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Pascal: Journal of Computer Science and Informatics
Published by Cv. Devi Tara Innovations
ISSN : -     EISSN : 30475074     DOI : -
Core Subject : Science, Education, Social, Engineering,
Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Social Sciences
Pascal: Journal of Computer Science and Informatics is a national scientific journal that publishes research articles in the field of Computer Science and Informatics which include: Computer Engineering, Information Engineering, Computer Science, Information Systems, Information Technology, Software Engineering, Computer Systems, Computer Networks, Application of Information Technology and Other Fields of Computer Science and Informatics that have not been listed
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
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Search results for , issue "Vol. 3 No. 01 (2025): Pascal: Journal of Computer Science and Informatics" : 3 Documents clear
Design and Evaluation of an Adaptive Traffic Signal Control System Based on Mamdani Fuzzy Logic Elisa, Nova; Ira C, Ira C; Verawati, Sofia; Gracia M, Angelica; Orlan R, Orlan R
Pascal: Journal of Computer Science and Informatics Vol. 3 No. 01 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

Traffic congestion in urban areas has become an increasingly complex problem due to the rapid growth in the number of vehicles and the limitations of fixed-time traffic signal control systems. Conventional approaches are unable to respond dynamically to fluctuations in traffic density, often resulting in high waiting times and reduced intersection capacity. This study aims to design and evaluate an adaptive traffic signal control system based on Mamdani fuzzy logic to improve intersection control performance. The developed system uses two input variables, namely the number of vehicles on the main approach and the number of vehicles on the competing approach, and one output variable representing the green signal duration. Membership functions are modeled using triangular and trapezoidal shapes, while the rule base is structured in the form of a Fuzzy Associative Memory (FAM). The inference process is performed using the Mamdani method, and the crisp output value is obtained through centroid defuzzification. Performance evaluation is conducted under five traffic density scenarios representing low to highly congested conditions by comparing the fuzzy-based system with a fixed-time control system. The performance indicators used include average vehicle waiting time, queue length, and intersection throughput. The experimental results show that the fuzzy-based system is able to reduce average waiting time by 18–25% and increase throughput by 15–20%, particularly under moderate to congested traffic conditions. These findings demonstrate that Mamdani fuzzy logic can produce more adaptive, responsive, and efficient signal control compared to conventional methods, indicating its strong potential as an effective solution for the development of intelligent transportation systems in urban environments.
Application of Natural Language Processing Based on Machine Learning and IoT Data Pratiwi, Adellia; Lubis, Erliani Syahputri; Rangkuti, Fiqri Hidayat; Suyudi, M. Karim; Jefry, Togap Aland
Pascal: Journal of Computer Science and Informatics Vol. 3 No. 01 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The development of the Internet of Things (IoT) and Natural Language Processing (NLP) has opened new opportunities to build intelligent monitoring systems capable of processing multiformat data simultaneously. This study aims to apply machine learning–based NLP methods to analyze IoT data in order to improve the accuracy of real-time environmental condition detection. The dataset used consists of temperature and humidity parameters collected from IoT sensors, as well as textual data in the form of environmental condition reports. The textual data are processed through tokenization, lowercasing, stopword removal, stemming, and lemmatization, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The Naive Bayes algorithm is employed to classify conditions into Normal, Warning, and Critical based on a combination of sensor data and textual features. The experimental results show that integrating NLP with IoT data increases classification accuracy from 82% (using sensor data alone) to 91% and enables automatic, real-time condition detection. This study demonstrates that multiformat data integration through NLP and machine learning can enhance the effectiveness of intelligent monitoring systems and can be implemented in environmental, industrial, healthcare, and security domains, thereby making a significant contribution to data-driven decision-making.
Optimization of Indoor Navigation Using the A Algorithm and Adaptive Grid (Gridadapte) for Efficient Pathfinding Ritonga, Asia Leny; Fransiska, Ega; Afdillah, Hafidz; Nainggolan, Johan Alfredo; Sobari, Rahmad Imam; Sinaga, Sony Bahagia
Pascal: Journal of Computer Science and Informatics Vol. 3 No. 01 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

Optimal path navigation in indoor environments is a crucial problem in the development of robotic systems and location-based services due to complex spatial structures, the presence of obstacles, and limited available pathways. The A* algorithm, as a heuristic-based pathfinding method, is widely used; however, its performance degrades on high-resolution grid maps because of the increasing number of nodes that must be explored. This study proposes the integration of the A* algorithm with an adaptive grid simplification method (Gridadapte) to improve pathfinding efficiency without sacrificing route quality. The research methodology includes grid-based indoor map modeling, the application of Gridadapte to reduce cell density in low-obstacle areas, and the implementation of the A* heuristic function for optimal path search. Performance evaluation is conducted through simulations on several indoor map scenarios by comparing conventional A* and Gridadapte-based A* in terms of the number of explored nodes, path length, and computation time. Simulation results show that the proposed approach significantly reduces the number of search nodes by 30–45% and accelerates computation time by 25–40% compared to A* on regular grids, while the resulting path length remains optimal and does not experience a significant increase. These findings indicate that Gridadapte is effective in reducing the A* search space while preserving the topological structure of the environment. Therefore, the combination of A* and Gridadapte is proven to enhance both the efficiency and accuracy of pathfinding in complex indoor environments. This approach has strong potential for application in autonomous robotic systems, smart building guidance systems, and location-based Internet of Things (IoT) applications in indoor settings such as hospitals, campuses, and shopping malls.

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