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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 329 Documents
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Deteksi dan Klasifikasi Kendaraan Berbasis Algoritma You Only Look Once (Yolov7) Rohiman, Yusuf Kautsar; Bulkis Kanata; L Ahmad S Irfan Akbar
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.509

Abstract

The increasing traffic density in Indonesia highlights the need for an accurate vehicle detection system to support infrastructure planning. This study aims to implement the YOLOv7 algorithm for detecting and classifying various types of vehicles in traffic images. The method involves training the model using Google Colab on a Kaggle dataset consisting of 6,633 images, with a batch size of 1, 19 training epochs, and optimization using the Stochastic Gradient Descent (SGD) algorithm. The training results show that the model achieved a precision of 93.22%, recall of 90.64%, mAP@0.5 of 94.27%, and mAP@0.5:0.95 of 69.19%, with a total training time of 1 hours. In conclusion, the YOLOv7 algorithm is effective for vehicle detection and classification, although increasing the number of training epochs is recommended to further enhance model performance.
Pendekatan Hybrid Respond to Criteria Weighting dan Utalities Theory Additives untuk Pemilihan Supplier Bahan Baku dalam Industri Makanan A’yun, M Qurrota; Priandika, Adhie Thyo
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.516

Abstract

Choosing the right raw material supplier is a crucial factor in maintaining quality and production continuity in the food industry. This study proposes a hybrid approach that combines the Respond to Criteria Weighting (RECA) method to objectively determine the weight of the criteria and the Utilities Theory Additives (UTA) method to evaluate alternatives based on partial utility functions. This approach is designed to accommodate the complexity of decision-maker preferences as well as the multi-criteria assessment dynamics that often occur in the supplier selection process. Case studies were conducted on several supplier alternatives by considering various criteria. The results of the ranking of alternative suppliers based on the combination of the RECA and UTA methods can be seen that the MB alternative obtained the highest score of 0.8578 as the first rank, followed in order by TM obtained a score of 0.8576 as the second rank, and AJ obtained a score of 0.8573 as the third rank. The results of the analysis show that the combination of the two methods is able to produce accurate, consistent, and relevant ratings to the strategic needs of the company. This approach makes a significant contribution to improving objectivity, transparency, and efficiency in decision-making, particularly in the food industry sector which relies heavily on supply chain reliability.
Penerapan Kombinasi Metode Entropy dan SMART Dalam Pemilihan Kepala Divisi Keuangan Yusran, Muhamad; Priandika, Adhie Thyo
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.517

Abstract

The election of the Head of the Finance Division is an important decision that requires an objective and systematic evaluation of the existing candidates. This study proposes the application of a combination of Entropy and SMART (Simple Multi-Attribute Rating Technique) methods to support the decision-making process in the election of the Head of the Finance Division. The Entropy method is used to objectively determine the weight of the criteria, based on the distribution of candidate assessment data, while the SMART method is applied to assess each candidate based on predetermined criteria. The results of the ranking of candidates for the Head of the Finance Division are based on the final score obtained by each candidate. Based on these results, candidate A5: Eko Prabowo ranks highest with a score of 0.6667, followed by A7: Gita Susanti with a score of 0.6097. These results show that Eko Prabowo is the most superior candidate to be considered as the Head of the Finance Division, based on the assessment method used in this study. The combination of these two methods allows for more accurate, transparent and accountable decision-making, as it is based on objective and structured calculations.
Sistem Pendukung Keputusan Pemberian Kredit Kendaraan Menggunakan G2M Weighting dan Metode Comprehensive Distance Based Ranking Simamora, Parningotan; Priandika, Adhie Thyo
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.518

