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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
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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 462 Documents
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Security Assessment of E-Commerce Website Using NIST SP 800-115 Based on OWASP Top 10 Arif Setyo Wibowo; Henni Endah Wahanani; Andreas Nugroho Sihananto
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1186

Abstract

The rapid growth of e-commerce platforms in Indonesia has increased the risk of cyber threats targeting sensitive user data, including personal information and payment details. PT. XYZ, a mattress company that recently launched its first e-commerce website, has attracted 42,222 visitors and generated revenue of Rp994,878,300 within its first six months, yet has never undergone any form of security testing. This raises serious concerns, as undetected vulnerabilities may expose the platform to identity theft, data breaches, and unauthorized access. This study aims to identify existing security vulnerabilities, determine the severity level of each finding, and provide concrete remediation recommendations before those vulnerabilities are exploited. The assessment was conducted using the NIST SP 800-115 framework across four phases: Planning, Discovery, Attack, and Reporting, with vulnerability classification based on OWASP Top 10 (2021). The Discovery phase utilized Google Dorking, WHOIS, wfuzz, Wappalyzer, Nmap, Burp Suite, and OWASP ZAP to gather intelligence and identify weaknesses. The Attack phase successfully exploited six confirmed vulnerabilities: Clickjacking, CSP Header Not Set, Vulnerable JS Library, Cross-Domain Misconfiguration, Source Code Disclosure, and Username Enumeration and Brute Force, mapped to OWASP categories A05, A06, and A07, with risk levels ranging from Medium to High. This research contributes by demonstrating that newly deployed platforms are not inherently secure and that integrating NIST SP 800-115 with OWASP Top 10 provides a structured approach to identifying real security vulnerabilities in e-commerce systems.
Pengembangan Aplikasi berbasis Virtual Reality untuk Sistem Teaching Robot pada SmartEdu Station Yuliadi Erdani; Sarosa Castrena Abadi; Ihsan Kamaludin; Abdur Rohman Harits Martawireja; Adhitya Sumardi Sunarya; Nuryanti Nuryanti
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1187

Abstract

Conventionally, the trajectory programming procedure for industrial robots using physical teach pendants imposes a high cognitive load, is prone to triggering structural collision accidents for novice students, and limits training efficiency due to the minimal availability of equipment. This study aims to solve these issues by developing an autonomous direct teaching system for the UR5e manipulator utilizing Virtual Reality (VR) technology and a Digital Twin architecture at the SmartEdu-Station facility. As a solution, the system captures global spatial coordinate inputs from the user's natural hand movements via 6-DoF VR controller tracking, then translates them into real-time virtual robot posture visualization using Inverse Kinematics computation. The trajectory coordinate sets are validated for safety as autonomous waypoint data before being transmitted to the physical controller. Empirical testing results prove that data communication via TCP/IP and RTDE protocols records a trajectory packet transmission success rate of 100%, with an average execution response delay consistently under 1 second. Furthermore, interface ergonomics testing using the System Usability Scale (SUS) questionnaire on 10 respondents yielded an average score of 79.0 (Good Category). This research contributes significantly by providing an offline teaching simulation framework that successfully eliminates the risk of physical equipment damage while interactively and safely reducing the learning curve of spatial mapping.
Deteksi Kondisi Terumbu Karang Menggunakan YOLO versi 8 pada Citra Bawah Laut Secara Real-Time Keysia Lestari Sasikome; Irham Aadiyaat Mohammad; Michael Owen Patindingo; Yonatan Parassa; Robby Tangkudung
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1191

