Implementation of Single Linked on Machine Learning for Clustering Student Scientific Fields

Saiful Nur Arif; Muhammad Dahria; Sarjon Defit; Dicky Novriansyah; Ali Ikhwan

doi:10.30812/matrik.v22i1.2337

Saiful Nur Arif STMIK Triguna Dharma, Medan, Indonesia
Muhammad Dahria STMIK Triguna Dharma, Medan, Indonesia
Sarjon Defit Universitas Putra, Padang, Indonesia
Dicky Novriansyah STMIK Triguna Dharma, Medan, Indonesia
Ali Ikhwan Universitas Islam Negeri Sumatera Utara, Indonesia

DOI: https://doi.org/10.30812/matrik.v22i1.2337

Keywords: Clustering, Euclidean, Machine Learning, Single Linkage, Scientific Fields

Abstract

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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