Mapping the Potential of Prospective New Students Utilizing K-Means and Fuzzy C-Means Clustering
DOI:
https://doi.org/10.47134/ijsl.v6i1.481Keywords:
Clustering, K-Means, Fuzzy C-Means, Student AdmissionAbstract
UIN Walisongo Semarang has implemented outreach initiatives to engage prospective students, but these endeavors often lack data-informed approaches. This research intends to analyze the potential of incoming students through K-Means and Fuzzy C-Means (FCM) clustering techniques. The dataset comprises admission information from 2022 to 2024, focusing on five key factors: gender, admission pathway, study program, school type, and geographic origin. Data preprocessing was performed before conducting the clustering analysis. The Elbow Method and Silhouette Score were utilized to identify the optimal K for K-Means, whereas the Fuzzy Partition Coefficient and Xie-Beni Index were applied for FCM. Findings indicate that K-Means generated more distinct cluster boundaries, while FCM provided adaptability with overlapping clusters. Principal Component Analysis and the Davies-Bouldin Index were employed to facilitate the assessment. The mapping results are displayed by faculty, showcasing regional patterns and student demographics. This research establishes a data-driven basis for UIN Walisongo's strategic recruitment and admissions strategies.
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Abdullah, D., Susilo, S., Ahmar, A. S., Rusli, R., & Hidayat, R. (2022). The Application of K-Means Clustering for Province Clustering in Indonesia of The Risk of The COVID-19 Pandemic Based on COVID-19 Data. Quality and Quantity, 56(3), 1283–1291. https://doi.org/10.1007/s11135-021-01176-w. DOI: https://doi.org/10.1007/s11135-021-01176-w
Achmad, F. S., & Widiastuti, I. (2024). Educator Recruitment and Selection Strategies in Private Universities in Indonesia : Policies and Practices. AL-ISHLAH: Jurnal Pendidikan, 16(4), 4419–4429. https://doi.org/10.35445/alishlah.v16i4.6000. DOI: https://doi.org/10.35445/alishlah.v16i4.6000
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The K-Means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics (Switzerland), 9(8), 1–12. https://doi.org/10.3390/electronics9081295. DOI: https://doi.org/10.3390/electronics9081295
Al-Alawi, M. K., Jaddoa, A., Cugley, J., & Hassanin, H. (2024). A Novel Enhanced SOC Estimation Method for Lithium-ion Battery Cells using Cluster-based LSTM Models and Centroid Proximity Selection. Journal of Energy Storage, 97, 1–15. https://doi.org/10.1016/j.est.2024.112866. DOI: https://doi.org/10.1016/j.est.2024.112866
Amartha, M. R. (2020). Penyebaran Mahasiswa Baru Menggunakan Metode Fuzzy C-Means Untuk Mencari Daerah Promosi Yang Potensial Distribution of New Students Using the Fuzzy C-Means Method To Look for Potential Promotional Areas. Journal of Information Technology and Computer Science (INTECOMS), 3(2),102-112. https://doi.org/10.31539/intecoms.v3i2.1524. DOI: https://doi.org/10.31539/intecoms.v3i2.1524
Ampadu, Y. B. (2023). Handling Big Data in Education: A Review of Educational Data Mining Techniques for Specific Educational Problems. AI, Computer Science and Robotics Technology, 2(April), 1–16. https://doi.org/10.5772/acrt.17. DOI: https://doi.org/10.5772/acrt.17
Chin, S., & Collins, J. E. (2024). Clustering Methods in Rheumatic and Musculoskeletal Diseases Research: An Educational Guide to Best Research Practices. The Journal of Rheumatology, 51(1), 1160–1168. https://doi.org/10.3899/jrheum.2024-0519. DOI: https://doi.org/10.3899/jrheum.2024-0519
Chong, B. (2021). K-Means Clustering Algorithm: A Brief Review. Academic Journal of Computing & Information Science, 4(5), 37–40. https://doi.org/10.25236/ajcis.2021.040506. DOI: https://doi.org/10.25236/AJCIS.2021.040506