Abstract

Coral reef ecosystems play an important role in maintaining the balance of the marine environment and supporting the marine tourism sector. However, coral reef damage due to climate change, pollution, and human activities continues to increase, requiring efficient and sustainable monitoring methods. This study aims to develop a coral reef condition detection system based on the YOLOv8 method by utilizing real-time underwater imagery. The research dataset was obtained from the coral reef conservation area of ??Bahoi Village, West Likupang, North Sulawesi. The research stages include dataset collection, image preprocessing, data augmentation, object annotation, YOLOv8 model training, model performance evaluation, and web-based detection system implementation. Model evaluation was carried out using precision, recall, mean Average Precision (mAP), and confusion matrix metrics. The test results showed that the YOLOv8 model was able to detect coral reef objects with good performance, indicated by a precision value of 76.51%, recall of 98.57%, mAP50 of 86.78%, and mAP50-95 of 86.77%. Confusion matrix analysis showed that the model did not misclassify coral reef species, while a small number of errors occurred only in objects detected as background due to underwater environmental conditions such as water turbidity and light refraction. The results showed that YOLOv8 is effective for detecting and monitoring coral reef conditions automatically and in real time, thus potentially supporting conservation activities and sustainable marine ecosystem management.
Implementasi Data Mining K-Means Clustering Untuk Pengelompokan Produk Keramik Berdasarkan Frekuensi, Volume, dan Jangkauan Penjualan Ferdian Arya Dinata; Alwis Nazir; Fadhilah Syafria; Teddie Darmizal; Eka Pandu Cynthia
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1195

Abstract

Ceramic inventory management at CV. Makmur Bersama has generally relied on intuition or partial sales data, without accounting for purchasing behavior patterns as a whole. This approach simultaneously creates two major risks: overstocking of slow-moving products, which burdens working capital and storage space, and stockouts of high-demand products, which can result in lost sales opportunities. This problem is further compounded by the limitation of stock data, which typically contains only a single quantitative variable such as the number of units sold and is therefore unable to comprehensively capture product demand characteristics, such as how frequently a product is purchased or how broad its customer base is. As a result, restocking decisions and promotional strategies are often poorly targeted. This research applies the K-Means algorithm to cluster ceramic products based on historical sales patterns as a solution to this limitation. Historical sales data from CV. Makmur Bersama for the 2025 period, consisting of 6,328 transactions, was processed into 417 unique products through a feature engineering approach using Frequency, Monetary, and Reach (FMR) namely transaction count, total quantity sold, and unique customer count per product. After outlier detection using the Interquartile Range (IQR) method, 381 products remained for the clustering process. The optimal number of clusters was determined using the Elbow Method, resulting in k=4 as the best cluster count. Evaluation using the Davies-Bouldin Index (DBI) produced a value of 0.8954, categorized as good, and stability testing across five iterations with different random states showed consistent results (DBI standard deviation of 0.0034). The clustering results produced Cluster 1 (190 products, 49.9%) as slow-moving products, Cluster 2 (34 products, 8.9%) as top-performing products with an average transaction frequency of 30.8 times, Cluster 3 (93 products, 24.4%) as potential products, and Cluster 4 (64 products, 16.8%) as products with limited demand. This research provides practical contributions for companies in determining restocking priorities, promotional strategies, and working capital efficiency based on actual sales patterns. This research contributes methodologically through the adaptation of the RFM framework into FMR to better suit real-world data constraints, as well as the integration of the Elbow Method, Davies-Bouldin Index, and stability testing as a comprehensive validation mechanism. Practically, the segmentation results can be directly utilized by the company as a basis for restocking priorities, promotional strategies, and working capital allocation efficiency based on actual sales patterns.
Rancang Bangun Website Billing Pada Penyewaan Playstation Menggunakan Metode Waterfall Erwin Irawan; Novi Tristanti
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1211

