Penentuan Jumlah Kelas Matakuliah Menggunakan Fuzzy Tsukamoto dan Metode K-Means Cluster

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Yessy Fitriani
Mochamad Farid Rifai
M. Yoga Distra Sudirman


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.


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Fitriani, Y., Rifai, M., & Sudirman, M. Y. (2019). Penentuan Jumlah Kelas Matakuliah Menggunakan Fuzzy Tsukamoto dan Metode K-Means Cluster. PETIR: Jurnal Pengkajian Dan Penerapan Teknik Informatika, 12(2), 196 - 211.


[1] 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
[2] 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.
[3] Kusumadewi, S., & Purnomo, H. (2013). Aplikasi Logika Fuzzy untuk Pendukung Keputusan (2nd ed.). Graha Ilmu.
[4] Tsukamoto for Decision Making in Crude Palm Oil Production Planning. 2016 International Conference on Industrial Engineering and Operations Management, 2206–2210
[5] Bon, A. T., & Utami, S. F. (2016). Applying Fuzzy Inference System.
[6] 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
[7] 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.
[8] 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.
[9] Siregar, R., Siregar, Z., & Arianto, R. (2019). Klasifikasi Sentiment Analysis Pada Komentar Peserta Diklat Menggunakan Metode K-Nearest Neighbor. KILAT, 8(1).
[10] 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

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