Self Organizing Maps (Kohonen) untuk Cluster Bidang Karya Ilmiah (Skripsi) Mahasiswa Berdasarkan Nilai-Nilai Matakuliah Pendukung Machine Learning
- Hery Sunandar Universitas Budi Darma, Medan, Indonesia
-
Yasir Hasan
*
Universitas Budi Darma, Medan,
Indonesia
(*) Corresponding Author
Abstract
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.
Downloads
References
A. S. Khazari, F. Marisa, and I. D. Wijaya, “Sistem Rekomendasi Penentuan Judul Skripsi Menggunakan Algoritma Decision Tree,” J. Teknol. dan Manaj. Inform., vol. 3, no. 1, 2017, doi: 10.26905/jtmi.v3i1.1248.
I. Wahyudin, E. Tita Tosida, and F. Andria, “Teori dan Panduan Praktis Data Science dan Big Data,” Quality, no. March, pp. 1–6, 2020.
A. R. Sinaga and Y. Hasan, “Aplikasi Pendukung Keputusan Penentuan Dosen Pembimbing Skripsi S1 Teknik Informatika,” Inf. dan Teknol. Ilm., pp. 182–187, 2015, [Online]. Available: https://s3.amazonaws.com/academia.edu.documents/40796550/31._Jurnal__ALEX.pdf?response-content-disposition=inline%3B filename%3D31._Jurnal_ALEX.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BAPZF7ABG3%2F20200418%2Fus-east-1%2Fs3%2Faws4_r.
R. R. Santoso, R. Megasari, and Y. A. Hambali, “Implementasi Metode Machine Learning Menggunakan Algoritma Evolving Artificial Neural Network Pada Kasus Prediksi Diagnosis Diabetes,” JATIKOM (Jurnal Apl. dan Teor. Ilmu Komputer), vol. 3, no. 2, pp. 85–97, 2020.
Fahrizal, F. O. Reynaldi, and N. Hikmah, “Implementasi Machine Learning pada Sistem PETS Identification Menggunakan Python Berbasis UBuntu,” J. Inf. Syst. Informatics Comput., vol. 4, no. 1, pp. 86–91, 2020.
F. A. Bachtiar, I. K. Syahputra, and S. A. Wicaksono, “Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 5, p. 543, 2019, doi: 10.25126/jtiik.2019651755.
R. Umar, A. Fadlil, and R. R. Az-Zahra, “Self Organizing Maps(SOM) untuk Pengelompokkan Jurusan di SMK,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 2, p. 131, 2018, doi: 10.23917/khif.v4i2.7044.
S. S. Nagari and L. Inayati, “Implementation of Clustering Using K-Means Method To Determine Nutritional Status,” J. Biometrika dan Kependud., vol. 9, no. 1, p. 62, 2020, doi: 10.20473/jbk.v9i1.2020.62-68.
A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 1, pp. 75–82, 2020, doi: 10.31294/ijcit.v5i1.7951.
D. Rahmalia and T. Herlambang, “Application Kohonen Network and Fuzzy C Means for Clustering Airports Based on Frequency of Flight,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 3, no. 3, pp. 229–236, 2018, doi: 10.22219/kinetik.v3i3.608.
T. Honkela, “Description of Kohonen ’ s Self-Organizing Map,” pp. 1–7, 2018.
N. F. Arno and M. Ahsan, “Neural Network,” Power Syst., vol. 28, pp. 75–159, 2017, doi: 10.1007/978-3-030-64777-3_4.
D. Sarkar, R. Bali, and T. Sharma, Practical Machine Learning with Rust. 2020.
L. S. Moonlight, F. Faizah, Y. Suprapto, and N. Pambudiyatno, “Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 2, p. 149, 2021, doi: 10.20473/jisebi.7.2.149-161.
M. D. Kartikasari, “Self-Organizing Map Menggunakan Davies-Bouldin Index dalam Pengelompokan Wilayah Indonesia Berdasarkan Konsumsi Pangan,” Jambura J. Math., vol. 3, no. 2, pp. 187–196, 2021, doi: 10.34312/jjom.v3i2.10942.
A. A. Muin, “IMPLEMENTASI SELF ORGANIZING MAPS ( SOM ) KLASIFIKASI PENDUDUK UNTUK MENENTUKAN KEPUTUSAN PEMBANGUNAN DAERAH PRIORITAS MISKIN ( Studi Kasus Kota Makassar ) Berdasarkan dirumuskan , apakah mengklasifikasi latar metode belakang SOM berdasar dapat dapat ting,” pp. 1–8, 2018.
G. Munawar, “Implementasi Algoritma Self Organizing Map (SOM) untuk Clustering Mahasiswa pada Matakuliah Proyek (Studi Kasus : JTK POLBAN),” Pros. 6th Ind. Res. Work. Natl. Semin., pp. 66–78, 2015.
E. B. Perkasa, B. Wijaya, and J. Jamhari, “Pemodelan Jaringan Kohonen Untuk Pengenalan Nama Asteroid Kohonen Network Modeling for Asteroid Name Recognition,” vol. 10, no. 2, pp. 127–138, 2020.
R. N. R. Wijaya, “Kohonen Self-Organizing Maps,” pp. 3–5, [Online]. Available: https://github.com/RinRoya/Kohonen-Self-Organizing-Maps/
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Self Organizing Maps (Kohonen) untuk Cluster Bidang Karya Ilmiah (Skripsi) Mahasiswa Berdasarkan Nilai-Nilai Matakuliah Pendukung Machine Learning
Pages: 239-246
Copyright (c) 2022 Hery Sunandar, Yasir Hasan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