Abstract

The process of recording usage duration and calculating tariffs at the AS2 Lalung PlayStation rental business is still carried out conventionally using notebooks and calculators. This method leads to various problems, such as errors in recording the start and end times of play, inaccuracies in rental cost calculations, and difficulties in compiling daily, weekly, and monthly income reports. Furthermore, the manual system cannot properly document transaction history in a structured manner, making it difficult for business owners to monitor revenue and make business decisions. This study aims to design and build a web-based billing system capable of automatically recording rental duration, accurately calculating costs, and presenting structured and real-time income reports. The development method used is Waterfall, which includes the stages of requirement identification, system design (using UML, ERD, and mockups), implementation (using PHP, the CodeIgniter 4 framework, and MySQL database), and testing using a black-box testing approach. The main contributions of this research include the application of a 15-minute time rounding algorithm using the ceil() function, which is more proportional and fair for customers compared to conventional hourly systems, as well as the development of a lightweight, easy-to-implement web-based billing system specifically designed for the internal needs of small-to-medium-scale PlayStation rentals. In addition, this system integrates dynamic PlayStation unit management features and multi-filter income reports that are not yet available simultaneously in similar PlayStation rental systems. The results show that the system successfully records start and end times in real-time, calculates fees based on hourly rates (IDR 5,000 for PS3 and IDR 8,000 for PS4), stores all transaction data in the database, and automatically generates daily, weekly, and monthly income reports. Functional testing on 18 scenarios shows that all key features, such as admin login, PlayStation unit management, rental processing, invoice generation, and report presentation, function as required (100% success rate). With this system, the risk of recording errors can be significantly reduced, and service efficiency increases by more than 75% compared to manual methods. Future research can develop online booking features, digital payment integration, and report export to PDF or Excel formats to further expand system functionality.
Implementation of the Analytical Hierarchy Process (AHP) with AI-Assisted Validation for Waste Processing Method Selection Agung Firdausi Ahsan; Tri Dewi Sugiharti; Novi Wahyuningtias
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1227

Abstract

The increasing volume of municipal solid waste in Sumenep Regency has created significant challenges for local authorities in selecting an effective and sustainable waste treatment method. The selection process requires consideration of multiple criteria, including economic, technical, environmental, social, labor, and material recovery aspects. Therefore, this study aims to determine the most suitable waste processing method by applying the Analytical Hierarchy Process (AHP) as the primary decision-making approach and an AI-assisted validation approach as a comparative evaluation tool. The AHP method was used to calculate the relative importance of criteria and rank three waste treatment alternatives, namely Composting, Sanitary Landfill, and Incineration, based on expert judgments. To strengthen the reliability of the decision-making process, an AI-assisted evaluation using a Large Language Model (LLM) was conducted to assess the same alternatives according to the established criteria and compare the resulting rankings with those obtained from AHP. The results of the AHP analysis indicate that Composting has the highest priority weight of 48.0%, followed by Sanitary Landfill with 33.5% and Incineration with 18.5%. Similarly, the AI-assisted evaluation generated the highest score for Composting (0.9835), followed by Sanitary Landfill (0.6130) and Incineration (0.6025). The consistency between the rankings produced by AHP and the AI-assisted assessment demonstrates the robustness of the selected alternative. The findings suggest that Composting is the most appropriate waste treatment method for Sumenep Regency due to its superior environmental performance, social acceptance, and material recovery potential. Furthermore, the study highlights the potential of AI-assisted evaluation as a supporting validation tool for enhancing multi-criteria decision-making in waste management planning.
Identifikasi Penggunaan Chat GPT Pada Esai TOEFL Menggunakan Metode Long Short Term Memory Karina Natasya Darmawan; Silvester Dian Handy Permana; Ketut Bayu Yogha Bintoro
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.772

Abstract

The use of Artificial Intelligent (AI) technology is increasing along with technological developments. One of the technologies that is often used is Chat GPT (Generative Pre-trained Transformers). Chat GPT is an application used for many things such as source of information, write an essay, and answer TOEFL essay questions. Because of its easiness, people will excessively use this that can cause people to lose creativity because they do not understand the material context and rely too much on the AI text result, which poses academic risks. Teachers also have difficulty to distinguish between AI and human text writing. Therefore, this research is to identify whether TOEFL essay are result of human text or GPT. This research used the Long Short Term Memory (LSTM) method to identify the use of GPT in TOEFL essay. This research also used 3 different split data configurations to find the best results. This research consists of 2 TOEFL essay datasets with the same prompt and has total of 220 data samples. The LSTM method is a modification of algorithm Recurrent Neural Network (RNN) and part of Deep Learning. The LSTM method involves memory cell controlled by three gates, such as input gate, forgot fate, output gate, and the hidden state. The gates are used to decide and control the information added, deleted, and removed from memory cell. The results of this research is a system that can help teachers detect the use of GPT in TOEFL essay. This research successfully identified the use of GPT in TOEFL essay in a 70:30 data split configuration with a loss score of 25,07%, accuracy score of 89,83%, and prediction score of 64,32%. Therefore, it is hoped that this system can help teachers identify the use of GPT and facilitate the assessment of TOEFL essay.
Development of Chatbot Integration in Personal Finance Management Applications Using Generative Pre-Trained Transformer (GPT) 4o Sawali Wahyu; Tomflynn Beltsazar
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.776

