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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 37 Documents
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Decision Support System for Determining Effective Learning Strategies for Students Using the SMART Method Athaya, Fara; Simanjuntak, Magdalena; Sitompul, Melda Pita Uli
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 02 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

Effective learning strategies are essential factors in improving students’ academic achievement. However, at SMP Negeri 2 Binjai, several challenges remain, including the low effectiveness of applied learning methods, the lack of adaptation to individual learning styles, and the limited use of academic data in supporting learning decisions. These issues were further exacerbated by the post-pandemic shift toward hybrid learning models, which has not been fully optimized. To address this problem, this study designed a Decision Support System (DSS) using the SMART (Simple Multi-Attribute Rating Technique) method to recommend suitable learning strategies for students. The system was developed through stages of requirement analysis, logical design of the SMART calculation, and the implementation of integrated multi-criteria processing. The results show that the system can provide objective and accurate learning strategy recommendations. From 32 students analyzed, 11 students (34.37%) were recommended to adopt E-learning, 7 students (21.87%) to use Blended Learning, and 14 students (43.75%) to apply Traditional Learning. The highest score of 1.00 was achieved by two students in the E-learning category, while the lowest score of 0.125 was recorded in the Traditional category. These findings confirm that the application of the SMART method in DSS is effective in helping teachers and students determine more adaptive and personalized learning strategies, thereby supporting the improvement of learning quality in schools.
A Decision Support System for the Selection and Distribution of Superior Durian Seedlings to the Community Using the Decision Tree Method Danisuwara, Ardiya Kansya; Manurung, Hotler; Simanjuntak, Magdalena
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 02 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The durian fruit is an agricultural commodity with high economic value and strong demand both domestically and internationally. However, the success rate of durian cultivation in Indonesia remains relatively low, at approximately 30.3%. This is partly due to the limited experience of farmers in managing durian plantations and the absence of an objective system for selecting eligible recipients of superior seedlings. Inaccurate selection of seedling recipients can lead to low productivity, suboptimal fruit quality, and an imbalance between market supply and demand. To address these issues, this study proposes the development of a Decision Support System (DSS) for the selection of superior durian seedling recipients using the Decision Tree algorithm. The study identifies several factors influencing eligibility, including age, land area, land ownership, farming experience, socioeconomic status, number of plants, water availability, membership in farmer groups, regional location, and education level. Data from 300 respondents were collected and processed through several preprocessing stages, including categorical data encoding, numerical data binning, normalization, and the division of training and testing datasets. The Decision Tree model was developed using the Scikit-learn library in the Python programming language, with the Gini index as the splitting criterion. The experimental results indicate that the model achieved an accuracy of 85%, a precision of 90%, and a recall of 95% for the "Eligible" class, demonstrating the system’s effectiveness in accurately identifying qualified recipients. The system was implemented as a GUI-based desktop application using Tkinter, equipped with features for data input, eligibility prediction, recipient data management, and statistical visualization. The implementation of this system is expected to enhance objectivity, efficiency, and accountability in the distribution of superior durian seedlings, thereby contributing to increased productivity among durian farmers and promoting better market equilibrium.
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.
Design Of Accounting Information System for Transaction Management and Financial Reports at the Medan Pratama Haji Clinic Daeli, Cosmas Samuel; Lumbanbatu, Maristella J.; Naibaho, Anggi Wulandari
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 rapid development of information technology requires healthcare institutions to digitize their financial data management to make administrative processes more effective and accurate. Medan's Pratama Haji Clinic currently still uses a manual system for recording transactions and preparing financial reports, resulting in frequent reporting delays, recording errors, and difficulties in tracking historical data. Based on these conditions, this study formulates three main problems, namely: how is the manual accounting information system currently implemented at Medan's Pratama Haji Clinic, what are the obstacles faced in managing financial reports manually, and how to design an accounting information system for managing transactions and financial reports. The purpose of this study is to design and build a computer-based accounting information system that can assist the process of recording transactions and preparing financial reports effectively at Medan's Pratama Haji Clinic. The research method uses the System Development Life Cycle (SDLC) with stages of analysis, design, implementation, and testing. Data were obtained through observation, interviews, and documentation. The system was designed using PHP and MySQL with the help of DFD, ERD, and Context Diagram tools. The results show that the system built is able to integrate all financial transaction processes and produce reports automatically, accurately, and efficiently. This system also facilitates management's oversight and decision-making. The study concluded that a computer-based accounting information system can replace manual systems and improve the efficiency of the clinic's finance department. A recommendation for further research is to develop this system with a web-based automated reporting module to make financial information more transparent and accessible.
A SAW-Based Decision Support System for Selecting Final Project Topics in the Informatics Management Department at STMIK Mulia Darma Siregar, Kristian; Siagian, Edward Robinson; Tampubolon, Kennedi
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

Selection of Final Project Topics is a crucial stage in the academic process, as it significantly affects the smoothness of project preparation and completion. However, the determination of final project topics often remains subjective and does not systematically consider students’ academic abilities. This study aims to design and develop a Decision Support System for selecting final project topics for students in the Informatics Management Department using the Simple Additive Weighting (SAW) method. The SAW method is employed to evaluate and rank alternative topics based on several criteria, including supporting course grades, student interest, programming skills, system analysis and design capabilities, and the availability of supervising lecturers. The results indicate that the SAW method can provide objective and transparent recommendations for final project topics. Based on the calculation, alternative A4 achieved the highest preference score of 0.962, making it the most recommended final project topic. Therefore, the developed system is expected to assist both students and academic staff in making more effective and structured decisions regarding final project topic selection.

Page 4 of 4 | Total Record : 37

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