Abstract

Providing vehicle loans is one of the important services in the financing sector that requires an objective and accurate evaluation process of prospective debtors. This research aims to develop a Decision Support System (SPK) that can assist in the selection process of providing vehicle credit by combining the G2M Weighting and Comprehensive Distance-Based Ranking methods. The G2M method is used to objectively determine the weight of criteria based on multi-assessment analysis, while the CDR method is used to conduct alternative rankings based on a comprehensive distance to ideal and non-ideal solutions. The results of the calculation using the comprehensive distance-based ranking method, Gita ranked first with a final value of -0.0098, showing that it has the closest distance to ideal conditions and the furthest from non-ideal conditions compared to other alternatives. In second place is Ahmad with a value of -0.0067, followed by Hadi in third place with a value of -0.0042. The final results show that the combination of these two methods is able to provide effective recommendations in identifying potential debtors who are most deserving of credit, taking into account all assessment criteria in a comprehensive and structured manner. This system is expected to improve decision-making accuracy, speed up the selection process, and minimize the risk of errors in vehicle lending.
Qualitative Analysis of Shadow IT Practices in Higher Education to Identify IT Security Needs Ahmad Aunul Bari Hayiz; Arif Wibisono
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.520

Abstract

The presence of Shadow IT in educational settings often stems from users' attempts to fulfill work needs that are not met by formal systems. These tools, typically accessed outside institutional infrastructure, are preferred due to their practical use in day-to-day activities. However, such informal practices have been observed to trigger a variety of technical and security-related issues that remain undocumented by institutional IT policies. These tools may also pose significant risks to information security and work system reliability. This research aims to examine recurring problems encountered by users and interpret how these issues relate to key components in the work systems such as participants, technologies, information, processes, and products/services. The study employed a qualitative case study and applied the Eisenhardt method (1989) approach involving 35 respondents through interviews and field observations. Data was analyzed using open coding to extract recurring problem patterns. The analysis revealed four primary categories of problems System Error, Slow System Response, Access Limitations and Data Loss. These findings indicate that Shadow IT disrupts the flow of information and affects both technological and human elements within the work system. The study contributes by mapping factual user difficulties to concrete IT security needs that extend beyond formal policies. It suggests that effective IT security must address not only technical safeguards but also user behavior, access reliability, and adaptive policies capable of integrating informal digital practices.
Desain Sistem Computer Assisted Test (CAT) Pada Olimpiade Sains Nasional (OSN) Tingkat Provinsi Lampung Berbasis Website dengan Metode SCRUM Melda Agarina; Arman Suryadi K; Sutedi; Muh Royan Fauzi M; Hendri Purnomo; Reyhandoni
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.522

Abstract

The National Science Olympiad (OSN) is a prestigious competition that requires an accurate, efficient, and transparent assessment system. This study aims to design and develop a web-based Computer Assisted Test (CAT) system for the implementation of OSN at the provincial level in Lampung. The system was developed using the Scrum method, an iterative and incremental software development framework consisting of sprint planning, daily scrum, sprint review, and sprint retrospective stages. The development team consisted of a Scrum Master, Product Owner, and developers, with structured backlog planning and evaluation in each sprint. The CAT system includes features such as question creation, test time configuration, question randomization, automatic scoring, and test result analysis. Testing results indicate that the system meets user needs, enhances assessment efficiency and objectivity, and provides ease of use for participants. This system can be implemented in provincial-level OSN events and serves as an innovative solution to support technology-based assessment systems.
Model Machine Learning Untuk Analisis Sentimen Masyarakat Terhadap Kenaikan PPN di Media Sosial X Ridho Pratama, Ilham; Cahyana, Yana; Rahmat; Wahiddin, Deden
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.523

Abstract

This study examines people's reactions to the Indonesian government's plan to adjust the VAT rate from 11% to 12%, which is scheduled to take effect in 2025. This policy triggered a variety of opinions among netizens, especially on the social networking service X. To explore public opinion, data was collected through web crawling techniques from October to December 2024, resulting in 1,871 records. Then the dataset was preprocessed by text cleaning, case folding, tokenization, stopword removal, and stemming, and the dataset was reduced to 1806. In addition, up to 1000 data will be manually labeled, negative, neutral, positive, by language experts to ensure that each sentence has the appropriate label. These data are used for testing and training, then up to 806 unlabeled data are used as final testing. At the word weighting stage, the Term Frequency-Inverse Document Frequency (TF-IDF) method is used to perform the process. In this study, three machine learning algorithms were used to compare the classification performance, namely Support Vector Machine (SVM), Random Forest, and Decision Tree. Based on the evaluation results, the SVM algorithm recorded the highest accuracy rate of 94%, followed by Random Forest with 93% and Decision Tree with 91%. The results showed a predominance of negative sentiments, indicating public dissatisfaction with the policy. This study proves that machine learning techniques can be effectively used to capture public perceptions through social media, which in turn can be a benchmark for the government to make decisions that will be enforced.
Peningkatan Performa Naive Bayes dengan Fitur Chi-Square pada Analisis Sentimen Komentar Pengguna Aplikasi Netflix Jusia, Pareza Alam; Pahlevi, Riza; Pardamean Simanjuntak, Daniel Sintong; Jasmir
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.532