Dubey, A. K., Gupta, U., & Jain, S. (2018). Comparative Study of K-Means and Fuzzy C-Means Algorithms on The Breast Cancer Data. International Journal on Advanced Science, Engineering and Information Technology, 8(1), 18–29. https://doi.org/10.18517/ijaseit.8.1.3490. DOI: https://doi.org/10.18517/ijaseit.8.1.3490
Et-Taleby, A., Boussetta, M., & Benslimane, M. (2020). Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image. International Journal of Photoenergy, 2020, 1–7. https://doi.org/10.1155/2020/6617597. DOI: https://doi.org/10.1155/2020/6617597
Gatea, A. N., & Al-asadi, H. A. A. (2024). Soft Clustering Techniques : An In-Depth Analysis of GMM and FCM Algorithms and Comparative Performance. Journal of Basrah Researches (Sciences), 50(2), 223–239. https://doi.org/10.56714/bjrs.50.2.19. DOI: https://doi.org/10.56714/bjrs.50.2.19
Gewers, F. L., Ferreira, G. R., De Arruda, H. F., Silva, F. N., Comin, C. H., Amancio, D. R., & Costa, L. D. F. (2021). Principal Component Analysis: A Natural Approach to Data Exploration. ACM Computing Surveys, 54(4), 1–33. https://doi.org/10.1145/3447755. DOI: https://doi.org/10.1145/3447755
Habsy, B. A., Zakirah, A., Rahmah, M. A., & Nafisah, C. A. (2023). Implementasi Teori Kognitif dan Konstruktivisme dalam Pembelajaran bagi Peserta Didik. Tsaqofah, 4(1), 326–342. https://doi.org/10.58578/tsaqofah.v4i1.2182. DOI: https://doi.org/10.58578/tsaqofah.v4i1.2182
Hairani, H., Susilowati, D., Puji Lestari, I., Marzuki, K., & Mardedi, L. Z. A. (2022). Segmentasi Lokasi Promosi Penerimaan Mahasiswa Baru Menggunakan Metode RFM dan K-Means Clustering. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 275–282. https://doi.org/10.30812/matrik.v21i2.1542. DOI: https://doi.org/10.30812/matrik.v21i2.1542
Han, H. (2023). Fuzzy Clustering Algorithm for University Students’ Psychological Fitness and Performance Detection. Heliyon, 9(8), e18550. https://doi.org/10.1016/j.heliyon.2023.e18550. DOI: https://doi.org/10.1016/j.heliyon.2023.e18550
Hassan, A. A. H., Shah, W. M., Othman, M. F. I., & Hassan, H. A. H. (2020). Evaluate the Performance of K-Means and the fuzzy C-Means Algorithms to Formation Balanced Clusters in Wireless Sensor Networks. International Journal of Electrical and Computer Engineering, 10(2), 1515–1523. https://doi.org/10.11591/ijece.v10i2.pp1515-1523. DOI: https://doi.org/10.11591/ijece.v10i2.pp1515-1523
Huang, Z. Y., Lin, S., Long, L. L., Cao, J. Y., Luo, F., Qin, W. C., … Gregersen, H. (2020). Predicting the Morbidity of Chronic Obstructive Pulmonary Disease Based on Multiple Locally Weighted Linear Regression Model with K-Means Clustering. International Journal of Medical Informatics, 139(April), 104141. https://doi.org/10.1016/j.ijmedinf.2020.104141. DOI: https://doi.org/10.1016/j.ijmedinf.2020.104141
Ikotun, A. M., Almutari, M. S., & Ezugwu, A. E. (2021). K‐Means‐Based Nature‐Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions. Applied Sciences (Switzerland), 11(23). https://doi.org/10.3390/app112311246. DOI: https://doi.org/10.3390/app112311246
Jia, H., & Cheung, Y. M. (2018). Subspace Clustering of Categorical and Numerical Data with An Unknown Number of Clusters. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3308–3325. https://doi.org/10.1109/TNNLS.2017.2728138. DOI: https://doi.org/10.1109/TNNLS.2017.2728138
Krasnov, D., Davis, D., Malott, K., Chen, Y., Shi, X., & Wong, A. (2023). Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection. Entropy, 25(7), 1–14. https://doi.org/10.3390/e25071021. DOI: https://doi.org/10.3390/e25071021
