Higher Education Institution Clustering Based on Key Performance Indicators using Quartile Binning Method
DOI:
https://doi.org/10.30812/matrik.v24i1.4244Keywords:
Descriptive Analytics, Higher Education Institution, Key Performance Indicators, Quartile-Based Clustering, Statistical AnalysisAbstract
The Key Performance Indicators of Higher Education Institutions (KPI-HEIs) are a crucial component of the internal quality assurance system that supports the achievement of excellence status for higher education institutions. Many private higher education institutions face challenges in independently analyzing the key performance assessment indicators of Private Higher Education Institutions (PHEIs), which often require complex methodological approaches and specialized expertise. The research aims to cluster PHEIs based on achieving key performance indicators (KPIs). Research the method used descriptive statistical methods and quartile binning techniques to analyze and cluster data based on the achievement of KPI-HEIs. The research results, based on descriptive statistical analysis, identified outliers in eight KPI-HEIs, along with a dominance of zero values in KPI 1, KPI 2, KPI 6, KPI 7, and KPI 8, with the highest proportion reaching 90.91% for KPI 8. Based on these findings, clustering using the quartile binning method resulted in four clusters of PHEIs based on KPIs: Cluster 1 consists of 19 institutions with poor, Cluster 2 consists of 14 institutions with fair achievement, Cluster 3 consists of 16 institutions with good achievement, and Cluster 4 consists of 17 institutions with very good achievement, which can serve as examples for other institutions. This research concludes that the quartile binning method successfully categorized private higher education institutions based on their achievement of KPIs into four clusters: poor, fair, good, and very good. This outcome demonstrates the effectiveness of the method in understanding the performance distribution of these institutions. It provides valuable insights for stakeholders to develop data-driven strategies aimed at enhancing educational quality.
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References
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Tinggi yang Memiliki Daya Adaptasi Terhadap Perubahan Kebutuhan Pengguna secara Cepat dan Sering,†Jurnal Al-Azhar
Indonesia Seri Sains Dan Teknologi, vol. 5, no. 3, April, p. 121, 2020, https://doi.org/10.36722/sst.v5i3.372.
[4] V. S. Fatmawaty, I. Riadi, and H. Herman, “Klasterisasi Perguruan Tinggi LLDIKTI V Berdasarkan Indikator Kinerja Utama
dan PDDIKTI Menggunakan K-Means Clustering,†Jurnal Media Informatika Budidarma, vol. 8, no. 2, April, pp. 878–888,
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https://doi.org/10.12785/ijcds/160185.
[6] K. Umbleja, M. Ichino, and H. Yaguchi, “Hierarchical conceptual clustering based on quantile method for identifying microscopic
details in distributional data,†Springer : Advances in Data Analysis and Classification, vol. 15, no. 2, pp. 407–436,
2020, https://doi.org/10.1007/s11634-020-00411-w.
[7] S. Nurtanti and P. H. Husna, “Analisis Tingkat Pengetahuan Dan Ansietas Tentang Vaksinasi Covid 19 Pada Kader Kesehatan,â€
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Fisika Matematika I Materi Vektor,†Jurnal Pendidikan Mipa, vol. 12, no. 1, pp. 13–19, 2022, https://doi.org/10.37630/
jpm.v12i1.528.
[10] C. S. K. Dash, A. K. Behera, S. Dehuri, and A. Ghosh, “An outliers detection and elimination framework in classification task
of data mining,†Decision Analytics Journal, vol. 6, no. 6, January, p. 8, 2023, https://doi.org/10.1016/j.dajour.2023.100164.
[11] E. D. B. Sudewo, M. K. Biddinika, and A. Fadlil, “DenseNet Architecture for Efficient and Accurate Recognition of Javanese
Script Hanacaraka Character,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, March,
pp. 453–464, 2024, https://doi.org/10.30812/matrik.v23i2.3855.
[12] I. Riadi, A. Yudhana, and M. R. Djou, “Optimization of Population Document Services in Villages using Naive Bayes and k-NN
Method,†International Journal of Computing and Digital Systems, vol. 15, no. 1, pp. 127–138, 2024, https://doi.org/10.12785/
ijcds/150111.
[13] D. Faroek, Rusydi Umar, and Imam Riadi, “Classification Based on Machine Learning Methods for Identification of Image
Matching Achievements,†Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 198–206, 2022, https:
//doi.org/10.29207/resti.v6i2.3826.
