Mamdani Fuzzy System for RPL Student Selection: A Case Study from a University in Jember
Abstract
The student selection process through the Recognition of Prior Learning (RPL) pathway continues to face challenges related to objectivity and transparency, particularly in integrating qualitative and quantitative data from various assessment instruments such as interviews and problem-solving tests. This study aims to develop a decision support system based on the Mamdani-type fuzzy logic to support a fair and standardized RPL student selection process. A quantitative approach was employed through intelligent system modeling, with system implementation carried out using Microsoft Excel by modifying IF-THEN formulas to simulate fuzzy logic principles. The system incorporates two input variables interview scores and problem-solving test scores and one output variable representing admission eligibility, categorized into five levels (A-E). Testing on a sample of ten prospective students showed that the system was able to perform systematic evaluations and generate more objective and consistent admission decisions. The conclusion of this study is that the application of a fuzzy inference system can improve the quality of decision making in the selection of RPL students, as well as support the principles of competency based and fair assessment.
References
Ali, M., Halik, W., Ramli, U., Banggu, M., Salmawati, Rais, L., Basri, L., Wahid, B., Hidaya, N., Sangadji, I. M., & Purnomo, A. (2024). Sosialisasi Sistem Pendidikan Rekognisi Pembelajaran Lampau (RPL) di Pemerintah Kota Sorong. Abdimas: Papua Journal of Community Service, 6(1), 49–57. https://doi.org/10.33506/pjcs.v6i1.3128
Ardiansah, Y., Luchia, N. T., Hastari, D., Rifat, T. M. F., Rachfaizi, R., Putri, N. A., & Ginting, E. S. (2024). Application of The Fuzzy Mamdani Method in Determining KIP-Kuliah Recipients for New Students. Public Research Journal of Engineering, Data Technology and Computer Science, 2(1), 11–17. https://doi.org/10.57152/predatecs.v2i1.1087
Aswita Ginting, R., Febrian, D., Mobo, F. D., & Dehham, S. H. (2023). Decision Support System in Determining the Job Waiting Period for Graduates of Unimed Using the Mamdani Method of Fuzzy Logic. Numerical: Jurnal Matematika Dan Pendidikan Matematika, 7(2), 275–286. https://doi.org/10.25217/numerical.v7i2.3105
Buranda, M. S., & Bernard, M. (2019). Analisis Kemampuan Pemecahan Masalah Matematik Materi Lingkaran Siswa Smp Berdasarkan Gender. JPMI (Jurnal Pembelajaran Matematika Inovatif), 2(1), 33. https://doi.org/10.22460/jpmi.v2i1.p33-40
Elfaladonna, F., & Isa, I. G. T. (2022). Uji Efektifitas Metode Fuzzy Logic Mamdani Pada Penerimaan Beasiswa Bantuan Menggunakan Matlab. SINTECH (Science and Information Technology) Journal, 5(1), 75–86. https://doi.org/10.31598/sintechjournal.v5i1.1043
Kemenristekdikti. (2023b). Rekognisi Pembelajaran Lampau. https://dikti.kemdikbud.go.id/wp-content/uploads/2023/04/Panduan-Banpem-Pelaksanaan-RPL-Tipe-A-2023-1-1.pdf
Klau, D. Y., Tursina, T., & Novriando, H. (2023). Implementasi Metode Fuzzy Inference System (FIS) Mamdani dalam Pemilihan Bidang Keahlian Mahasiswa. Jurnal Impresi Indonesia, 2(4), 372–383. https://doi.org/10.58344/jii.v2i4.2389
Mamdani, E. H., & Assilian, S. (1975). An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2
My, N., Ardiansyah, M., & Sarbani, A. A. (2025). Meningkatkan Keaktifan Siswa Melalui Model Project Based Learning dengan Pendekatan TPACK. Pinisi Journal PGSD, 5(1), 62–68. https://www.researchgate.net/publication/390049385_Meningkatkan_Keaktifan_Siswa_Melalui_Model_Project_Based_Learning_dengan_Pendekatan_TPACK
Pólya, G., & Conway, J. H. (2004). How to solve it: A new aspect of mathematical method (Expanded Princeton Science Library ed). Princeton University Press.
Rizvi, S., Mitchell, J., Razaque, A., Rizvi, M. R., & Williams, I. (2020). A fuzzy inference system (FIS) to evaluate the security readiness of cloud service providers. Journal of Cloud Computing, 9(1), 42. https://doi.org/10.1186/s13677-020-00192-9
Sumitre, M., & Kurniawan, R. (2014). Rancang Bangun Sistem Pendukung Keputusan Seleksi Penerimaan Tenaga Pengajar Dengan Metode Fuzzy Inference System (Fis) Mamdani. Jurnal Informatika, 14(1), 61–71.
Wardoyo, R., & Yuniarti, W. D. (2020). Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning. IJID (International Journal on Informatics for Development), 9(1), 29. https://doi.org/10.14421/ijid.2020.09105
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3). https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L. H. (1965). Fuzzy sets (Vol. 8, pp. 338–353). https://www.sciencedirect.com/science/article/pii/S001999586590241X