CLASSIFICATION OF GENDER INEQUALITY IN INDONESIA: UNSUPERVISED AND SUPERVISED LEARNING APPROACHES
DOI:
https://doi.org/10.25077/jmua.15.1.108-122.2026Keywords:
Gender Inequality, Machine Learning, Regional ClassificationAbstract
Gender inequality in Indonesia is a multidimensional problem that has a wide impact on human development. This study aims to model and classify the level of gender inequality between provinces in Indonesia with a combined approach of unsupervised and supervised learning. Secondary data from 38 provinces in 2024 were analyzed using five methods: K-Means, Self-Organizing Map (SOM), hybrid SOM-KMeans, Support Vector Machine (SVM), and Logistic Regression. In the unsupervised approach, the SOM and SOM-KMeans methods show better cluster coherence than K-Means. In the supervised approach, the SVM method provides better classification performance compared to logistic regression. Overall, SVM was obtained with the highest accuracy, which was 89.47%, surpassing other methods. This research makes a methodological contribution to the use of machine learning for spatial-based gender inequality risk mapping, as well as implications for more precise and adaptive data-based policymaking.
References
[1] U. Nations, “Human Development Report 2025,” United Nations, May 2025. Accessed: Jul. 23, 2025. [Online]. Available: https://hdr.undp.org/content/human-development-report-2025
[2] Badan Pusat Statistik Indonesia, “Indeks Ketimpangan Gender (IKG) 2024.” Badan Pusat Statistik Indonesia, 2025.
[3] E. Tusianti, Kajian Penghitungan Indeks Ketimpangan Gender. Jakarta: BPS-Statistics Indonesia, 2020.
[4] I. Kertati, “Analisis Indeks Pembangunan Gender (IPG) Dan Indeks Pemberdayaan Gender (IDG) Kota Surakarta,” Public Service and Governance Journal, vol. 2, no. 1, pp. 01–11, Jan. 2021, doi: 10.56444/psgj.v2i1.597.
[5] A. V. Y. Sitorus, “Dampak Ketimpangan Gender terhadap IPM dan Kemiskinan di Indonesia. Jurnal Ekonomi dan Pembangunan,” Sosio Informa, vol. 2, no. 1, pp. 89–101, 2016.
[6] E. Duflo, “Women Empowerment and Economic Development,” Journal of Economic Literature, vol. 50, no. 4, pp. 1051–1079, Dec. 2012, doi: 10.1257/jel.50.4.1051.
[7] B. R. Lamichhane, M. Isnan, and T. Horanont, “Exploring machine learning trends in poverty mapping: A review and meta-analysis,” Science of Remote Sensing, vol. 11, p. 100200, Jun. 2025, doi: 10.1016/j.srs.2025.100200.
[8] A. M. Yolanda, A. Adnan, and A. A. Dwiputri, “Pemodelan Klasifikasi Pada Indeks Ketimpangan Gender (Ikg) Tahun 2020 Dengan Metode Naïve Bayes,” JKB, vol. 7, no. 1, pp. 22–31, Jul. 2022, doi: 10.37306/kkb.v7i1.118.
[9] S. D. Raihannabil, H. M. A. Ilyas, H. N. Shafira, M. A. Riani, N. N. Hastin, and T. K. H. Siregar, “Perbandingan Agglomerative Nesting dan K-Means untuk Klasterisasi Ketimpangan Gender berdasarkan Dimensi Kesehatan Reproduksi,” Seminar Nasional Official Statistics, vol. 2024, no. 1, Art. no. 1, Nov. 2024, doi: 10.34123/semnasoffstat.v2024i1.1977.
[10] J. Han, M. Kamber, and J. Pei, Data mining: Concepts and techniques. Morgan Kaufmann, 2012.
[11] T. Kohonen, Self-organizing maps. Springer Science & Business Media, 2012.