Abstract

This study discusses sentiment analysis using the Naïve Bayes algorithm with Chi-Square. The purpose of this study is to determine the effect of Chi-Square feature selection on the performance of the Naïve Bayes algorithm in analyzing document sentiment. The research data was taken from Netflix Application user comments. Testing was carried out by analyzing document sentiment with and without Chi-Square feature selection. Furthermore, it was evaluated using the accuracy, precision, and recall methods. The results of this study are that the addition of CS features to NB significantly improves all evaluation metrics, especially recall and F1-score, indicating that additional features help improve the model's ability to understand data. The combination of NB + CS with a 70:30 split gives the best results, making it the optimal choice.
Implementasi Sistem Pengelolaan Program Mentoring Mahasiswa Baru Menggunakan Metode Prototyping Noor Dhiana, Feby Alya; Supriyono; Abid Yanuar Badharudin; Achmad Fauzan
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.538

Abstract

The mentoring program at the University of Muhammadiyah Purwokerto (UMP) organized by the Institute for the Study and Practice of Islam (LPPI) was first implemented in the 2004/2005 academic year. This program aims to provide guidance in understanding and internalizing the values of Al Islam and Muhammadiyah (AIK) in daily life. However, until now, it  still uses a manual system in recording attendance, managing materials, and monitoring mentoring activities. This causes several problems, such as lack of efficiency in administration, difficulties in data reporting, and limited access for mentors and mentees. Therefore, a website-based information system is needed  to optimize mentoring management. This research aims to design a website-based application  that simplifies the mentoring administration process, improves the efficiency of attendance recording, and provides easier access for mentors, mentees, coaches, and  LPPI admins. The method used in the development of this system is prototyping, which consists of several stages, including gathering needs, making  initial prototyping, evaluation by users, and developing the system based on the feedback received. This system is built using website-based technology  using the PHP programming language  with the Laravel framework, MySQL is used as a database, for the interface display, Tailwind CSS is used. The results of the study show that this mentoring system can improve the efficiency of mentoring administration with an online attendance process, activity reporting in the form of a mentor logbook and activity evaluation. In addition, this system allows  LPPI admins to monitor the course of mentoring in real time, so that transparency in mentoring activities increases. The test with black-box testing stated that all the main functions could run well according to their functions, then for the test with SUS (System Usability Scale) it showed that the average value of the SUS test was 84.16 and was stated as very good. It is hoped that the mentoring process at UMP can run more effectively, efficiently, and structured.
Penerapan NLP Pada Chatbot Telegram Untuk Informasi Seputar Handphone Umam, Arfiyan Khusnul; Esti Wijayanti; Ahmad Abdul Chamid
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.540

Abstract

This research develops a chatbot based on the Telegram platform that integrates Natural Language Processing (NLP) technology to provide information about mobile phones quickly, accurately, and efficiently. With the increasing need for users to access data regarding specifications, prices, reviews, and device damage diagnosis interactively, this chatbot becomes a relevant solution in supporting digital literacy and improving user experience. The system was developed using Agile methodology through stages of needs analysis, interface design, Telegram API implementation, and NLP integration with BERT architecture for intent recognition and named entity recognition. Data was collected through web scraping from e-commerce platforms, technology review websites, and community forums, then structured in a MongoDB database. Main features include product specification searches, damage identification, latest news, and interactive guides that support device problem-solving. White Box Testing showed satisfactory results with Statement Coverage 92%, Branch Coverage 88%, Intent Recognition accuracy 87%, Response Time 2.1 seconds, and Query Success Rate 97.5%. Evaluation results confirm that the chatbot is able to perform its functions responsively and practically, ready for deployment, and has potential to be expanded to other digital platforms to increase information technology competitiveness in the digital era.

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