TY - GEN
T1 - A comparative study
T2 - 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020
AU - Hasanah, Novrindah Alvi
AU - Atikah, Luthfi
AU - Herumurti, Darlis
AU - Yunanto, Andhik Ampuh
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/19
Y1 - 2020/9/19
N2 - Combining games with learning methods are the most effective way to increase learning motivation, ratification, concentration, and student skills in understanding and solving problems. One of the most popular games is Sudoku. Traditional methods that have used to solve problems in the Sudoku game show a fairly complex solution. So, a good method for solving these problems is needed such as Ant Colony Optimization, which can be used for path searching. This research uses Ant Colony Optimization as a method to find the best path effectively and efficiently to complete the game. Test results used as a benchmark for the Ant Colony Optimization method are better at completing the game by compiling it with traditional methods such as Backtracking. The result of this research shows that Ant Colony Optimization has better performance than Backtracking algorithm. It was proven by 75 trials conducted at three levels of the game resulting in 67 trials (89%) showing Ant Colony Optimization completing the game faster than Backtracking Algorithm.
AB - Combining games with learning methods are the most effective way to increase learning motivation, ratification, concentration, and student skills in understanding and solving problems. One of the most popular games is Sudoku. Traditional methods that have used to solve problems in the Sudoku game show a fairly complex solution. So, a good method for solving these problems is needed such as Ant Colony Optimization, which can be used for path searching. This research uses Ant Colony Optimization as a method to find the best path effectively and efficiently to complete the game. Test results used as a benchmark for the Ant Colony Optimization method are better at completing the game by compiling it with traditional methods such as Backtracking. The result of this research shows that Ant Colony Optimization has better performance than Backtracking algorithm. It was proven by 75 trials conducted at three levels of the game resulting in 67 trials (89%) showing Ant Colony Optimization completing the game faster than Backtracking Algorithm.
KW - Ant Colony Optimization (ACO)
KW - Backtracking Algorithm
KW - Sudoku
UR - http://www.scopus.com/inward/record.url?scp=85096821571&partnerID=8YFLogxK
U2 - 10.1109/iSemantic50169.2020.9234267
DO - 10.1109/iSemantic50169.2020.9234267
M3 - Conference contribution
AN - SCOPUS:85096821571
T3 - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020
SP - 548
EP - 553
BT - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 September 2020 through 20 September 2020
ER -