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All Journal Mimbar PGSD Undiksha Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Alfiana Sahraini
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Computer Science & IT Education Engineering

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Evaluation Evaluating the Duration and Intensity of MathQuest Game Use in Mathematics Learning: A Comparative Study of Elementary Students’ Engagement and Participation Alfiana Sahraini; Syaharuddin; Vera Mandailina
MIMBAR PGSD Undiksha Vol. 13 No. 2 (2025): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpgsd.v13i2.92548

Abstract

The problem underlying this study is the suboptimal use of instructional duration in enhancing student engagement and participation, particularly in the context of game-based learning. Moreover, there is a lack of research that specifically examines the impact of short-duration differences on student engagement patterns, both individually and in groups. Therefore, this study aims to compare the levels of student engagement and participation in mathematics learning based on differences in duration and intensity of MathQuest game usage. This study employed a quantitative approach with an experimental design. The research subjects were 49 sixth-grade elementary school students divided into two groups, namely Class A and Class B. Data were collected using observation sheets and pre-test and post-test instruments developed based on validated indicators of student engagement (X1) and participation (X2). The collected data were analyzed using descriptive statistics and the Independent Sample t-test to determine the significance of differences between the two groups. The analysis results showed significance values for individual engagement (0.977), group engagement (0.084), individual participation (0.834), group participation (0.834), and participation within the group (0.145), all of which were greater than 0.05, indicating no significant differences in the average levels of engagement and participation between students in Class A and Class B, both individually and in groups. Although there were slight variations in mean scores and standard deviations, these differences were not substantial enough to influence the conclusions of the study.
Evaluating the Accuracy of a Hybrid Neural Model with RBF-Polynomial Kernel for Rainfall Prediction: A Comparative Analysis of Trainlm and Trainrp Functions Syaharuddin, Syaharuddin; Abdillah, Abdillah; Alfiana Sahraini; Lilis Suriani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6388

Abstract

Accurate rainfall prediction is crucial for effective water management and disaster mitigation. This study introduces a novel hybrid neural model that employs a fourth-degree polynomial kernel and provides the first empirical comparison of the trainlm and trainrp functions to enhance forecasting accuracy. This study explored the application of a neural network algorithm with RBF-Polynomial (degree 4) kernel for training and testing data in rainfall forecasting. This study focused on monthly rainfall data collected from Mataram City, Indonesia. We developed a hybrid BP-RVM algorithm as the main algorithm that offers a predictive approach to compare the trainlm and trainrp functions. We conducted 20 trials with combinations of learning, momentum, and gamma-RBF at internal values of 0.01-0.9. The training results from trainrp with more than 118 iterations yielded the best performance with learning rate 0.8 and momentum 0.2; MSE value of 2,236.25 and RMSE of 47.29. These results indicate a relatively low error rate for the proposed method. In contrast, the trainlm method, which only requires 18 iterations with a learning rate of 0.6 and momentum of 0.4, produces an MSE of 2,689.25 and RMSE of 51.86, showing its efficiency in reducing the computation time but with a slightly higher error rate than trainrp. Overall, the trainrp method was more accurate in capturing actual rainfall patterns with lower error rates, whereas the trainlm method exhibited good stability but greater sensitivity to parameter variations. This comparative analysis highlights the potential of trainrp to achieve more precise rainfall predictions within the study area.
Co-Authors Abdillah Abdillah Lilis Suriani Syaharuddin Syaharuddin Syaharuddin Vera Mandailina