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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 462 Documents
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Sistem Pemesanan Restoran Berbasis QR Code dan Kitchen Display System Muhammad Irzan; Boy Yuliadi
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.v6i3.1001

Abstract

This research was motivated by the ongoing manual food ordering process at the Bakmi Ayam Bangka Chandra Restaurant, resulting in service delays, potential recording errors, and a lack of integration between customers, cashiers, and the kitchen. This situation impacts operational efficiency and customer satisfaction levels. The objective of this research was to design and develop a web-based food ordering information system integrated with the Kitchen Display System (KDS) to improve the speed, accuracy, and effectiveness of the restaurant's service process. The research method used was a qualitative approach, with data collection techniques including observation, interviews, and literature review. The system was developed using the Waterfall method, encompassing the stages of needs analysis, system design, implementation, and testing. The designed system allows customers to place orders independently by scanning a QR code, while order data is sent in real time to the kitchen and cashier without manual recording. The result of this research is an integrated ordering system that accelerates service flow, reduces the risk of communication errors, and provides more structured transaction recording. Implementing this system improves restaurant operational efficiency, enhances service responsiveness, and optimizes order management.
Identifikasi Jenis Buah Apel berdasarkan Ektraksi Ciri Warna Fitur HSV dengan Model Jaringan Syaraf Tiruan Backpropagation Ayu Ratna Juwita; Cici Emilia Sukmawati; Adi Rizky Pratama; Resi Sujiwo Bijokangko; Agung Susilo Yudha Irawan
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1010

Abstract

Automatic identification of apple varieties is one of the challenges in the field of digital image processing, especially due to the similarity of visual characteristics between varieties and the influence of lighting conditions. This study aims to develop an apple variety classification system based on color feature extraction in the HSV (Hue, Saturation, Value) color space combined with Gray Level Co-occurrence Matrix (GLCM) texture features and classified using a Multilayer Perceptron (MLP) Artificial Neural Network. The research process begins with apple image segmentation using the Otsu thresholding method to separate objects from the background, followed by extraction of HSV color features and texture features in the form of contrast and energy. The obtained feature data is then normalized using StandardScaler and divided into training data of 80% and test data of 20%. The MLP model is trained with two hidden layers of 64 and 32 neurons, using the ReLU activation function and the Adam optimization algorithm with a maximum of 500 epochs. The test results show that the developed system is able to achieve a classification accuracy of 87.5% on the test data. These results indicate that the combination of HSV color features and GLCM texture classified using Backpropagation Neural Network is quite effective in identifying apple types, although there are still challenges in classes that have similar color characteristics.
A Web-Based Information System for Early Childhood Education Licensing Governance using Extreme Programming Whempy Setya Anggara; Ari Muzakir
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1011

Abstract

The management of licensing data for Early Childhood Education (ECE) institutions at the Department of Education and Culture of Ogan Ilir Regency previously relied on fragmented spreadsheet-based systems, resulting in data redundancy, limited accessibility, low traceability, and a high risk of information loss. This study aims to develop a centralized web-based licensing information system using the Extreme Programming (XP) methodology to support digital governance and administrative efficiency. The system was developed through four iterative cycles based on user stories and continuous feedback from administrative staff. The application was implemented using the Laravel framework and a MySQL database to ensure system security and data integrity. Functional evaluation was conducted using Black-box Testing and User Acceptance Testing (UAT). The testing results indicate that the system achieved 100 percent functional accuracy and reduced average licensing data processing time from approximately 15 minutes per record to less than 2 minutes. In addition, automated PDF document generation decreased report preparation time from about 20 minutes to less than 10 seconds. User acceptance evaluation showed a satisfaction level of 88%, indicating high usability and operational suitability. These results demonstrate that the application of Extreme Programming, particularly through iterative development and continuous user feedback, contributed significantly to system adaptability and reliability. The developed system enhances administrative efficiency, data consistency, transparency, and regulatory compliance in public sector educational administration.
Implementasi Scrum untuk Meningkatkan Adaptivitas Pengembangan Sistem Business Intelligence Pada Perusahaan Distributor Alat Kesehatan Nursanti Novi Arisa; Indrayanto Dwicaksono; Is Riosena Nur Soffa
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.v6i3.1018

