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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) AJAD : Jurnal Pengabdian kepada Masyarakat
Defni
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Humanities Computer Science & IT Engineering

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Pemanfaatan Website untuk Promosi Pariwisata di Kawasan Agrowisata Puncak Labuang Kurnia, Rahmi Putri; Defni; Hadi, Ronal; Wijaya, Taruma Leo; Fryonanda, Harfebi; Hafizun, Altaf
AJAD : Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/ajad.v4i3.404

Abstract

The development of technology-based tourism is a strategic step to increase the competitiveness of local destinations, such as Puncak Labuang in Nagari Limau Manis. This community service activity aims to increase the visibility and accessibility of Puncak Labuang tourism information by developing the visit.limaumanis.id website. The methods used include identifying needs, designing, developing systems, testing functionality, and training representatives of the Limau Manis Community Empowerment Forum (FPM). The activity results show that users consider this website easy to use and valuable. The training provided improves the ability of FPM representatives to manage and update content independently. A post-training survey revealed that 90% of participants felt more confident operating the website, and 88% considered the interface easy to understand. This implementation is expected to support the promotion of local tourism effectively and sustainably, which positively impacts the economy and welfare of the local community. This program shows the importance of synergy between technology and society for developing local potential.
Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process Defni; Andi Fathul Mukminin; Ainil Mardhiah; Titin Ritmi; Junaldi; Yuhefizar; Fibriyanti
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.v9i5.6552

Abstract

The Rubik's Cube is a complex puzzle with an enormous number of possible configurations, making it a challenging problem for both humans and computational methods to solve. While traditional solving algorithms rely on predefined strategies, this study explores the application of reinforcement learning (RL) to develop an adaptive and efficient solution model. This study aims to create an RL_based solver using the Markov Decision Process (MDP) framework, optimizing for speed, move efficiency, and solving steps. The proposed model employs Q-learning and Monte Carlo Tree Search (MCTS) to determine the optimal actions at each game state, which are trained through extensive Rubik's Cube simulations. The key novelty of this study lies in the integration of MCTS with Q-learning to enhance decision-making efficiency by reducing the number of moves compared with conventional methods. The experimental results demonstrate that the model achieves near-optimal solutions with fewer moves, outperforming basic rule-based approaches. Additionally, a web-based application was developed to provide real-time solving strategies based on user-input cube configurations. This study contributes to the advancement of RL applications in combinatorial puzzles and offers a practical tool for Rubik's Cube enthusiasts seeking to improve their solving techniques.
Co-Authors Ainil Mardhiah Andi Fathul Mukminin Fibriyanti Fryonanda, Harfebi Hafizun, Altaf Junaldi kurnia, rahmi putri Ronal Hadi Taruma Leo Wijaya Titin Ritmi Yuhefizar Yuhefizar