Implementation Of Ahc For Automated Learning Devices Using Student Behavior Analysis Through Lms (Case Study At Smk Telkom Sidoarjo)

Arby, Ahmad Yuan (2026) Implementation Of Ahc For Automated Learning Devices Using Student Behavior Analysis Through Lms (Case Study At Smk Telkom Sidoarjo). Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

The development of educational technology has encouraged the utilization of Learning Management Systems (LMS) as learning media capable of recording various student activities throughout the learning process. These activity data can be utilized to identify student learning behavior patterns more objectively. However, in practice, the learning process is still generally conducted in a uniform manner and does not accommodate the characteristics of individual learning styles. Therefore, this study aims to classify students based on their activity patterns within an LMS using the Agglomerative Hierarchical Clustering (AHC) method with a Single Linkage approach. This research employed an exploratory quantitative approach based on Educational Data Mining. The dataset was obtained from student activities within the LMS and consisted of variables including login frequency, material access duration, number of submitted assignments, forum participation, average quiz scores, and assignment response time. The data underwent preprocessing stages consisting of data cleaning and normalization, followed by clustering using Agglomerative Hierarchical Clustering with the Single Linkage approach. The results indicate that the Single Linkage method produced three clusters, consisting of 29 students in the first cluster, 1 student in the second cluster, and 1 student in the third cluster. The first cluster represents the dominant group, characterized by diverse and relatively active learning behaviors. The second cluster was interpreted as an Auditory learning style due to its high level of forum participation and prompt assignment responses, while the third cluster was interpreted as a Visual learning style because of its high material access duration and assignment completion rate. These findings reveal the presence of a chaining effect, where the majority of data points are merged into a single large cluster. Based on the results, it can be concluded that Agglomerative Hierarchical Clustering with the Single Linkage approach is capable of grouping student data; however, it produces an imbalanced cluster distribution. Therefore, this method can be used for the initial identification of student learning patterns, but consideration of alternative clustering methods is necessary to obtain more optimal clustering results.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Wahanani, Henni Endah	NIDN0022097811	henniendah.if@upnjatim.ac.id
Thesis advisor	Maulana, Hendra	NIDN1423128301	hendra.maulana.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Ahmad Yuan Arby

Date Deposited:

17 Jun 2026 04:11

Last Modified:

17 Jun 2026 04:11

URI:

https://repository.upnjatim.ac.id/id/eprint/53994