Abstract

In the Industry 4.0 era, optimizing data utilization has become an important factor in enhancing organizational monitoring effectiveness and decision-making processes. PT Promedika Mitra Utama and PT Promedika Mitra Farma have digitalized various operational aspects, including employee activities, correspondence management, risk management, and weekly reporting. However, the generated data have not been optimally integrated to support managerial analysis. This study aims to design and implement a dashboard-based Business Intelligence (BI) system to improve monitoring effectiveness and managerial information accessibility. The development process includes performance metric identification, data collection and cleansing, data integration, and centralized data storage, with visualization implemented using Google Looker Studio. The Scrum method was applied to accommodate evolving variables and visualization requirements throughout iterative development and stakeholder feedback. The system development was completed in three sprints with a 100% backlog completion rate. Evaluation through sprint reviews and stakeholder validation demonstrated that the dashboard successfully accommodated changing requirements and supported a more systematic and integrated monitoring process. The resulting dashboard consists of three main reports, namely an integrated operational report and weekly reports for each company. The findings indicate the effectiveness of the Scrum approach in developing an adaptive BI system.
Deteksi Pemalsuan QRIS MPM Statis Menggunakan YOLO, PaddleOCR dan Metode Berbasis Aturan I Putu Gede Dharma Saputra; Made Windu Antara Kesiman; I Made Gede Sunarya
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1020

Abstract

Transforming the digital payment system through Quick Response Code Indonesian Standard (QRIS) Static Merchant Presented Mode (MPM) has provided convenience for MSMEs, yet also triggered significant security risks, particularly quishing attacks involving fraudulent sticker overlays. This research aims to develop a comprehensively integrated fraud detection system using the YOLOv11 Single-Stage Detector architecture and a rule-based inference engine. The research methodology includes the construction of a representative dataset of 898 images and the implementation of a three-layer validation mechanism comprising spatial layout analysis based on ASPI standards, textual semantic validation using PaddleOCR, and geospatial verification of GPS coordinates. The system utilizes the SequenceMatcher algorithm with adaptive thresholds to accommodate merchant typography variations. Experimental results indicate that the YOLOv11-m variant provides the best localization accuracy with a mean Average Precision (mAP) 50-95 score of 0.8682. End-to-end evaluation test images yielded an overall system accuracy of 96.15%. Significantly, the system achieved a recall of 1.00 for the suspicious class, proving its ability to identify all potential visual manipulation threats without omission. Although blurred images lowered the authentic class recall to 0.90, security principles remained intact by classifying unvalidated data as suspicious. This study provides a significant contribution to strengthening digital payment integrity through a precise, lightweight detection mechanism aligned with Indonesian national regulations.
Perbandingan Kinerja Naïve Bayes dengan dan Tanpa SMOTE untuk Klasifikasi Gangguan Kecemasan Mahasiswa pada Data Tidak Seimbang Nurhadi Surojudin; Sufajar Butsianto; Andri Firmansyah
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1021

Abstract

Anxiety disorders are one of the most common mental health problems experienced by university students and may affect learning concentration and academic performance. The analysis of psychological survey data using machine learning techniques can support early detection of student anxiety conditions. However, one of the main challenges in mental health data analysis is the presence of class imbalance within the dataset. This study aims to analyze the effect of applying the Synthetic Minority Oversampling Technique (SMOTE) on the performance of the Naïve Bayes algorithm for multi-class classification of student anxiety levels, which are categorized into three classes: No Stress, Eustress, and Distress. The dataset used in this research was obtained from student questionnaire data and underwent several preprocessing steps including data cleaning, feature transformation, and dataset splitting using the hold-out method with a ratio of 80% training data and 20% testing data. Model performance was evaluated using a confusion matrix with evaluation metrics including accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes model without SMOTE achieved an accuracy of 0.84, precision 0.78, recall 0.41, and F1-score 0.54. After applying SMOTE, the model achieved an accuracy of 0.82, precision 0.74, recall 0.69, and F1-score 0.71. These results indicate that SMOTE improves the model's ability to detect minority classes in multi-class classification problems, although a slight decrease in overall accuracy is observed.
Fenomena Hollow Shell Effect Pada Aplikasi Finansial: Evaluasi Paradoksal User Experience Kaspro di Kalangan Pengemudi Maxim Ilham Al Hafidz Muhyiddien; Ispandi Ispandi
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.v6i3.1024

Abstract

Digital transformation in the online transportation sector positions digital wallets not merely as optional payment tools, but as absolute work infrastructures for driver partners. This study aims to critically evaluate user satisfaction and experience with the KasPro application within the Maxim Driver Jabodetabek community. Given the high mobility and heterogeneity of the gig worker population, this research is positioned as an exploratory quantitative study. Data collection involved 99 respondents, determined using Slovin's formula with a 10% margin of error through purposive sampling techniques. Evaluation using the User Experience Questionnaire (UEQ) instrument revealed a sharp usability paradox. The Perspicuity scale achieved a highly satisfactory positive score (2.32), indicating an easily learnable interface. However, the other five crucial dimensions fell into the Bad category with extreme negative values: Attractiveness (-2.39), Efficiency (-2.42), Dependability (-2.50), Stimulation (-2.46), and Novelty (-2.35). Theoretically, this anomaly, where Perspicuity is inversely proportional to functional satisfaction, occurs due to the Hollow Shell Effect under conditions of forced adoption. For gig workers who have an absolute dependence on daily income liquidity, visual navigational ease instantly loses its significance when the system fails to meet basic utilities such as access speed and financial transaction security. This gap triggers passive resistance behavior, where users minimize operational interaction. The main contribution of this research is expanding human-computer interaction literature by validating the Hollow Shell Effect anomaly within the gig worker ecosystem, and proposing a design paradigm shift towards Reliability-First UX principles. Therefore, developers are recommended to prioritize backend infrastructure optimization through microservices architecture and e-KYC system automation, rather than simply carrying out visual aesthetic updates.
Identifikasi Indikasi Risiko Depresi pada Unggahan Media Sosial X Menggunakan Natural Language Processing dan Algoritma Random Forest Arif Siswandi; Arif Susilo
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1026

