak diketahui1
Elisa Desi AsrianiAfiliasi tidak diketahui1
Ratna Novita SariAfiliasi tidak diketahui1
Nur HidayatiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujm/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">A Data Mining Approach to Wage Inequality Analysis in Indonesia: A Clustering Study Using Fuzzy C-Means</dc:title>
	<dc:creator xml:lang="en">Harwanti, Nur Achmey Selgi</dc:creator>
	<dc:creator xml:lang="en">Hendikawati, Putriaji</dc:creator>
	<dc:creator xml:lang="en">Sanusi, Ratna Nur Mustika</dc:creator>
	<dc:creator xml:lang="en">Pratama, Alfian Adi</dc:creator>
	<dc:subject xml:lang="en">Fuzzy C-Means Clustering</dc:subject>
	<dc:subject xml:lang="en">Regional Economy </dc:subject>
	<dc:subject xml:lang="en">Wage Analysis</dc:subject>
	<dc:description xml:lang="en">This study aims to cluster Indonesian provinces based on the average wage structure of workers across 17 economic sectors using the Fuzzy C-Means (FCM) method. The wage data underwent preprocessing steps including missing value imputation using the median, logarithmic transformation to reduce skewness, and Z-Score standardization to ensure uniform data scaling. The evaluation of the number of clusters and fuzziness values was conducted using the Silhouette coefficient and Fuzzy Partition Coefficient (FPC), with the best results achieved at three clusters and a fuzziness value of 1.3. Further analysis using Principal Component Analysis (PCA) provided visualization of the clusters, while radar charts illustrated wage characteristics by sector within each cluster. The clustering results reveal significant economic disparities among provinces: Cluster 1 consists of provinces with the highest wages dominated by high-value-added sectors such as mining and finance; Cluster 0 shows a balanced wage distribution reflecting a transitional economy; and Cluster 2 includes provinces with the lowest wages facing structural challenges. These findings offer a comprehensive overview of regional economic diversity in Indonesia and can serve as a basis for policy-making aimed at more equitable economic development.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-07-27</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/ujm/article/view/25755</dc:identifier>
	<dc:identifier>10.15294/ujm.v13i2.25755</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Mathematics; Vol. 13 No. 2 (2024): Unnes Journal of Mathematics Volume 2, 2024; 39-47</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v13i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujm/article/view/25755/5200</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Unnes Journal of Mathematics</dc:rights>
</oai_dc:dc>