A FUZZY-BASED EXPERT SYSTEM FOR DETERMINANTS OF TEACHER PERFORMANCE
Abstract
Performance is the level of success achieved by a person in carrying out their duties and responsibilities as well as their ability to achieve the goals and standards that have been set. Teachers' performance is evaluated on a regular basis at each school. Teacher performance evaluation is carried out to identify flaws in task execution and to gain an overview of the results to be achieved in the future. So far, teacher performance appraisal is done manually, which is very difficult and time-consuming and feels less objective. Therefore, a fuzzy-based assessment system needs to be designed so that it helps in making decisions more quickly, precisely, and objectively. Rules are designed and tested using the Mamdani fuzzy logic method, which is implemented through the Matlab Toolbox software. To produce a more accurate performance rating, more membership function output is needed so that a more accurate performance rating can be produced.
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H. Martin, "Measuring Qualitative Performance Criteria with Fuzzy Sets," in International Conference on Business Information Systems, 2019, pp. 417-423.
S. Papadimitriou, K. Chrysafiadi, and M. Virvou, "FuzzEG: Fuzzy logic for adaptive scenarios in an educational adventure game," Multimedia Tools and Applications, vol. 78, pp. 32023-32053, 2019.
R. Karthika, L. J. Deborah, and P. Vijayakumar, "Intelligent e-learning system based on fuzzy logic," Neural Computing and Applications, pp. 1-10, 2019.
M. A. M. Sahagun, "A Fuzzy Logic Approach for Course Outcomes-based Assessment," in 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pp. 1-6
C. Troussas, A. Krouska, and C. Sgouropoulou,"Dynamic Detection of Learning Modalities Using Fuzzy Logic in Students’ Interaction Activities," in International Conference on Intelligent Tutoring Systems, 2020, pp. 205-213.
K. Chrysafiadi, C. Troussas, and M. Virvou, "Combination of fuzzy and cognitive theories for adaptive e-assessment," Expert Systems with Applications, p. 113614, 2020.
M. Megahed and A. Mohammed, "Modeling Adaptive E-Learning Environment using Facial Expressions and Fuzzy Logic," Expert Systems with Applications, p.113460, 2020
Ismunu, R. S., Purnomo, A. S., & Subardjo, R. Y. (2020). Sistem Pakar Untuk Mengetahui Tingkat Kecemasan Mahasiswa Dalam Menyusun Skripsi Menggunakan Metode Multi Factor Evaluation Process Dan Inferensi Fuzzy Tsukamoto. Seminar Nasional Multi Disiplin Ilmu (SENDI) (pp.65-72). Semarang: Universitas Stikubank
Kurniati, N. I., Mubarok, H., & Reinaldi, A. (2017). Rancang Bangun Sistem Pakar Diagnosa tingkat Depresi Pada Mahasiswa Tingkat Akhir Menggunakan Metode Fuzzy Tsukamoto (Studi Kasus: Universitas Siliwangi). JOIN, Vol. 2, No. 1, ISSN : 2527-9165, 49-55.
Prastianingrum, G., & Purnomo, A. S. (2019). Sistem Pakar Diagnosa Fobia Menggunakan Metode Certainty Factor. JMAI (Jurnal Multimedia dan Artificial Intelligence), Vol. 3, No. 2, ISSN : 2580-2593, 73-80
M. Radja, M. A. Londa, and K. Sara, “Penerapan Metode Logika Fuzzy dalam Evaluasi Kinerja Dosen,†Matrix J. Manaj. Teknol. dan Inform., vol.10, no.2, pp.78–86,2020, doi: 10.31940/matrix.v10i2.1841.
T. Agustin, A. Toibin, and A. S. Purnomo, “Sistem Pakar Pengembangan Skala Minat Karir Mahasiswa Dengan Inferensi Fuzzy Tsukamoto The Expert System Of The Development Of Student ’ s Career Interest Scales Using Tsukamoto ’ s Inference Fuzzy,†Pros. Semin.Nas. Multimed. Artif. Intell., no. 84, pp. 156-162, 2018.
D. A. Puryono, “Sistem Informasi Pendeteksi Hama Penyakit Tanaman Padi Menggunakan Metode Fuzzy Tsukamoto Berbasis Android,†vol.10,no.2,pp.63–69,2018, doi:10.31219/osf.io/hpk5s.
A. D. Saputri, R. D. Ramadhani, and R.Adhitama, “Logika Fuzzy Sugeno Untuk Pengambilan Keputusan Dalam Penjadwalan Dan Pengingat Service Sepeda Motor,†J. Informatics, Inf. Syst. Softw. Eng. Appl., vol. 2, no. 1, pp. 49–55, 2019, doi: 10.20895/inista.v2i1.95.
A. Azizah, A. Waris, and M. T. Sapsal, “Penerapan Sistem Fuzzy Logic pada Alat Ukur Kadar Nutrisi pada Sistem Hidroponik,†J. Agritechno, vol. 12, no. 2, pp. 85–93, 2019, doi: 10.20956/at.v0i0.215.
R. Apriliana, A. Damayanti, and A. B. Pratiwi, “Sistem Pakar Diagnosa Hipertiroid Menggunakan Certainty Factor dan Logika Fuzzy,†Contemp. Math. Appl., vol. 2, no. 1, p. 57, 2020, doi: 10.20473/conmatha.v2i1.19302.
DOI: https://doi.org/10.33387/jiko.v6i1.5796
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