Rought Set: Effective Method for Determining Scholarship Recipients

Authors

Silfia Andin , Sarjon Defit

DOI:

10.29303/jppipa.v10i4.7088

Published:

2024-04-25

Issue:

Vol. 10 No. 4 (2024): April

Keywords:

KIP college, Knowledge, Rules, Rought set scholarships

Research Articles

Downloads

How to Cite

Andin, S., & Defit, S. (2024). Rought Set: Effective Method for Determining Scholarship Recipients. Jurnal Penelitian Pendidikan IPA, 10(4), 1624–1632. https://doi.org/10.29303/jppipa.v10i4.7088

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Abstract

Every year, higher education institutions receive a KIP Tuition scholarship quota that has been determined by Ristek Dikti through LLDIKTI which is given during the new student admissions process. The process of determining recipients is carried out manually resulting in inaccurate scholarship recipients being selected and the selection results may not be the same based on those who participated in making the decision. This research is motivated by the need for an algorithm for determining prospective scholarship recipients that is appropriate and effective because the recipient selection process often takes a long time because many high school and equivalent students register so that they exceed the quota limit while the quota given is limited. This research aims to use a system for scholarship recipients and provide rules and knowledge, namely rough set Theory and adapted to the Rosetta application, using prospective student data during the selection process for new students who apply for the KIP Kuliah scholarship in the 2020/2021 academic year. The resulting decision is the KIP Opportunity which consists of 4 (four) attributes, including parents' income, housing status, dependents, and parental status. The results of this research using sample data from 12 people produced 6 (six) rules and knowledge of 26 rules. This research is very supportive in identifying the eligibility of KIP Kuliah recipients.

References

Akbari, S., Khanzadi, M., & Gholamian, M. R. (2018). Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index. Engineering, Construction and Architectural Management, 25(4), 534–558. https://doi.org/10.1108/ECAM-05-2016-0110

Chelly Dagdia, Z., Zarges, C., Beck, G., & Lebbah, M. (2020). A scalable and effective rough set theory-based approach for big data pre-processing. Knowledge and Information Systems, 62(8), 3321–3386. https://doi.org/10.1007/s10115-020-01467-y

Chen, Q., & Huang, M. (2021). Rough fuzzy model Based feature discretization in intelligent data preprocess. Journal of Cloud Computing, 10(1), 5. https://doi.org/10.1186/s13677-020-00216-4

Cosma, S., Venturelli, A., Schwizer, P., & Boscia, V. (2020). Sustainable Development and European Banks: A Non-Financial Disclosure Analysis. Sustainability, 12(15), 6146. https://doi.org/10.3390/su12156146

Dwiputranto, T. H., Setiawan, N. A., & Adji, T. B. (2022). Rough-Set-Theory-Based Classification with Optimized k-Means Discretization. Technologies, 10(2), 51. https://doi.org/10.3390/technologies10020051

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., Kumar, V., Rahman, M. M., Raman, R., Rauschnabel, P. A., Rowley, J., Salo, J., Tran, G. A., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168

Ekwonwune, E. N., Ubochi, C. I., & Duroha, A. E. (2022). Data Mining as a Technique for Healthcare Approach. International Journal of Communications, Network and System Sciences, 15(09), 149–165. https://doi.org/10.4236/ijcns.2022.159011

Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26(6), 3333–3361. https://doi.org/10.1007/s11948-020-00276-4

Gleißner, W., Günther, T., & Walkshäusl, C. (2022). Financial sustainability: Measurement and empirical evidence. Journal of Business Economics, 92(3), 467–516. https://doi.org/10.1007/s11573-022-01081-0

Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1–47. https://doi.org/10.1016/S0377-2217(00)00167-3

Gustriansyah, R., Ermatita, E., & Rini, D. P. (2022). An approach for sales forecasting. Expert Systems with Applications, 207, 118043. https://doi.org/10.1016/j.eswa.2022.118043

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6(1), 44. https://doi.org/10.1186/s40537-019-0206-3

Huang, Z., & Li, J. (2021). Multi-scale covering rough sets with applications to data classification. Applied Soft Computing, 110, 107736. https://doi.org/10.1016/j.asoc.2021.107736

Jaroji, J., Danuri, D., & Putra, F. P. (2016). K-Means Untuk Menentukan Calon Penerima Beasiswa Bidik Misi Di Polbeng. Inovtek Polbeng - Seri Informatika, 1(1), 87. https://doi.org/10.35314/isi.v1i1.129

Järvinen, P., Siltanen, P., & Kirschenbaum, A. (2021). Data Analytics and Machine Learning. In C. Södergård, T. Mildorf, E. Habyarimana, A. J. Berre, J. A. Fernandes, & C. Zinke-Wehlmann (Eds.), Big Data in Bioeconomy (pp. 129–146). Springer International Publishing. https://doi.org/10.1007/978-3-030-71069-9_10

Jassim, M. A., & Abdulwahid, S. N. (2021). Data Mining preparation: Process, Techniques and Major Issues in Data Analysis. IOP Conference Series: Materials Science and Engineering, 1090(1), 012053. https://doi.org/10.1088/1757-899X/1090/1/012053

