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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
Core Subject : Science,
Computer Science & IT
Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, Fault analysis, and Diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data • Cryptography • Model and Simulation • Image Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 4 (2024): June 2024" : 5 Documents clear
Perancangan Aplikasi Manajemen Surat Keluar Responsive Dengan Bootstrap Berbasis Web Aditya Ramadhani; Andrean Akbar Permana; Azlam Abi Yansah; Affan Setya Fahriza; Hendry
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.342

Abstract

The objective of this project is to develop a Responsive Outgoing Mail Management Application utilizing Bootstrap for the Household and Bappeda Division of Medan City. The objective is to enhance productivity by efficiently gathering, analyzing, and utilizing data inside a commercial entity. Furthermore, he incorporates the assessment and development of a web-based archive management information system for handling incoming and outgoing mail at the Jambi DPRD City Secretariat. The system employs Unified Modeling Language (UML) for the purpose of system modeling. Furthermore, this study highlights the significance of establishing a comprehensive archive system for both incoming and departing emails. The system will be implemented utilizing the Model View Controller (MVC) paradigm, renowned for its efficacy in structuring and overseeing documents throughout the business. The term to summarize, the suggested application for overseeing outgoing mail in the Household Division and Bappeda Medan City aligns with prior research on enhancing effectiveness through web-based systems in various organizational settings. By combining UML modeling and MVC approaches, it is possible to develop apps that effectively streamline outgoing email processes. This research enhances the field of information management systems by offering valuable insights into the development of responsive web apps customized to fit the specific needs of enterprises. This subsequently enhances operational efficiency and optimizes data management processes.
Pengembangan Front-End Edu-Perikanan Pada Program Smart Fisheries Village Bangsring dengan Metode UCD dan UEQ Rohman, Thoifur; Hadiq
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.353

Abstract

The development of the front-end Edu Fisheries in the Smart Fisheries Village (SFV) Bangsring program using User-Centered Design (UCD) and User Experience Questionnaire (UEQ) aims to improve access to and quality of fisheries education. SFV Bangsring faces specific issues such as the low level of education and awareness among the local community regarding sustainable fishing practices, as well as the ineffectiveness of previous conventional approaches. The UCD method is used to understand user needs and design an intuitive user interface, while the UEQ method is used to evaluate user satisfaction with the developed system. The development results show a significant improvement in the accessibility and quality of fisheries education, with a 40% increase in fishermen's knowledge about sustainable fishing practices and a 35% increase in user satisfaction compared to previous approaches. The evaluation with UEQ covers aspects of attractiveness, clarity, efficiency, accuracy, stimulation, and novelty, all of which show positive average score increases. This research emphasizes the importance of a user-centered approach in designing educational systems that respond to user needs, as well as the significant positive impact on improving the accessibility and quality of fisheries education, and awareness and sustainable fishing practices in the SFV Bangsring environment. The implementation of this technology is expected to continue supporting the empowerment of the fishing community through better and more sustainable education.
Pemanfaatan Pengenalan Citra Kematengan Jengkol Untuk Saran Masakan Menggunakan Metode Algoritma Deep Learning Sanjaya, Handika; Kurniawan, Rudi; Rusdiyanto
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.358

Abstract

Currently, technological development is progressing rapidly. One of the areas is in artificial intelligence and computer vision technology. One of the branches of science studied in artificial intelligence and computer vision technology is deep learning, which focuses on the use of artificial neural networks that learn about classification and object detection directly through images and videos. With the advent of deep learning, researchers are focusing on developing a jengkol (dogfruit) ripeness detection system using deep learning methods. This research uses 1500 jengkol images as a dataset and uses Roboflow for labeling. The results of the labeling will be divided into 3 types of classes: young, medium, and old jengkol. The YOLOV5 algorithm is used for training the jengkol dataset. The next stage is testing, where the approaches used are confusion matrix, classification report, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), as well as the F1-Score value. The purpose of this testing is to see the precision results and identify the optimal accuracy on data from the trained model that can be achieved by the system being tested or evaluated, which involves compiling a classification report into three categories: young, medium, and old, based on visual characteristics that can be detected by the deep learning algorithm. From the real-time testing and evaluation results obtained in this study, the accuracy value is 80%.
Implementasi dan Pengujian Menggunakan Metode BlackBox Testing Pada Sistem Informasi Tracer Study Zen, Muhammad; Irwan; Hafni; Ananda, M. Dea Putra
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.359

Abstract

The research is aimed at implementing and testing the information systems of the Tracer Study at Panca Development University using the BlackBox Testing method. The BlackBox test method is chosen to evaluate the functionality of the system without having to know the internal details of that system. Previous research results showed that BlackBox testing was effective in identifying functional errors without requiring in-depth knowledge of the internal structure of the system. In the context of information system development, the use of testing methods such as BlackBox Testing becomes essential to ensure that the developed system functions properly according to the needs of the user. Implementation of this method on the Tracer Study information system is expected to improve the quality of the system and ensure that the system can provide accurate and useful information to the user. Thus, the research contributes to the development of the information system Tracer Studies in the higher education environment, in particular Panca Budi Development University, with a testing approach that focuses on the external functionality of the systems. The results of the implementation and testing using the BlackBox Testing method are expected to improve the quality and reliability of the Tracer Study information system that can support better decision-making in the future.
Implementation of Entropy and Additive Ratio Assessment Methods in Determining the Best Warehouse Location Waqas Arshad, Muhammad; Setiawansyah; Mesran
Bulletin of Computer Science Research Vol. 4 No. 4 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i4.360

Abstract

The main problems in determining warehouse location include high operational costs, inadequate accessibility and infrastructure, and strict and complex local regulations. In addition, environmental risks and natural disasters, as well as economic fluctuations and market dynamics also add to the challenge of choosing an optimal location. The purpose of this study is to apply the Entropy and ARAS methods in evaluating the potential of optimal warehouse locations, thereby providing clear and structured guidance for decision-makers in choosing the optimal warehouse location to meet the company's operational and strategic needs. The implementation of the entropy and ARAS methods in determining the best warehouse location involves a systematic approach in evaluating several criteria that are important for optimal decision-making. The Entropy method helps in objectively assessing the importance of each criterion by quantifying the uncertainty or variation present in the data. The ARAS method complements this by allowing comparative analysis of alternative warehouse locations based on their performance against ideal criteria, taking into account both quantitative and qualitative aspects. Thus, both methods provide a solid framework for selecting warehouse locations that not only meet logistics requirements efficiently but are also in line with strategic business objectives, ensuring a well-informed decision-making process in supply chain management and logistics. The results of the ranking of the best warehouse location selection using the entropy and ARAS methods show AB Location as the first rank with a value of 0.9781, DD Location as the second place with a value of 0.8362, and IP Location as the third place with a value of 0.8143.

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