Self Organizing Maps (Kohonen) untuk Cluster Bidang Karya Ilmiah (Skripsi) Mahasiswa Berdasarkan Nilai-Nilai Matakuliah Pendukung Machine Learning - Hery Sunandar; Yasir Hasan (Forum Kerjasama Pendidikan Tinggi (FKPT), 2022)
The time for the preparation of scientific papers is considered too fast and not suitable for students due to several things in the scope of its preparation. One of the reasons is that the student is unable to complete and the supporting aspects of the field of scientific work being done, many students whose topics of scientific work are in machine learning or the like, but these students are unable to complete their scientific work because they do not understand the theoretical supporting knowledge of the machine learning field. So that it can make the student depressed or even harder to repeat his scientific work the next semester. The scope of machine learning courses are statistics and probability, matrix and linear algebra, algorithms and programming, and data structures. This research was conducted to overcome the problems faced by students, namely knowing the suitability group for the field of student scientific work they will be working on. So that in its preparation students can be responsible in their trial and are of higher quality. The clustering test with the self organizing maps (SOM) algorithm is more stable because the input according to the data owned is not random, only the weighting is done randomly but based on the uniform low (mins) and high (max) limit values. The desired number of clusters is two namely cluster 0 is able to do scientific work based on machine learning and cluster 1 vice versa. The SOM process for 40 student data with a target of two clusters and the results are cluster 0 = 14 students, cluster 1 = 26 students. The result is obtained by increasing the radius = 1, which previously this achievement was not successful if radius = 0.