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November 19, 2025
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March 30, 2026
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Abstract

Search algorithms play a crucial role in artificial intelligence, particularly in solving pathfinding and combination problems such as the 8-Puzzle game. This study presents the development of a web-based 8-Puzzle game designed to introduce endemic Wallacea fauna by comparing the performance of Blind Search and Heuristic Search algorithms. The system is built using HTML, CSS, and JavaScript, developed with Notepad++, and executed using a standard web browser. Four search algorithms are implemented, consisting of two blind search methods (Breadth First Search and Depth First Search) and two heuristic search methods (Greedy Best First Search and A*). Performance testing is conducted using three scenarios: testing easy puzzle configurations, testing high-complexity configurations, and cross-platform testing on desktop and mobile devices. The experimental results show that heuristic search consistently outperforms blind search. A* produces optimal solution paths with fewer expanded nodes, while Greedy achieves the fastest execution time. In contrast, DFS performs the worst, requiring in-depth node exploration and long execution time. Multi-platform evaluation shows that the game runs quite smoothly on both desktop and mobile devices. These results indicate that heuristic search, especially A*, is the most effective method for solving the educational 8-puzzle game of Wallacea endemic animal recognition.

Keywords

8-Puzzle Blind Search Endemic Heuristic Search Wallacea Animals

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