A Comparative Study of Students Graduation Analysis Using Classification Methods in Undergraduate Electrical Engineering Tidar University
Abstract
This research aimed to classify achievement factors for electrical engineering students at Tidar University using K-Means and Agglomerative Clustering classification algorithms. The goal was to understand if any parameters influence high-achieving student performance. The Indonesian government and private sector for university students provide significant education funds. Student scholarships are awarded based primarily on GPA and entry path, overburdening staff and causing confusion during distribution to eligible recipients. A system was needed to accommodate additional eligible criteria. The researcher selected factors to identify engineering student performance, including school origin, entry path, tuition fees, and GPA. These inputs could determine graduation status. The results compared calculation methods based on collected data accuracy, processing times, and characterizing clustered data to determine the best classification method. Agglomerative Hierarchical Clustering performed better. Accuracy testing on 600 training data points yielded 73.94% for improved K-means and 90.42% for AHC. The Average processing time was 674.92 seconds for improved K-means and 554.35 seconds for AHC. Silhouette testing also characterized calculation methods, with improved K-means scoring best at 0.654 and AHC at 0.787 using two clusters.
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B. Bertaccini, S. Bacci, and A. Petrucci, “A graduates’ satisfaction index for the evaluation of the university overall quality,” Socio-Economic Planning Sciences, vol. 73, p. 100875, Feb. 2021, doi: https://doi.org/10.1016/j.seps.2020.100875
BAN-PT, Buku VI Matriks Penilaian Instrumen Akreditasi Program Studi Sarjana. Jakarta, 2008.
C. Aina and G. Casalone, “Early labor market outcomes of university graduates: Does time to degree matter,” Socioecon. Plann. Sci., p. 100822, Mar. 2020. doi: 10.1016/j.seps.2020. 100822
X. Xu, J. Wang, H. Peng, and R. Wu, “Prediction of academic performance associated with internet usage behaviors using machine learning algorithms,” Comput. Human Behav., vol. 98, pp. 166–173, Sep. 2019. doi:10.1016/j.chb.2019.04.015
R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate students’ performance using educational data mining,” Computers & Education, vol. 113, pp. 177–194, Oct. 2019, doi: https://doi.org/10.1016/j.compedu.2017.05.007
J. M. Borwn, “Predicting Math Test Scores Using K- Nearest Neighbor,” IEEE Integr. STEM Conf., 2017. doi:10.1109/ISECon.2017. 7910221
S. A. D. Syarif, “Trending Topic Prediction by Optimizing K-Nearest Neighbor Algorithm,” IEEE, 2020.
D. Kabakchieva, “Student Performance Prediction by Using Data Mining Classification Algorithms,” Int. J. Comput. Sci. Manag. Res., vol. 1, no. 4, pp. 686–690, 2020. doi: 10.5772/intechopen.91449
Y. Palumpun and S. N. Alam, “Pengelompokan Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Means,” no. November, pp. 98–102, 2019.
R. Rosmini, A. Fadlil, and S. Sunardi, “Implementasi Metode K-Means Dalam Pemetaan Kelompok Mahasiswa Melalui Data Aktivitas Kuliah,” It J. Res. Dev., vol.3, no. 1, p.22,2018.doi:10.25299/itjrd.2018.vol3 (1).1773
H. Sunaryanto, M. A. Hasan, and G. Guntoro, “Classification Analysis of Unilak Informatics Engineering Students Using Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN),” IT Journal Research and Development, vol. 7, no. 1, pp. 36–42, Aug. 2022, doi: https://doi.org/10.25299/itjrd.2022.8912
H. Februariyanti and D. B. Santoso, “Hierarchical Agglomerative Clustering Untuk Pengelompokan Skripsi Mahasiswa,” Pattern Recognition, 2019, doi: 10.1016/0031-3203(79)90049-9.
Chen Guang-ping and Wang Wen-peng, “An improved K-means algorithm with meliorated initial center,” Jul. 2019, doi: https://doi.org/10.1109/iccse.2019.6295047
Y. H. Chrisnanto and G. Abdullah, “The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI),” Matrix : Jurnal Manajemen Teknologi dan Informatika, vol. 11, no. 1, pp. 26–35, Mar. 2021, doi: https://doi.org/10.31940/matrix.v11i1.2330
Bora, D.J., dan Gupta, A.K., 2019, Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2014, 2501-2506.
Y. Christian and J. Jimmy, “Perancangan Model Predıksı Performa Akademık Mahasıswa Menggunakan Algorıtma K-Means Clusterıng (Studı Kasus: Unıversıtas Xyz),” CoMBInES - Conference on Management, Business, Innovation, Education and Social Sciences, vol. 1, no. 1, pp. 643–649, Mar. 2021.
Sri Dewi, Sarjon Defit, and Y. Yunus, “Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means (Studi Kasus SMP Pembangunan Laboratorium UNP),” Jurnal Sistim Informasi dan Teknologi, Sep. 2020, doi: https://doi.org/10.37034/jsisfotek.v3i1.98
D. Exasanti and A. Jananto, “Analisa Hasil Pengelompokan Wilayah Kejadian Non-Kebakaran Menggunakan Agglomerative Hierachical Clustering di Semarang,” Jurnal Tekno Kompak, vol. 15, no. 2, p. 63, Aug. 2021, doi: https://doi.org/10.33365/jtk.v15i2.1166
L. Zahrotun, “Analisis Pengelompokan Jumlah Penumpang Bus Trans Jogja Menggunakan Metode Clustering K-Means Dan Agglomerative Hierarchical Clustering (AHC),” Jurnal Informatika, vol. 9, no. 1, Jan. 2019, doi: https://doi.org/10.26555/jifo.v 9i1.a2045
K. K. Raihana, S. M. K. Rishad, T. Sadia, S. Ahmed, M. S. Alam, and R. M. Rahman, “Identifying Flood Prone Regions In Bangladesh By Clustering,” Proc. - 17th IEEE/ACIS Int. Conf. Comput. Inf. Sci. ICIS 2018, pp. 556–561, 2018, doi: 10.1109/ICIS.2018.8466533.
Z. Arifin, S. Santosa, and M. A. Soeleman, “Klasterisasi Genre Cerpen Kompas Menggunakan Agglomerative Hierarchical Clustering- Single Linkage,” J. Cyberku, vol. 13, no. 2, pp. 2–2, Dec. 2019.
G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications, ASA- SIAM Series on Statistics and Applied Probability. Society for Industrial and Applied Mathematics, Alexandria, VA, 2007.
I. H. Witten, E. Frank, dan M. A. Hall, Data mining: practical machine learning tools and methods, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011. doi:10.1016/C2009-0-19715-5
R. A. Johnson dan D. W. Wlchern, Applied Multivarate Statistical Analysis (Sixth Edition), 6 ed. Pearson Prentice Hall : New Jersey, 2007.doi: 10.1007/978-3-642-17229-8
A. Struyf, M. Hubert, and P. J. Rousseeuw, “Clustering in an Object-Oriented Environment,” Journal of Statictical Software, vol 1, Issue 4, pp. 1-30, 1997. doi: 10.18637/jss.v001.i04
DOI: https://doi.org/10.15408/jti.v17i1.32132 Abstract - 0 PDF - 0
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