Analysis University of Surakarta KIP Scholarship Recipients Using the Fuzzy MADM Method and C-45
Keywords:C-45; Fuzzy MADM; KIP,;Ranking; Covid-19
The impact of the Covid-19 pandemic has forced the economic activity of the Indonesian population to decline drastically, which has an impact on the education funding process. Given these problems, it is necessary to develop a Decision-Making System to assist the selection process for KIP admissions for students who meet the requirements. The purpose of this research is that the provision of KIP can be right on target. For decision making, three stages are used with the method used, the first stage is the C-45 method for student priority decision making, the second stage is the Fuzzy MADM method, and the third stage is ranking according to the total quota. which is determined. The initial selection used the C-45 method with the variables of GPA, parents' income, achievements, parental dependents, and cases. The results of the C4.5 calculation show that the first priority is parental dependents with a Gain value of 0.007822696, followed by a GPA with a Gain value of -0.130011482, the third priority is Parents' Income with a Gain value of -0.702657067 and the last priority is an achievement. The results of the calculation are continued with Fuzzy MADM resulting in 5 rules used to determine student priorities (can) or not. The results achieved from 140 students who applied were accepted by 135 students who passed the initial stage, and out of 135 rankings, 70 students were determined to receive scholarships from the Government with the highest calculation score of 21 and the lowest of 14.4.
Aji Setiawan, & Akbar, J. N. (2019). Implementation Fuzzy C-Means on Decision Support System BPNT (Bantuan Pangan Non-Tunai) Ministry of Social Affairs Indonesia. EMITTER International Journal of Engineering Technology, 7(2), 559–569. https://doi.org/10.24003/emitter.v7i2.444
Asidik, I., Kusrini, & Henderi. (2018). Decision Support System Model of Teacher Recruitment Using Algorithm C4.5 and Fuzzy Tahani. Journal of Physics: Conference Series, 1140(1). https://doi.org/10.1088/1742-6596/1140/1/012030
Data, P., Dalam, M., Bantuan, D., Di, S., Desa, I. P., Indikator, K., & Desa, P. P. (2020). TNP2K. 01.
Fiarni, C., Sipayung, E. M., & Tumundo, P. B. T. (2019). Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm. Journal of Information Systems Engineering and Business Intelligence, 5(1), 57. https://doi.org/10.20473/jisebi.5.1.57-66
Fitri, A. A., Pradnyana, I. M. A., & Darmawiguna, I. G. M. (2018). Decision Support System for “Buleleng Cerdas” Program Social Fund Recipient Candidates with Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) Method. Scientific Journal of Informatics, 5(2), 213–223. https://doi.org/10.15294/sji.v5i2.16457
Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
Haning, M. T., & Tahili, M. H. (2018). Advances in Social Science, Education and Humanities Research, volume 191 Asian Association for Public Administration Annual Conference (AAPA 2018). Advances in Social Science, Education and Humanities Research, 191(Aapa), 197–209.
Hoga Saragih, Murni Marbun, B. R. (2015). Development Of Decision Support System Determining The Students As Scholarship Awardees By Fuzzy Multi Attribute Decision Making (FMADM). Jurnal Sistem Informasi (Journal of Information Systems), 9 No 2, 75–81.
Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy Multicriteria Decision-Making: A Literature Review. International Journal of Computational Intelligence Systems, 8(4), 637–666. https://doi.org/10.1080/18756891.2015.1046325
Kahraman, C., Öztayşi, B., & Çevik Onar, S. (2016). Intuitionistic Fuzzy Multicriteria Evaluation of Outsource Manufacturers. IFAC-PapersOnLine, 49(12), 1844–1849. https://doi.org/10.1016/j.ifacol.2016.07.851
Kamila, I., & Helma, S. S. (2019). Implementation of MOORA Method for Determining Prospective Smart Indonesia Program Funds Recipients. (2), 1920–1925. https://doi.org/10.35940/ijeat.B2860.129219
Komsiyah, S., Wongso, R., & Pratiwi, S. W. (2019). Applications of the fuzzy ELECTRE method for decision support systems of cement vendor selection. Procedia Computer Science, 157, 479–488. https://doi.org/10.1016/j.procs.2019.09.003
Kurniawan, H., Swondo, A. P., Sari, E. P., Ummi, K., Yufrizal, & Agustin, F. (2019). Decision Support System to Determine the Student Achievement Scholarship Recipients Using Fuzzy Multiple Attribute Decision Making (FMADM) with SAW. 2019 7th International Conference on Cyber and IT Service Management, CITSM 2019, 3–8. https://doi.org/10.1109/CITSM47753.2019.8965326
Mathematics, A., Susilowati, T., Jasmi, K. A., Basiron, B., Huda, M., Shankar, K., … Julia, A. (2018). Determination of Scholarship Recipients Using Simple. 119(15), 2231–2238.
Meylinda, N. A., & Daniawan, B. (2018). Decision Support System for Home Loan Credit Using AHP and SAW methods ( Study Case : PT . Bintang Baru ). 1, 1–10.
Moon, J., & Kang, C. (2001). Application of fuzzy decision making method to the evaluation of spent fuel storage options. Progress in Nuclear Energy, 39(3), 345–351. Retrieved from http://www.sciencedirect.com/science/article/pii/S0149197001000191
Okfalisa, Insani, F., Abdillah, R., Anggraini, W., & Saktioto. (2019). Smart performance measurement tool in measuring the readiness of lean higher education institution. International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 422–427. https://doi.org/10.23919/EECSI48112.2019.8976930
Putra, D. M. D. U., & Pratama, P. A. (2020). Decision support system selection of tender winners project development building of STMIK STIKOM Indonesia with TOPSIS method. Journal of Physics: Conference Series, 1469(1). https://doi.org/10.1088/1742-6596/1469/1/012026
Rabiha, S. G., & Sasmoko. (2019). Analysis of the indicator’s performance to predict Indonesian Teacher Engagement Index (ITEI) using artificial neural networks. Procedia Computer Science, 157, 266–273. https://doi.org/10.1016/j.procs.2019.08.166
Setiawan, N., Suharjito, & Diana. (2019). A comparison of prediction methods for credit default on peer to peer lending using machine learning. Procedia Computer Science, 157, 38–45. https://doi.org/10.1016/j.procs.2019.08.139
Sugiyarti, E., Jasmi, K. A., Basiron, B., Huda, M., Shankar, K., & Maseleno, A. (2018). Decision support system of scholarship grantee selection using data mining. International Journal of Pure and Applied Mathematics, 119(15), 2239–2249. https://doi.org/10.5772/47788
Susilowati, T., Suyono, & Andewi, W. (2017). Decision Support System To Determine Scholarship Recipients At Sman 1 Bangunrejo Using Saw Method. International Journal Information System and Computer Science (IJISCS), 1(2), 59–66.
Syahputra, H., Sutrisno, & Gultom, S. (2020). Decision Support System for Determining the Single Tuition Group (UKT) in State University of Medan Using Fuzzy C-Means. Journal of Physics: Conference Series, 1462(1). https://doi.org/10.1088/1742-6596/1462/1/012071
How to Cite
Copyright (c) 2022 Ramadhian Agus Triono Sudalyo, Bayu Mukti
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.