RULE BASE DENGAN UJI STATISTIK T-STUDENT UNTUK KETUNTASAN BELAJAR DAN HIERARCHICAL AGGLOMERATIVE CLUSTERING

ARNELAWATI, ARNELAWATI and Rini, Dian Palupi and Ermatita, Ermatita (2024) RULE BASE DENGAN UJI STATISTIK T-STUDENT UNTUK KETUNTASAN BELAJAR DAN HIERARCHICAL AGGLOMERATIVE CLUSTERING. Masters thesis, Sriwijaya University.

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Abstract

Assessment of learning completion is a crucial aspect in education, but conventional methods are often inaccurate and rigid, especially in accommodating variations in student abilities. This study aims to develop a more accurate and adaptive assessment system by combining the rule method based on the t-student statistical test to determine the threshold of incompleteness in learning mathematics, in addition this study also applies the Hierarchical Agglomerative Clustering (HAC) method to cluster the causes of incompleteness in learning mathematics. By using quantitative data from the results of tests and questionnaires on students, the results obtained that the rule can make significant decisions on student values That are around the threshold of completeness. Furthermore, the HAC method can cluster the factors that cause incompleteness in learning mathematics into 4 large categories, namely: Quality of Teaching and Teacher Guidance, Student Interest and Motivation, Understanding of Material, and Involvement and Classroom Environment. The conclusion obtained from this study is that a new approach to determining learning completeness using a rule base based on the t-student test can provide a dynamic threshold, and shows that the HAC method is effective in grouping factors that cause students' incomplete mathematics learning.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	HIERARCHICAL AGGLOMERATIVE CLUSTERING
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Arnelawati Arnelawati
Date Deposited:	15 May 2025 07:57
Last Modified:	15 May 2025 07:57
URI:	http://repository.unsri.ac.id/id/eprint/172430

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