Penerapan Fuzzy Mamdani Dengan Particle Swarm Optimization (PSO) Pada Penilaian Kinerja Dosen (Studi Kasus STMIK Kaputama)
Abstract
Abstract-- The use of technology at this time is very developed along with the development of the times, so there are still many young people who need to get proper teaching. The lessons can be obtained from what parents have taught in the home environment and lecturers at the College. Regardless of the responsibilities of parents at home, we as young people are also obliged to study in school to college to achieve our goals. There are many bona fide universities and even the tuition fees vary, but universities that have expensive fees cannot be used as a benchmark that the university is competent, both from the teaching staff and academic regulations in the college. A competent university is a university that has competent teaching staff. In the Teaching and Learning Process at Higher Education, lecturers have an important role in the success of students in mastering the material given. STMIK Kaputama evaluates the performance of lecturers through 3 (three) variables, namely: Material Variables, Discipline Variables and Attitude Variables. With parameter values: Very Low (SR), Low (R), Enough (C), Good (B) and Very Good (SB), from the parameters the results of the Lecturer Performance Assessment will be known.Then it is necessary to build an application to assess the performance of lecturers using Fuzzy Mamdani with Particle Swarm Optimization (PSO), so that the assessment of Lecturer Performance can be assessed quickly.
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Keywords: Lecturer Performance, Fuzzy Mamdani, PSO
References
[1] Adewuyi, A.P. 2013. Performance Evaluation of Mamdani-type and Sugeno-type Fuzzy Inference System Based Controllers for Computer Fan. International Journal Information Technology and Computer Science (IJITCS) 5 (1)
[2] Chen, G. & Pham, T.T. 2001. Introduction to Fuzzy Sets, Fuzzy Logic,and Fuzzy Control Systems. CRC Press : Washington DC
[3] Fechera B, Kustija. J & Elvyanti. S. 2012. Optimasi Penggunaan Membership Function Logika Fuzzy Pada Kasus Identifikasi Kualitas Minyak Transformator. Electrans 11 (2) : 1412-3762.
[4] Nasr, A.S., Rezaei, M & Barmaki, M.D. 2012. Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd Province,Iran. International Journal of Computer Applications 59 (7) : 0975-8887.
[5] Nezhad, Q.A., Zand, J.P & Hoseini, S.S. 2013. An Investigation on Fuzzy Logic Controllers (Takagi-Sugeno & Mamdani) in Inverse Pendulum System. International Journal of Fuzzy Logic Systems (IJELS) 3(3)
[6] Richie Cindy Anggria, Afriyudi & Febriyanti Panjaitan 2015, “Penerapan Metode Fuzzy TOPSIS dalam Sistem Pendukung Keputusan Penilaian Kinerja dan Jabatan Karyawan Balai Penelitian Sembawa†Student Colloquium Sistem Informasi & Teknik Informatika (SC-SITI).
[7] Sri Eniyati, Rina Candra Noor Santi, 2007, “Perancangan Sistem Pendukung Keputusan Penilaian Prestasi Dosen Berdasarkan Penelitian dan Pengabdian Masyarakatâ€Â, Jurnal Teknologi Informasi DINAMIK Volume XV, No.2, Juli 2010:136-142, ISSN : 0854-9524.
[8] Sumiati & Nuryadhin, S. 2013. Decision Support Systems In Determining Lecturer’s Performance Appraisal Using Fuzzy Database Method of Mamdani’s Model (Case Study at the University of Serang Raya). International Journal of Application or Innovation in Engineering & Management (IJAIEM) 2(11) : 2319-4847.
[9] Sutojo, T., Mulyanto, E., Suhartono, V. 2011. Kecerdasan Buatan. Yogyakarta: Penerbit ANDI.
[10] Vasant, I.E.P & Webb.J. 2009. The Application of Mamdani Fuzzy Model for Auto Zoom Function of a Digital Camera. (IJCSIS) International Journal of Science and Information Security 6(3) : 1947-5500.
[11] Saragih, Rijois Iboy Erwin, and Darsono Nababan. "Increase Performance Genetic Algorithm In Matching System By Setting GA Parameter." Journal of Physics: Conference Series. Vol. 1175. No. 1. IOP Publishing, 2019.
[12] Guo, H. & He, J. 2013. A modified particle swarm optimization algorithm. Journal of Computer Science 10(2) : 341-346.
[2] Chen, G. & Pham, T.T. 2001. Introduction to Fuzzy Sets, Fuzzy Logic,and Fuzzy Control Systems. CRC Press : Washington DC
[3] Fechera B, Kustija. J & Elvyanti. S. 2012. Optimasi Penggunaan Membership Function Logika Fuzzy Pada Kasus Identifikasi Kualitas Minyak Transformator. Electrans 11 (2) : 1412-3762.
[4] Nasr, A.S., Rezaei, M & Barmaki, M.D. 2012. Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd Province,Iran. International Journal of Computer Applications 59 (7) : 0975-8887.
[5] Nezhad, Q.A., Zand, J.P & Hoseini, S.S. 2013. An Investigation on Fuzzy Logic Controllers (Takagi-Sugeno & Mamdani) in Inverse Pendulum System. International Journal of Fuzzy Logic Systems (IJELS) 3(3)
[6] Richie Cindy Anggria, Afriyudi & Febriyanti Panjaitan 2015, “Penerapan Metode Fuzzy TOPSIS dalam Sistem Pendukung Keputusan Penilaian Kinerja dan Jabatan Karyawan Balai Penelitian Sembawa†Student Colloquium Sistem Informasi & Teknik Informatika (SC-SITI).
[7] Sri Eniyati, Rina Candra Noor Santi, 2007, “Perancangan Sistem Pendukung Keputusan Penilaian Prestasi Dosen Berdasarkan Penelitian dan Pengabdian Masyarakatâ€Â, Jurnal Teknologi Informasi DINAMIK Volume XV, No.2, Juli 2010:136-142, ISSN : 0854-9524.
[8] Sumiati & Nuryadhin, S. 2013. Decision Support Systems In Determining Lecturer’s Performance Appraisal Using Fuzzy Database Method of Mamdani’s Model (Case Study at the University of Serang Raya). International Journal of Application or Innovation in Engineering & Management (IJAIEM) 2(11) : 2319-4847.
[9] Sutojo, T., Mulyanto, E., Suhartono, V. 2011. Kecerdasan Buatan. Yogyakarta: Penerbit ANDI.
[10] Vasant, I.E.P & Webb.J. 2009. The Application of Mamdani Fuzzy Model for Auto Zoom Function of a Digital Camera. (IJCSIS) International Journal of Science and Information Security 6(3) : 1947-5500.
[11] Saragih, Rijois Iboy Erwin, and Darsono Nababan. "Increase Performance Genetic Algorithm In Matching System By Setting GA Parameter." Journal of Physics: Conference Series. Vol. 1175. No. 1. IOP Publishing, 2019.
[12] Guo, H. & He, J. 2013. A modified particle swarm optimization algorithm. Journal of Computer Science 10(2) : 341-346.
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2019-07-01
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