[12] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, vol. 103. in Springer Texts in Statistics, vol. 103. New York, NY: Springer New York, 2013. doi: 10.1007/978-1-4614-7138-7.
[13] D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression. John Wiley & Sons, 2013.
[14] H. Dalianis, “Evaluation Metrics and Evaluation,” in Clinical Text Mining: Secondary Use of Electronic Patient Records, H. Dalianis, Ed., Cham: Springer International Publishing, 2018, pp. 45–53. doi: 10.1007/978-3-319-78503-5_6.
[15] P. Zeng, F. Sun, Y. Liu, Y. Wang, G. Li, and Y. Che, “Mapping future droughts under global warming across China: A combined multi-timescale meteorological drought index and SOM-Kmeans approach,” Weather and Climate Extremes, vol. 31, p. 100304, Mar. 2021, doi: 10.1016/j.wace.2021.100304.
[16] A. M. Yolanda, A. Adnan, N. E. Goldameir, and F. A. Rizalde, “The comparison of accuracy on classification data with machine learning algorithms (Case study: Human development index by regency/city in Indonesia 2020),” AIP Conference Proceedings, vol. 2536, no. 1, p. 060002, May 2023, doi: 10.1063/5.0118720.
[17] M. I. Sabila and F. R. Sutikno, “Pengembangan Infrastruktur Hijau Dalam Pembangunan Green City di Kota Kediri,” Planning for Urban Region and Environment Journal (PURE), vol. 13, no. 4, pp. 195–206, Oct. 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Departemen Matematika dan Sains Data FMIPA UNAND
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All articles published in Jurnal Matematika UNAND (JMUA) are open access and licensed under the Creative Commons Attribution-ShareAlike (CC BY-SA) license. This ensures that the content is freely available to all users and can be shared and adapted, provided appropriate credit is given and any adaptations are distributed under the same license.
Copyright Holder
The copyright of all articles published in Jurnal Matematika UNAND is held by the Departemen Matematika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Universitas Andalas (UNAND). This applies to all published versions, including the HTML and PDF formats of the articles.
Author Rights
While the Departemen Matematika dan Sains Data FMIPA UNAND holds the copyright for all published content, authors retain important rights under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA). This license grants authors and users the following rights:
- Reuse: Authors can reuse and distribute their work for any lawful purpose, including sharing on personal websites, institutional repositories, or in subsequent publications.
- Attribution and Adaptation: Authors and others may remix, adapt, and build upon the published work for any purpose, even commercially, as long as proper credit is given to the original authors, and any derivative works are distributed under the same CC BY-SA license.
Creative Commons License (CC BY-SA)
Under the terms of the CC BY-SA license, users are free to:
- Share: Copy and redistribute the material in any medium or format.
- Adapt: Remix, transform, and build upon the material for any purpose, even commercially.
However, the following conditions apply:
- Attribution: Users must give appropriate credit to the original author(s) and Departemen Matematika dan Sains Data FMIPA UNAND, provide a link to the license, and indicate if changes were made. Attribution must not imply endorsement by the author or the journal.
- ShareAlike: If users remix, transform, or build upon the material, they must distribute their contributions under the same license as the original.
For more information about the CC BY-SA license, please visit the Creative Commons website.
Third-Party Content
If authors include third-party material (such as figures, tables, or images) that is not covered by a Creative Commons license, they must obtain the necessary permissions for reuse and provide proper attribution. Authors are required to ensure that any third-party content complies with open-access licensing requirements or includes permissions for redistribution under similar terms.
Copyright and Licensing Information Display
The copyright and licensing terms will be clearly displayed on each article's landing page, as well as within the full-text versions (HTML and PDF) of all published articles.
No "All Rights Reserved"
As an open-access journal, JMUA does not use "All Rights Reserved" policies. Instead, the CC BY-SA license ensures that the works remain accessible and reusable for a wide audience while still protecting both the authors' and the copyright holder's rights.
Â
