Implementation of Single Linked on Machine Learning for Clustering Student Scientific Fields
DOI:
https://doi.org/10.30812/matrik.v22i1.2337Keywords:
Clustering, Euclidean, Machine Learning, Single Linkage, Scientific FieldsAbstract
Machine Learning in classifying scientific fields according to the competence of students. Currently STMIK Triguna Dharma is quite difficult to map the scientific fields that will be used by students in submitting titles, so that the results of the thesis made are less than optimal. For this reason, it is necessary to map this concentration to assist students in completing theses through specialization classes. The Mechanical Learning technique used in solving this problem is to use the Single Linkage Technique. The process of testing the method begins with determining the standard data used and then looking for the proximity value using Euclidean so that later cluster results will be obtained from mapping scientific fields. From the Single Linkage Technique process that has been carried out, several cluster results will be obtained, namely clusters that map groups of STMIK Triguna Dharma students who have competence and clusters that map groups of STMIK Triguna Dharma students who lack competence. From the results of this grouping, the institution will make specialization classes according to the resulting cluster. Thus creating a specialization class that is in accordance with the competencies possessed by STMIK Triguna Dharma students
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References
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[4] J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Machine Learning with Applications Deep learning in computer vision : A critical review of emerging techniques and application scenarios,†Machine Learning with Applications, vol. 6, no. March, pp. 1–13, 2021, doi: 10.1016/j.mlwa.2021.100134.
[5] L. Ganda, R. Putra, and A. Anggrawan, “Pengelompokan Penerima Bantuan Sosial Masyarakat dengan Metode K-Means,†Matrik: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer, vol. 21, no. 1, pp. 205–214, 2021, doi: 10.30812/matrik.v21i1.1554.
[6] T. R. P. Bishop, S. Von Hinke, B. Hollingsworth, A. A. Lake, H. Brown, and T. Burgoine, “Machine Learning with Applications Automatic classification of takeaway food outlet cuisine type using machine ( deep ) learning,†Machine Learning with Applications, vol. 6, no. July, pp. 1–11, 2021, doi: 10.1016/j.mlwa.2021.100106.
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[8] I. F. Yuliati and P. R. Sihombing, “Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia Implementation of Machine Learning Method in Risk Classification on Low Birth weight in Indonesia,†vol. 20, no. 2, pp. 417–426, 2021, doi: 10.30812/matrik.v20i2.1174.
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[13] Fitra, “Perbandingan Algoritme Machine Learning Untuk Memprediksi,†vol. 6, no. 5, pp. 543–548, 2019, doi: 10.25126/jtiik.2019611755.
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[15] R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 302–311, 2020.
[16] A. R. Riszky and M. Sadikin, “Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan Data Mining using Apriori Algorithm for Product Recommendation for Customers,†Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 3, pp. 103–108, 2019, doi: 10.14710/jtsiskom.7.3.2019.103-108.
[17] E. N. Osegi and E. F. Jumbo, “Machine Learning with Applications Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory,†Machine Learning with Applications, vol. 6, no. June, pp. 1–10, 2021, doi: 10.1016/j.mlwa.2021.100080.
[18] K. Giri, T. Kr, and P. Sarkar, “Machine Learning with Applications ECR-DBSCAN : An improved DBSCAN based on computational geometry,†Machine Learning with Applications, vol. 6, no. July, p. 100148, 2021, doi: 10.1016/j.mlwa.2021.100148.
[19] E. Nsugbe, O. Obajemu, O. William, and I. Sanusi, “Machine Learning with Applications Enhancing care strategies for preterm pregnancies by using a prediction machine to aid clinical care decisions,†Machine Learning with Applications, vol. 6, no. July, pp. 1–11, 2021, doi: 10.1016/j.mlwa.2021.100110.
[20] M. Kempa and Y. Peng, “Machine Learning with Applications Machine learning algorithms for fraud prediction in property insurance : Empirical evidence using real-world microdata,†Machine Learning with Applications, vol. 5, no. July, pp. 1–14, 2021, doi: 10.1016/j.mlwa.2021.100074.
[21] P. C. Pendharkar, “Machine Learning with Applications Hybrid radial basis function DEA and its applications to regression , segmentation and cluster analysis problems,†Machine Learning with Applications, vol. 6, no. June, pp. 1–9, 2021, doi: 10.1016/j.mlwa.2021.100092.
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Published
2022-11-30
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Articles
How to Cite
[1]
S. N. Arif, M. Dahria, S. Defit, D. Novriansyah, and A. Ikhwan, “Implementation of Single Linked on Machine Learning for Clustering Student Scientific Fields”, MATRIK, vol. 22, no. 1, pp. 259–272, Nov. 2022, doi: 10.30812/matrik.v22i1.2337.
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