[14] V. Magar, D. Ruikar, S. Bhoite, and R. Mente, “Innovative Inter Quartile Range-based Outlier Detection and Removal Technique
for Teaching Staff Performance Feedback Analysis,†Journal of Engineering Education Transformations, vol. 37, no. 3, pp.
176–184, 2024, https://doi.org/10.16920/jeet/2024/v37i3/24013.
[15] T. Z. Dessiaming, S. Anraeni, and S. Pomalingo, “College Academic Data Analysis Using Data Visualization,†Jurnal Teknik
Informatika (Jutif), vol. 3, no. 5, pp. 1203–1212, 2022, https://doi.org/10.20884/1.jutif.2022.3.5.310.
[16] M.Wahyuni, Statistik Deskriptif Untuk Penelitian Olah Data Manual dan SPSS versi 25. Bintang Pustaka Madani Yogyakarta,
2020, no. 1.
[17] M. Bansal, M. Kumar, M. Sachdeva, and A. Mittal, “Transfer learning for image classification using VGG19: Caltech-101
image data set,†Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 4, April, pp. 3609–3620, 2023,
https://doi.org/10.1007/s12652-021-03488-z.
[18] I. Riadi, H. Herman, F. Fitriah, and S. Suprihatin, “Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and
Medium Enterprises,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 1, pp. 169–182,
2023, https://doi.org/10.30812/matrik.v23i1.3363.
[19] S. Darmawan, Statistik Deskriptif. Penerbit Gava Media, 2023, no. 1.
[20] G. Navas-Palencia, “Optimal binning: mathematical programming formulation,†arvix, vol. 3, no. 8, December, pp. 1–22, 2022,
https://doi.org/10.48550/arXiv.2001.08025.
[21] A. ur Rehman and S. B. Belhaouari, “Unsupervised outlier detection in multidimensional data,†Journal of Big Data, vol. 8, no.
1, June, p. 80, 2021, https://doi.org/10.1186/s40537-021-00469-z.
[22] O. R. Y. Pradana, “Menentukan Letak Kuartil Pada Data Tunggal Menggunakan Metode Penjumlahan Satu,†Indonesia Journal
of Education and Learning Mathematics, vol. 1, no. 1, Desember, pp. 6–8, 2020.
[23] R. Prieto Curiel, C. Cabrera Arnau, M. Torres Pinedo, H. Gonz´alez Ram´ırez, and S. R. Bishop, “A continuous binning for
discrete, sparse and concentrated observations,†MethodsX, vol. 7, no. 7, Juli, p. 100709, 2020, https://doi.org/10.1016/j.mex.
2019.10.020.
[24] D. Vinsensia, S. Amri, J. Sihotang, and H. T. Sihotang, “New Method for Identification and Response to Infectious Disease
Patterns Based on Comprehensive Health Service Data,†vol. 23, no. 3, pp. 583–592, 2024, https://doi.org/10.30812/matrik.
v23i3.4000.
[25] M. Waskom, “Seaborn: Statistical Data Visualization,†Journal of Open Source Software, vol. 6, no. 6, April, p. 3021, 2021,
https://doi.org/10.21105/joss.03021.
[26] S. Rawat, A. Rawat, D. Kumar, and A. S. Sabitha, “Application of machine learning and data visualization techniques for
decision support in the insurance sector,†International Journal of Information Management Data Insights, vol. 1, no. 2, March,
p. 100012, 2021, https://doi.org/10.1016/j.jjimei.2021.100012.
[27] A. Fadlil, I. Riadi, and F. Andrianto, “Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-
Tuning,†International Journal of Computing and Digital Systems, vol. 16, no. 1, July, pp. 159–171, 2024, https://doi.org/
10.12785/ijcds/160113.
[28] T. Jambudi and S. Gandhi, “An Effective Initialization Method Based on Quartiles for the K-means Algorithm,†Indian Journal
Of Science And Technology, vol. 15, no. 35, pp. 1712–1721, 2022, https://doi.org/10.17485/ijst/v15i35.714.
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2024-11-14
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V. S. Fatmawaty, I. Riadi, and H. Herman, “Higher Education Institution Clustering Based on Key Performance Indicators using Quartile Binning Method”, MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 24, no. 1, pp. 141–154, Nov. 2024, doi: 10.30812/matrik.v24i1.4244.
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