Kusnadi, K., & Ningrum, S. U. D. (2024). National Politics Dr. Soetomo for Education in Indonesia: An Alternative to Strengthen the Pillars of Education. Jurnal Humanitas: Katalisator Perubahan Dan Inovator Pendidikan, 10(2), 206–219. https://doi.org/10.29408/jhm.v10i2.25169. DOI: https://doi.org/10.29408/jhm.v10i2.25169
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. (2018). Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering. IEEE Transactions on Fuzzy Systems, 26(5), 3027–3041. https://doi.org/10.1109/TFUZZ.2018.2796074. DOI: https://doi.org/10.1109/TFUZZ.2018.2796074
Ling, L. S., & Weiling, C. T. (2025). Enhancing Segmentation: A Comparative Study of Clustering Methods. IEEE Access, 13(January). https://doi.org/10.1109/ACCESS.2025.3550339. DOI: https://doi.org/10.1109/ACCESS.2025.3550339
Liu, H. (2023). Modern University Academy System: A Useful Exploration of Promoting the Cultivation of Innovative Abilities of College Students. International Journal of New Developments in Education, 5(22), 134–140. https://doi.org/10.25236/ijnde.2023.052223. DOI: https://doi.org/10.25236/IJNDE.2023.052223
Marbouti, F., Ulas, J., & Wang, C. H. (2021). Academic and Demographic Cluster Analysis of Engineering Student Success. IEEE Transactions on Education, 64(3), 261–266. https://doi.org/10.1109/TE.2020.3036824. DOI: https://doi.org/10.1109/TE.2020.3036824
Miraftabzadeh, S. M., Colombo, C. G., Longo, M., & Foiadelli, F. (2023). K-Means and Alternative Clustering Methods in Modern Power Systems. IEEE Access, 11(November), 119596–119633. https://doi.org/10.1109/ACCESS.2023.3327640. DOI: https://doi.org/10.1109/ACCESS.2023.3327640
Motlagh, O., Berry, A., & O’Neil, L. (2019). Clustering of Residential Electricity Customers using Load Time Series. Applied Energy, 237(August 2018), 11–24. https://doi.org/10.1016/j.apenergy.2018.12.063. DOI: https://doi.org/10.1016/j.apenergy.2018.12.063
Mughnyanti, M., Efendi, S., & Zarlis, M. (2020). Analysis of Determining Centroid Clustering X-Means Algorithm with Davies-Bouldin Index Evaluation. IOP Conference Series: Materials Science and Engineering, 725(1). https://doi.org/10.1088/1757-899X/725/1/012128. DOI: https://doi.org/10.1088/1757-899X/725/1/012128
Nabila, S. P., Ulinnuha, N., & Yusuf, A. (2021). Model Prediksi Kelulusan Tepat Waktu dengan Metode Fuzzy C-Means dan K-Nearest Neighbors menggunakan Data Registrasi Mahasiswa. Network Engineering Research Operation, 6(1), 39. https://doi.org/10.21107/nero.v6i1.199. DOI: https://doi.org/10.21107/nero.v6i1.199
Pampuch, L. A., Negri, R. G., Loikith, P. C., & Bortolozo, C. A. (2023). A Review on Clustering Methods for Climatology Analysis and Its Application over South America. International Journal of Geosciences, 14(09), 877–894. https://doi.org/10.4236/ijg.2023.149047. DOI: https://doi.org/10.4236/ijg.2023.149047
Patel, E., & Kushwaha, D. S. (2020). Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model. Procedia Computer Science, 171(2019), 158–167. https://doi.org/10.1016/j.procs.2020.04.017. DOI: https://doi.org/10.1016/j.procs.2020.04.017
Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., … Tang, Q. (2022). Research and Application of Machine Learning for Additive Manufacturing. Additive Manufacturing, 52(October 2021). https://doi.org/10.1016/j.addma.2022.102691. DOI: https://doi.org/10.1016/j.addma.2022.102691
Qorib, F. (2024). Tantangan dan Peluang Kolaborasi antara Perguruan Tinggi dan Masyarakat dalam Program Pengabdian di Indonesia. Journal of Indonesian Society Empowerment, 2(2), 46–57. https://doi.org/10.61105/jise.v2i2.119. DOI: https://doi.org/10.61105/jise.v2i2.119
Rajkumar, K. V., Yesubabu, A., & Subrahmanyam, K. (2019). Fuzzy clustering and Fuzzy C-Means partition cluster analysis and validation studies on a subset of CiteScore dataset. International Journal of Electrical and Computer Engineering, 9(4), 2760–2770. https://doi.org/10.11591/ijece.v9i4.pp2760-2770. DOI: https://doi.org/10.11591/ijece.v9i4.pp2760-2770