Abstract

Financial management is considered a crucial aspect considering the importance of financial matters in life. However, based on the survey of 13 Employees and 2 Parallel Students of Esa Unggul University revealed that most don't regularly record their finances. The reasons cited includes time constraints and lack of understanding. Additionally, none of the respondents used a financial recording application due to perceived complexity and inefficiency. To overcome this, a chatbot-based personal finance management application was built. The chatbot feature in this application is supported by the GPT-4o model and implemented by performing rapid engineering, which aims to simplify and streamline financial records. The app will be developed using the Mobile-D method, which includes five stages: explore, initialize, productize, stabilize, and system test & fix. The purpose of this study is to integrate chatbot features to simplify and accelerate the financial recording process. To evaluate its effectiveness, the app will undergo System Usability Scale (SUS) and User Acceptance Testing (UAT). The SUS resulted in a score of 77.16, categorized as "Good," while the UAT showed positive user feedback. These results show that the integration of chatbots with financial applications has successfully helped users manage their finances more easily and efficiently. This study proves the potential of chatbot technology integration in personal finance management, offering user-friendly solutions to overcome common obstacles in financial record keeping.
Implementasi Metode SMART dalam Sistem Pendukung Keputusan Pemilihan Pantai Terbaik Marsono Marsono; Karina Andriani; Cindy Vivin Avilia; Evi Rosalina Widyayanti; Asyahri Hadi Nasyuha; Dedi Rahman Habibie
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.781

Abstract

Beaches in Bantul Regency, Yogyakarta, such as Parangtritis, Depok, Samas, Goa Cemara, and Baru Beaches, attract tourists for their natural beauty, recreational activities like sandboarding, and local traditions like the Labuhan ceremony. However, tourists struggle to find the best destination among the many beach options based on factors such as ticket price, distance from the city center, cleanliness, facilities, and culinary experiences. Using the Simple Multi-Attribute Rating Technique (SMART) method, this study aims to provide recommendations for the best beaches in Bantul. SMART is a method that offers an objective approach to overcome the limitations of subjective reviews on social media. Data were collected from 31 tourists through field observations and online questionnaires from August to September 2025 to assess the relevance of the criteria and the level of tourist satisfaction with the five beaches. Field observations examined facilities and cleanliness, and the questionnaire assessed tourist preferences using a Likert scale. Baru Beach was the best destination according to the SMART analysis with a score of 0.971, excelling in cleanliness and facilities. Followed by Goa Cemara Beach with a score of 0.968, Parangtritis with a score of 0.962, Depok with a score of 0.959, and Samas with a score of 0.916. These results help beach managers improve facilities and promotions, while also helping tourists choose beaches based on their preferences, such as affordability or appealing local cuisine. Furthermore, this research encourages coastal environmental conservation and increases tourist visits, driving local economic growth in Bantul.
Pengembangan Game Teka-Teki Silang Berbasis Mobile Interaktif Bertingkat untuk Meningkatkan Kemampuan Berhitung Siswa Sekolah Dasar Sela Taramita; M Riski Qisthiano
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.787

Abstract

This study developed the MathCross Adventure application, a mobile-based arithmetic crossword puzzle game for elementary school students, using the R&D approach with the ADDIE model. In this study, the stages were implemented up to Implementation and Formative Evaluation (not summative evaluation). Validation was carried out by two subject matter experts/elementary school mathematics teachers and one learning media expert, resulting in an average feasibility of 88.8% (very feasible category). A limited trial was conducted at SD Negeri 27 Banyuasin with 20 sixth-grade students; the results showed the application was easy to use, engaging, and supported learning engagement through its tiered level features, leaderboard, and instant feedback. These findings confirm the product's formative feasibility for use in arithmetic learning. However, summative effectiveness (statistical improvement in arithmetic ability) has not been tested; further research is recommended using the N-Gain test or paired t-test with a larger sample to empirically verify the learning impact.

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