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All Journal IJIIS: International Journal of Informatics and Information Systems Asatiza: Jurnal Pendidikan
Yuliati, Emi
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Computer Science & IT Education Social Sciences

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The mediating role of learning motivation: An integrated TAM2 and self-determination theory model for e-learning achievement Yuliati, Emi; Rujianto, Eko; Karyono, Giat
Asatiza: Jurnal Pendidikan Vol. 7 No. 1 (2026): Asatiza: Jurnal Pendidikan
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/asatiza.v7i1.3572

Abstract

Prior research based on the Technology Acceptance Model 2 (TAM2) has largely emphasized technological and social factors while offering limited insight into how learner motivation translates system acceptance into meaningful learning outcomes. To address this gap, this study integrates TAM2 core constructs, namely Perceived Ease of Use, Perceived Usefulness, and Social Influence, with key motivational drivers, including hedonic, intrinsic, and extrinsic motivation, to examine their combined effects on behavioral intention, learning motivation, and learning achievement in e-learning contexts. Data were collected from 300 e-learning users using a Likert-scale questionnaire and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The results reveal that Perceived Ease of Use strongly influences Perceived Usefulness (β = 0.611, p < 0.001), indicating that simplifying system usability is fundamental for users to recognize the value of e-learning platforms. Perceived Usefulness and Social Influence significantly predict behavioral intention, while motivational factors collectively exert a substantial effect on learning motivation, which in turn strongly enhances learning achievement (β = 0.552, p < 0.001). The proposed model explains a substantial proportion of variance in learning motivation (64%) and learning achievement (55%), demonstrating good predictive relevance within the studied context. Mediation analysis further confirms that learning motivation serves as a key mechanism through which technology acceptance and motivational factors translate into improved learning outcomes. A key practical implication is that e-learning designers should prioritize not only system usefulness and ease of use but also hedonic elements that enhance enjoyment, as this jointly foster learning motivation and ultimately drive learning achievement.
Personalized Hydration Prediction: Leveraging Machine Learning to Model Daily Water Intake Based on Physical Activity and Environmental Factors Yuliati, Emi; Nurdiyanti, Oktavia Mulyo
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.297

Abstract

Adequate hydration is essential for maintaining optimal health and performance, yet individual hydration needs vary due to factors such as physical activity, environmental conditions, and demographic characteristics. Traditional methods of hydration assessment often overlook these dynamic factors, making it difficult to provide personalized recommendations. This study aims to develop a Random Forest Regression model to predict daily water intake based on physical activity levels, weather conditions, and demographic information. The model was trained and evaluated using a dataset that included these variables, and performance was assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The results showed that the Random Forest Regression model achieved an R² value of 0.8527, indicating that it explained over 85% of the variance in daily water intake. The MSE (0.1010) and MAE (0.2630) values confirmed that the model made accurate predictions. This study contributes to the field by offering a personalized approach to hydration prediction, which could be integrated into health applications and fitness tracking systems. By incorporating real-time physical activity data and environmental factors, the model provides dynamic hydration recommendations that can optimize health outcomes, particularly for high-risk groups such as athletes and the elderly. This research demonstrates the potential of Random Forest Regression for improving hydration management and advancing personalized health recommendations.
Co-Authors Giat Karyono Nurdiyanti, Oktavia Mulyo Rujianto, Eko