Penentuan Jumlah Kelas Matakuliah Menggunakan Fuzzy Tsukamoto dan Metode K-Means Cluster
Article Sidebar
Main Article Content
Abstract
The prediction of the number of courses is done by the department before making a schedule for each course. In practice, the number of classes in each course has a different number and there is often an opening or closing class when compiling a KRS due to the number of classes that are not in accordance with the number of students. A system is needed to produce a number of classes so that it can reduce the number of opening classes because the demand for a higher number of classes is in the class because of the interest in a class that will be opened. Fuzzy methods are used to predict students who will repeat the course based on student force and value variables. The K-Means method is used to classify the subjects with the number of students converted into 2 groups based on the number of students who have been taken and the number of students who repeat a number of subjects. The two methods used are implemented in the application system to predict the number of classes. The results of the fuzzy and K-method processes mean the output of the application predictions the number of classes.
Downloads
Article Details
References
Maulidinnawati, A., Parewe, A. K., & Firdaus Mahmudy, W. (2016). Seleksi Calon Karyawan Menggunakan Metode Fuzzy Tsukamoto. Seminar Nasional Teknologi Informasi Dan Komunikasi, 2089–9815. Retrieved from https://fti.uajy.ac.id/sentika/publikasi/makalah/2016/71.pdf
Dili Giyanti, E. (2017). Penerapan Logika Fuzzy Untuk Menentukan Mahasiswa Berprestasi di STMIK Cikarang MEnggunakan Java Netbeans dan MYSQL. Jurnal Informatika SIMANTIK, 2(September), 77–84. https://doi.org/10.24961/j.tek.ind.pert.2017.27.2.141
Kusumadewi, S., & Purnomo, H. (2013). Aplikasi Logika Fuzzy untuk Pendukung Keputusan (2nd ed.). Graha Ilmu.
Tsukamoto for Decision Making in Crude Palm Oil Production Planning. 2016 International Conference on Industrial Engineering and Operations Management, 2206–2210
Bon, A. T., & Utami, S. F. (2016). Applying Fuzzy Inference System.
Chaudhari, S., & Patil, M. (2014). Study and Review of Fuzzy Inference Systems for Decision Making and Control. American International Journal of Research in Science, 14–147. Retrieved from http://www.iasir.net.
Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications – Two decades review from 1994 to 2014. Expert Systems With Applications, 42, 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003
Sangadji, I., & Arvio, Y. (2018, March). Dynamic Segmentation Of Behavior Patterns Based On Quantity Value Movement Using Fuzzy Subtractive Clustering Method. In Journal of Physics: Conference Series (Vol. 974, No. 1, p. 012009). IOP Publishing.
Siregar, R., Siregar, Z., & Arianto, R. (2019). Klasifikasi Sentiment Analysis Pada Komentar Peserta Diklat Menggunakan Metode K-Nearest Neighbor. KILAT, 8(1). https://doi.org/10.33322/kilat.v8i1.421
A., Arianto, R., Indrianto, I., & Nugroho, B. (2018). The Obstacles Detector with Tahani Fuzzy Logic as The Tool for Blind People. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, , 72-80. doi:10.24843/LKJITI.2018.v09.i02.p02