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p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal International Journal of Applied Mathematics and Computing. International Journal of Mathematics and Science Education
Syed Asif Ali
Unknown Affiliation
Computer Science & IT Education Mathematics

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Relationship Between Mathematics Self-Efficacy and Problem-Solving Ability through the Jigsaw Cooperative Learning Model Jufri Jufri; Detri Amelia Chandra; Syed Asif Ali
International Journal of Mathematics and Science Education Vol. 1 No. 3 (2024): August : International Journal of Mathematics and Science Education
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijmse.v1i3.246

Abstract

This study investigates the relationship between mathematics self-efficacy and problem-solving ability, exploring the role of the Jigsaw cooperative learning model in enhancing both factors. Self-efficacy, defined as students' belief in their ability to successfully perform mathematical tasks, has been shown to significantly impact students' problem-solving capabilities. The research focuses on understanding how this belief influences academic performance and how the Jigsaw model, which fosters collaborative learning, affects students’ self-efficacy and problem-solving skills. A survey-based research design was employed, with participants consisting of university students enrolled in mathematics courses. Data collection involved administering a self-efficacy scale to assess students' confidence in solving mathematical problems and a problem-solving test to evaluate their abilities. Path analysis was used to determine the relationship between self-efficacy and problem-solving skills, and the effect of the Jigsaw model was evaluated by comparing pre- and post-test scores between the experimental and control groups. The results revealed a strong positive relationship between self-efficacy and problem-solving ability (β = 0.45). Additionally, the Jigsaw model significantly enhanced this relationship, improving both students' self-efficacy and problem-solving performance. These findings highlight the importance of fostering self-efficacy in mathematics education and the effectiveness of the Jigsaw cooperative learning model in achieving this goal. The study provides valuable insights into improving teaching strategies in mathematics, particularly for subjects requiring critical thinking and problem-solving skills, by promoting a more interactive and supportive learning environment.
Algorithmic Simulation for Optimization in Combinatorial Mathematics Using Heuristic Techniques Ahmad Budi Trisnawan; Syed Asif Ali; Erlita Sulistiati
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.274

Abstract

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.
Co-Authors Ahmad Budi Trisnawan Detri Amelia Chandra Erlita Sulistiati Jufri Jufri