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Evitarina, Nurita
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Computer Science & IT

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Pemodelan dan Simulasi Path Planning Robot Mobil Menggunakan Metode Ant System Pada Lingkungan Terstruktur Saktriawindarta, Rama; Suhendri, Suhendri; Evitarina, Nurita
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9862

Abstract

Path planning is a fundamental problem in mobile robot navigation that requires efficient route optimization while avoiding obstacles. This study implements the Ant System method to solve the mobile robot path planning problem using a modeling and simulation approach in a structured environment. The path planning process utilizes pheromone mechanisms and heuristic information to determine an optimal path from the initial state to the goal state. A total of ten simulation experiments were conducted with variations in the number of intermediate coordinates, iterations, and ants. The results show that the proposed method successfully generated collision-free paths in all experiments, with path lengths ranging from 31.4915 to 32.6788 units. The analysis indicates that the balance between the number of intermediate coordinates, iterations, and ants significantly affects path quality, where well-balanced parameters produce smoother and more stable trajectories. Overall, the Ant System method achieved a 100% success rate, demonstrating its effectiveness and reliability for mobile robot path planning in structured environments.
Pemodelan Pohon Keputusan Menggunakan Algoritma ID3 dalam Pendekatan Data Mining Hendri, Suhendri; Saktriawindarta, Rama; Evitarina, Nurita
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9865

Abstract

The rapid development of information technology has encouraged the use of data mining as a foundation for data-driven decision-making across various sectors, including the karaoke entertainment industry. This study aims to evaluate the performance of the ID3 algorithm in supporting decision support systems through the construction of a decision tree–based classification model. The research method employs the Knowledge Discovery in Databases (KDD) approach, which involves data selection, data transformation, modeling using the ID3 algorithm, and evaluation of decision outcomes. The performance of the method was evaluated based on five key aspects: decision-making capability, classification processing speed, classification result stability, model interpretability, and suitability to user needs. The results indicate that the ID3 algorithm achieved an average success rate of 92%, with the highest performance observed in processing speed and classification stability. These findings demonstrate that the ID3 algorithm is effective, efficient, and highly interpretable, making it suitable for implementation as a classification method in data mining–based decision support systems.
Co-Authors Hendri, Suhendri Saktriawindarta, Rama Suhendri Suhendri