cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282370070808
Journal Mail Official
jurnal.bulletincsr@gmail.com
Editorial Address
Jalan sisingamangaraja No 338 Medan, Indonesia
Location
Kota medan,
Sumatera utara
INDONESIA
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
 43   44   45   46   47 
Analisis Penerimaan Sistem E-Commerce Smartschool Menggunakan Model Unified Theory of Acceptance and Use of Technology Akram Farrasanto; Muhammad Najamuddin Dwi Miharja; Nanang Tedi Kurniadi
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.v6i4.1151

Abstract

The conventional school uniform purchasing process still faces various obstacles, such as limited service time, inefficient transaction recording, and minimal real-time transaction documentation. Therefore, to overcome these problems, SMK Bina Patriot implemented the SmartSchool e-commerce system as a transaction medium for purchasing school uniforms. This study aims to analyze user acceptance of the SmartSchool e-commerce system using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT model is used to test the effect of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions on Behavioral Intention in using the system. This study uses a quantitative approach by using data collection techniques through distributing questionnaires to 55 students as respondents in the study. Then the resulting data will be analyzed using multiple linear regression to determine the effect of each variable on the intention to use the system. The results of this study also show that Performance Expectancy has a significant effect on Behavioral Intention with a significance value of 0.037 (p < 0.05), while Social Influence also has a significant effect with a significance value of 0.002 (p < 0.05). Conversely, Effort Expectancy did not significantly influence Behavioral Intention, with a significance value of 0.667 (p > 0.05). Therefore, it can be seen that the variable with the most dominant influence is Social Influence, with a regression coefficient of 0.4882. This study contributes to extending the application of the UTAUT model within the context of school e-commerce systems at the secondary education level, which remains relatively underexplored in existing research. Furthermore, the findings of this study can serve as a reference for schools in designing implementation strategies and developing e-commerce systems that better align with users' needs and expectations
Evaluasi YOLOv8 untuk Deteksi Kendaraan pada Simulasi Citra Palang Parkir Syalomita Pasha Sante; Claudia Anastasia Danel; Valentino Rexy Artha Sumeru; Olga Engelien Melo; Anthon Arie Kimbal
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.v6i4.1152

Abstract

This study evaluates the use of YOLOv8 for vehicle detection in an image-based parking barrier simulation. The study was conducted because vehicles near a parking entrance are not always captured under ideal visual conditions. Some vehicles may appear far from the barrier point, partially occluded, captured under low-light conditions, or appear together with other vehicles in a single frame. The data were collected from two sources, namely vehicle photos taken using a mobile phone camera and vehicle images obtained from open internet sources. All images were grouped into five testing scenarios: vehicles in front of the barrier area, vehicles far from the barrier point, partially occluded vehicles, low-light conditions, and crowded areas. The testing process was carried out using the YOLOv8n model with a confidence threshold of 0.5. From a total of 131 test images, the model successfully detected vehicles in 103 images, failed to detect vehicles in 28 images, and produced 0 false detections. The average detection accuracy was 77,6%. The best result was obtained in the crowded area scenario, while the lowest result occurred in the low-light condition scenario. These findings show that YOLOv8n can be used as an initial evaluation for vehicle detection in a parking barrier simulation, although further testing with a live camera and physical devices is still needed. This study contributes to the preliminary evaluation of YOLOv8n for vehicle detection in parking barrier image simulation. The main contribution lies in examining the model’s ability to recognize vehicles under several visual conditions, including vehicles in front of the barrier area, vehicles far from the barrier, partially occluded vehicles, low-light conditions, and crowded areas. This study is not intended to represent a complete implementation of an automatic parking barrier system. Instead, it serves as an image-based preliminary evaluation to identify the potential and limitations of YOLOv8n before further development using live cameras and physical parking barrier devices.
Analisis Limitasi Performa Penilaian Esai Otomatis pada Aplikasi ESAO Berdasarkan Metrik BLEU dan ROUGE Akhmam Fahmi; Nuraini Nuraini; Maulana Fakih Latief
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.v6i4.1154

Abstract

The development of GenAI has encouraged the use of automated essay scoring technology through various platforms, one of which is the ESAO (Essay Analytic Online) application. Although this LLM-based system is capable of automatically generating assessment feedback narratives, standardizing evaluation methods to measure the reliability of these texts still faces significant challenges. This study aims to test the suitability of the Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics as instruments to measure the extratextual performance of the ESAO application. The research method was carried out by comparing feedback texts from ESAO with authentic lecturer assessment drafts on three different characteristics of the exam material: dataset condition analysis, descriptive statistics, and correlation and regression. The test results showed an average value of the BLEU metric of 0.0522 and ROUGE of 0.1255. This study revealed that low scores do not represent a functional failure of the ESAO application, but rather indicate fundamental limitations and shortcomings in using rigid lexical metrics (word-based metrics) in assessing dynamic generative texts. The BLEU and ROUGE metrics rely heavily on rigid n-gram overlap, thus failing to capture the semantic similarity, academic reasoning context, and linguistic variation generated by ESAO. This study concludes that traditional evaluation metrics such as BLEU and ROUGE are inaccurate and incompatible as a single benchmark for Generative AI performance in the context of educational assessment, necessitating a transition to semantic-based metrics in the future.
Deteksi Penipuan pada Transaksi Keuangan Digital Menggunakan Ensemble Learning: Studi Komparatif Random Forest, Gradient Boosting, dan XGBoost Nurul Akbar Tanjung; Sugeng Hary Purnomo; Sanwani Sanwani
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.v6i4.1155