Abstract

Depression among university students has become an important mental health concern due to its potential impact on quality of life and academic performance. Social media platform X, as a text-based communication medium, provides a space for spontaneous expression that may reflect users’ emotional states. This study aims to analyze linguistic patterns associated with indicative depressive expressions in social media posts using a Natural Language Processing (NLP) approach and the Random Forest algorithm. Data were collected through web scraping between January and November 2024 using keywords conceptually derived from the Patient Health Questionnaire-9 (PHQ-9) indicators and adapted to linguistic expressions commonly used in social media communication. From an initial collection of 36,081 posts, several filtering stages were conducted, including duplicate removal, language filtering, and elimination of irrelevant content, resulting in a final dataset of 1,070 posts used in this study. The high filtering rate indicates that many scraped posts did not directly represent relevant emotional expressions. The dataset was manually labeled into three indicative categories of depressive expressions: mild, moderate, and severe. The analytical process included text preprocessing, TF-IDF feature extraction, and classification modeling using the Random Forest algorithm. The evaluation results show an accuracy of 97%. However, this value should be interpreted cautiously because model performance may be influenced by dataset characteristics and the manual labeling process. Therefore, the proposed model should be regarded as an exploratory approach for identifying linguistic patterns associated with emotional expressions in social media text rather than a clinical diagnostic tool for depression.
Integrasi Principal Component Analysis dan Logistic Regression untuk Analisis Sentimen Kepuasan Pelanggan Berdasarkan Ulasan Online Tsalsabila Jilhan Haura; Rini Sovia; Gunadi Widi Nurcahyo
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.v6i4.1029

Abstract

Customer reviews on digital platforms are an important source of information for evaluating service quality and customer satisfaction levels. However, the unstructured nature of review data and its high feature dimensionality pose challenges in the sentiment analysis process. This study aims to develop a customer sentiment analysis model by integrating Principal Component Analysis (PCA) and Logistic Regression. The data used are 679 Indonesian-language reviews obtained through web scraping techniques from Google Reviews at ten d'Besto EBM branches in Padang City. The research stages include text preprocessing, TF-IDF weighting, dimensionality reduction using PCA, and sentiment classification using Logistic Regression. The results show that PCA is able to reduce data complexity by producing two principal components that explain 85.7% of the total data variance. The Logistic Regression model built on the features resulting from PCA reduction achieved an accuracy of 82%, demonstrating the model's ability to effectively classify positive and negative sentiments. In addition to improving computational efficiency, the use of PCA also helps reduce feature redundancy in high-dimensional text data. The contribution of this research is to produce a simpler and more efficient sentiment analysis approach to process customer reviews and provide data-based information that can be used to support service quality evaluation and decision-making in the culinary industry.
Perancangan UI/UX Fitur Pariwisata pada Aplikasi Garut Hebat Super App Menggunakan Metode Prototipe Futri Gina Firnanda; Bayu Pamungkas
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.v6i4.1030

Abstract

The tourism feature on the Garut Hebat Super App currently has limitations in navigation structure, presentation of destination information, and a lack of interactive elements, making it difficult for users to obtain tourism information quickly and in a structured manner. This study aims to design the UI/UX of the tourism feature using the Prototype method to produce an interface design that is easier to use and suits user needs. The Prototype method is implemented through the stages of communication, quick plan, modeling quick design, construction of prototype, and delivery and feedback carried out iteratively based on user feedback. The results of the study are interactive prototypes (high-fidelity) that include destination video features, ticket price information, and integrated tourist route maps. Usability evaluation was conducted using the System Usability Scale method on 50 respondents Test results show an average score of 80.09, placing it in the "acceptable" category, with a good level of usability that is acceptable to users. This value indicates that the prototype has a fairly good level of usability and is acceptable to users, although there are still several interface aspects that need to be improved. This research contributes to the design of a prototype-based UI/UX for tourism features on an integrated public service platform (super app) that combines destination information, ticket prices, travel videos, and route navigation in a single interface. The research findings are expected to serve as a reference for developers of regional public service applications in improving the quality of digital tourism services that are oriented towards user needs.

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