Journal of Vocational, Informatics and Computer Education
Vol 3, No 1 (2025): June 2025

Dari Persepsi ke Penerimaan: Analisis TAM terhadap Penggunaan E-Learning di Perguruan Tinggi Makassar

M. Andika Aswa (Universitas Negeri Makassar)
Muhammad Husair Nawawi (Universitas Negeri Makassar)
Nurrahmah Agusnaya (Universitas Negeri Makassar)
Rahmawati (Universitas Patompo)
Rachmawaty Kadir (Universitas Patompo)



Article Info

Publish Date
07 Jun 2025

Abstract

The development of digital technology has driven significant changes in learning systems, including the adoption of e-learning in higher education. This study aims to analyze student acceptance of the use of e-learning platforms in Makassar City universities using the Technology Acceptance Model (TAM) framework. The three main constructs analyzed include Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Attitude Toward Using (ATU). This research uses quantitative approach with survey method, involving 65 students from various study programs who actively participate in online learning. The instrument used was a Google Form-based questionnaire consisting of 15 statements, analyzed using multiple linear regression through Jamovi software. The results showed that PU and PEOU had a positive and significant effect on ATU, with a coefficient of determination (R²) of 0.702. This means that 70.2% of the variation in student attitudes towards e-learning can be explained by these two variables. This finding confirms that perceived usefulness and ease of use are key factors in shaping positive attitudes towards learning technology adoption. This research provides an empirical contribution to the strengthening of the TAM model in the Indonesian higher education context and offers practical recommendations for the development of e-learning systems that are more effective and responsive to user needs.

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Journal Info

Abbrev

VOICE

Publisher

Subject

Computer Science & IT Education

Description

1. Informatics and Computing Research addressing the design, development, implementation, and evaluation of computing technologies relevant to educational, professional, and digital learning environments, including but not limited to: Artificial Intelligence and Machine Learning Deep Learning and ...