Ran, X., Zhou, X., Lei, M., Tepsan, W., & Deng, W. (2021). A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots. Applied Sciences, 11(23). https://doi.org/10.3390/app112311202. DOI: https://doi.org/10.3390/app112311202
Rayala, V., & Kalli, S. R. (2021). Big Data Clustering using Improvised Fuzzy C-Means Clustering. Revue d’Intelligence Artificielle, 34(6), 701–708. https://doi.org/10.18280/RIA.340604. DOI: https://doi.org/10.18280/ria.340604
Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). Clustering Algorithms: A Comparative Approach. In PLoS ONE, 14. https://doi.org/10.1371/journal.pone.0210236. DOI: https://doi.org/10.1371/journal.pone.0210236
Sadeghi, M., Casey, P., Carranza, E. J. M., & Lynch, E. P. (2024). Principal Components Analysis and K-Means Clustering of Till Geochemical Data: Mapping and Targeting of Prospective Areas for Lithium Exploration in Västernorrland Region, Sweden. Ore Geology Reviews, 167(January), 106002. https://doi.org/10.1016/j.oregeorev.2024.106002. DOI: https://doi.org/10.1016/j.oregeorev.2024.106002
Sahin, A. (2018). Critical Issues in Islamic Education Studies: Rethinking Islamic and Western Liberal Secular Values of Education. Religions, 9(11). https://doi.org/10.3390/rel9110335. DOI: https://doi.org/10.3390/rel9110335
Sammouda, R., & El-Zaart, A. (2021). An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/4553832. DOI: https://doi.org/10.1155/2021/4553832
Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A Quantitative Discriminant Method of Elbow Point for The Optimal Number of Clusters in Clustering Algorithm. Eurasip Journal on Wireless Communications and Networking, 2021(1). https://doi.org/10.1186/s13638-021-01910-w. DOI: https://doi.org/10.1186/s13638-021-01910-w
Singh, J., & Singh, D. (2024). A Comprehensive Review of Clustering Techniques in Artificial Intelligence for Knowledge Discovery: Taxonomy, Challenges, Applications and Future Prospects. Advanced Engineering Informatics, 62. https://doi.org/10.1016/j.aei.2024.102799. DOI: https://doi.org/10.1016/j.aei.2024.102799
Yin, Y., Yong, Y., Qi, S., Yang, K., & Lan, Y. (2023). Cluster Analyses of Tropical Cyclones with Genesis in the South China Sea Based on K-Means Method. Asia-Pacific Journal of Atmospheric Sciences, 59(4), 433–446. https://doi.org/10.1007/s13143-023-00322-8. DOI: https://doi.org/10.1007/s13143-023-00322-8
Zaib, R., & Ourabah, O. (2023). Large Scale Data Using K-Means. Mesopotamian Journal of Big Data, 2023(2023), 36–45. https://doi.org/10.58496/mjbd/2023/006. DOI: https://doi.org/10.58496/MJBD/2023/006
Žalėnienė, I., & Pereira, P. (2021). Higher Education For Sustainability: A Global Perspective. Geography and Sustainability, 2(2), 99–106. https://doi.org/10.1016/j.geosus.2021.05.001. DOI: https://doi.org/10.1016/j.geosus.2021.05.001
Zarkasyi, M. R. (2024). Organizational Commitment of the Millennial Generation Workforce in Indonesian Higher Education: Systematic Literature Review. AL-ISHLAH: Jurnal Pendidikan, 16(2), 1097–1107. https://doi.org/10.35445/alishlah.v16i2.5151. DOI: https://doi.org/10.35445/alishlah.v16i2.5151
Zhang, Y., Wu, X., Xu, J., Ning, Z., & Han, X. (2025). Clustering Analysis of Yue Opera Character Tone Trends Based on Quantum Particle Swarm Optimization for Fuzzy C-Means. PLoS ONE, 20(1 January), 1–25. https://doi.org/10.1371/journal.pone.0313065. DOI: https://doi.org/10.1371/journal.pone.0313065
Zhou, S., Li, D., Zhang, Z., & Ping, R. (2021). A New Membership Scaling Fuzzy C-Means Clustering Algorithm. IEEE Transactions on Fuzzy Systems, 29(9), 2810–2818. https://doi.org/10.1109/TFUZZ.2020.3003441. DOI: https://doi.org/10.1109/TFUZZ.2020.3003441