Kaufmann, K. W., Lemmon, G. H., DeLuca, S. L., Sheehan, J. H., & Meiler, J. (2010). Practically Useful: What the R OSETTA Protein Modeling Suite Can Do for You. Biochemistry, 49(14), 2987–2998. https://doi.org/10.1021/bi902153g

Krishnan, T. V., Feng, S., & Jain, D. C. (2023). Peak sales time prediction in new product sales: Can a product manager rely on it? Journal of Business Research, 165, 114054. https://doi.org/10.1016/j.jbusres.2023.114054

Križanić, S. (2020). Educational data mining using cluster analysis and decision tree technique: A case study. International Journal of Engineering Business Management, 12, 184797902090867. https://doi.org/10.1177/1847979020908675

Kumar, G. S., & Premalatha, K. (2023). STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation. Distributed and Parallel Databases, 41(3), 233–266. https://doi.org/10.1007/s10619-023-07423-3

Leventhal, B. (2010). An introduction to data mining and other techniques for advanced analytics. Journal of Direct, Data and Digital Marketing Practice, 12(2), 137–153. https://doi.org/10.1057/dddmp.2010.35

Li, W., Liu, L., Zhang, H., Li, J., & Wang, Z. (2023). Examination Database and Online Paper Forming Algorithm for Mobile Personalized Learning Test. Indonesian Journal of Educational Research and Review, 6(1), 88–98. https://doi.org/10.23887/ijerr.v6i1.54381

Mauro, F., Vassalos, D., & Paterson, D. (2022). Critical damages identification in a multi-level damage stability assessment framework for passenger ships. Reliability Engineering & System Safety, 228, 108802. https://doi.org/10.1016/j.ress.2022.108802

Pattaraintakorn, P., & Cercone, N. (2008). A foundation of rough sets theoretical and computational hybrid intelligent system for survival analysis. Computers & Mathematics with Applications, 56(7), 1699–1708. https://doi.org/10.1016/j.camwa.2008.04.030

Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277

Rahayu, R. L., S, T. S., & Tambunan, L. (2022). Relationship of Building Form with Damage Pattern and Damage Level. IOP Conference Series: Earth and Environmental Science, 1091(1), 012019. https://doi.org/10.1088/1755-1315/1091/1/012019

Riegler, M. (2023). Towards a definition of sustainable banking—A consolidated approach in the context of guidelines and strategies. International Journal of Corporate Social Responsibility, 8(1), 5. https://doi.org/10.1186/s40991-023-00078-4

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2022.102817

Skowron, A., & Dutta, S. (2018). Rough sets: Past, present, and future. Natural Computing, 17(4), 855–876. https://doi.org/10.1007/s11047-018-9700-3

Sleiman, R., Mazyad, A., Hamad, M., Tran, K.-P., & Thomassey, S. (2022). Forecasting Sales Profiles of Products in an Exceptional Context: COVID-19 Pandemic. International Journal of Computational Intelligence Systems, 15(1), 99. https://doi.org/10.1007/s44196-022-00161-x

Sulistiani, H. (2018). Penerapan Algoritma Klasifikasi Sebagai Pendukung Keputusan Pemberian Beasiswa Mahasiswa [Preprint]. Ina-Rxiv. https://doi.org/10.31227/osf.io/yuavj

Tibaduiza Burgos, D. A., Gomez Vargas, R. C., Pedraza, C., Agis, D., & Pozo, F. (2020). Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications. Sensors, 20(3), 733. https://doi.org/10.3390/s20030733

Tinarbuko, S. (2019). Membaca Makna Iklan Politik Pilpres 2019. Mudra Jurnal Seni Budaya, 34(2), 250–258. https://doi.org/10.31091/mudra.v34i2.707

Wang, F. (2005). On acquiring classification knowledge from noisy data based on rough set. Expert Systems with Applications, 29(1), 49–64. https://doi.org/10.1016/j.eswa.2005.01.005

Wang, Y., Zhu, D., Zhang, B., Guo, Q., Wan, F., & Ma, N. (2021). Review of data scraping and data mining research. Journal of Physics: Conference Series, 1982(1), 012161. https://doi.org/10.1088/1742-6596/1982/1/012161

Wu, W.-T., Li, Y.-J., Feng, A.-Z., Li, L., Huang, T., Xu, A.-D., & Lyu, J. (2021). Data mining in clinical big data: The frequently used databases, steps, and methodological models. Military Medical Research, 8(1), 44. https://doi.org/10.1186/s40779-021-00338-z

Xu, J., Zeng, F., Liu, W., & Takahashi, T. (2022). Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Applied Sciences, 12(10), 4912. https://doi.org/10.3390/app12104912

Yao, Y., & Zhou, B. (2016). Two Bayesian approaches to rough sets. European Journal of Operational Research, 251(3), 904–917. https://doi.org/10.1016/j.ejor.2015.08.053

Zhang, G., & Qiu, H. (2021). Competitive Product Identification and Sales Forecast Based on Consumer Reviews. Mathematical Problems in Engineering, 2021, 1–15. https://doi.org/10.1155/2021/2370692

Author Biographies

Silfia Andin, Universitas Putra Indonesia YPTK Padang

Sarjon Defit, Universitas Putra Indonesia YPTK Padang

License

Copyright (c) 2024 Silfia Andin, Sarjon Defit

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).