Abstract

Digital payment fraud in Indonesia has grown alongside the dramatic expansion of mobile money services, creating a detection problem that conventional rule-based systems are increasingly ill-equipped to handle. This paper examines whether a soft-voting ensemble of Random Forest, Gradient Boosting, and XGBoost can offer a more effective solution. The model was trained on the PaySim synthetic dataset, consisting of 6.36 million mobile money transactions in which fraudulent cases account for just 0.129 percent of all records. SMOTE was used exclusively on the training data to address the extreme class imbalance before model fitting. Five-fold cross-validated Grid Search determined the hyperparameter configuration for each constituent model. On the held-out test set, the ensemble achieved 94.7 percent precision, 91.3 percent recall, 93.0 percent F1-score, and 0.987 AUC-ROC figures that consistently exceeded those of any single algorithm. Examining feature contributions revealed that the sender balance difference and transaction amount carried the most discriminative weight, a finding that aligns with known fraud behavior in mobile payment datasets. A local streaming latency test across 5,000 consecutive transactions produced an average response time of 147.3 ms, with the 99th percentile remaining below the 200 ms operational threshold. Taken together, the results indicate that the ensemble approach is not only statistically superior but also practically deployable within the real-time constraints of digital banking environments.
Optimasi Klasifikasi Hate Speech dan Offensive Language melalui Frozen RoBERTa Feature Extraction dan Random Forest Marsha Cahyani Dwisyakilla; Surya Agustian; Novriyanto Novriyanto; Muhammad Affandes
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.v6i4.1157

Abstract

Hate speech and offensive content detection on social media remains a significant challenge in Natural Language Processing (NLP) due to the characteristics of Twitter data, which are typically short, informal, and contain various elements such as mentions, URLs, hashtags, and emotional expressions that complicate the classification process. End-to-end Transformer fine-tuning approaches generally require substantial computational resources; therefore, this study explores a more computationally efficient approach by utilizing RoBERTa as a frozen feature extractor combined with Random Forest as the classifier. This approach enables the exploitation of contextual representations generated by Transformer models without requiring full model retraining.The study employs the HASOC 2021 English Track dataset, which consists of two classification tasks: Task A for binary classification (HOF and NOT) and Task B for multi-class classification (HATE, OFFN, PRFN, and NONE). The classification pipeline is optimized through the incorporation of handcrafted features, oversampling, Random Forest hyperparameter tuning, and threshold tuning in specific scenarios. Model performance is evaluated using accuracy, precision, recall, and F1-macro, with F1-macro serving as the primary metric due to class imbalance. The best-performing model achieved an F1-macro score of 0.80 on Task A and 0.64 on Task B. These results indicate that the combination of frozen RoBERTa representations and Random Forest provides strong performance for binary hate speech and offensive content classification. However, the performance on Task B highlights the difficulty of distinguishing linguistically similar categories, such as HATE, OFFN, and PRFN, suggesting that fine-grained multi-class classification remains a challenging task. Overall, the findings indicate that RoBERTa-based frozen feature extraction constitutes a computationally efficient alternative for hate speech detection on English Twitter data, although further improvements are required to enhance performance in multi-class classification settings.
Implementasi dan Evaluasi Performa Algoritma Naïve Bayes dalam Deteksi Dini Penyakit Diabetes Nurhasanah Nurhasanah; Nilovar Asyiah; Rahmawati Rahmawati
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.v6i4.1159

Abstract

Diabetes mellitus is one of the most prevalent chronic diseases worldwide and requires early detection to reduce the risk of severe complications through timely intervention. This study aims to implement and evaluate the performance of the Naïve Bayes algorithm in supporting the early detection of diabetes based on patients' health data. The study employed the Pima Indians Diabetes Dataset, consisting of 768 patient records with eight input attributes and one output attribute. During the preprocessing stage, zero values in physiological attributes were treated as missing values and replaced using the median of each respective attribute, followed by data consistency checking and dataset partitioning using the 80:20 split validation method. Model performance was evaluated using a confusion matrix with four performance metrics: accuracy, precision, recall, and F1-score. The experimental results showed that the Naïve Bayes algorithm achieved an accuracy of 88.31%, precision of 87.80%, recall of 90.00%, and an F1-score of 88.89%. These findings indicate that the proposed model performs well in classifying diabetes risk. The implementation of the model in a web-based application is expected to assist healthcare professionals and the general public as an early screening tool to support preliminary decision-making before comprehensive medical examination.
Pemodelan Topik pada Komentar Media Sosial X menggunakan Latent Dirichlet Allocation Ardelia Adzra; Safitri Juanita
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.v6i4.1161

Abstract

Sexual harassment is a social issue widely discussed on the social media platform X. However, the high volume of unstructured comments makes it difficult to manually identify the main topics of discussion. This study aims to identify the main topics in comments related to sexual harassment on X using the Latent Dirichlet Allocation (LDA) method. The data used consist of comments on the topic of sexual harassment collected from X during the 2024–2026 period. The research stages include data collection, data preparation, dictionary and corpus construction, LDA modeling with hyperparameter tuning, evaluation using coherence score, and topic interpretation based on dominant keywords and representative data. The results show that the best LDA model consists of four topics with a coherence score of 0.517. These four topics are interpreted as Handling Cases of Sexual Harassment in Educational Environments, Victims’ Experiences and Psychological Impacts, Cases of Sexual Harassment in Higher Education, and Protection Related to Sexual Harassment. These findings indicate that the LDA method is capable of identifying the main topics in sexual harassment comments and helping to organize unstructured social media data into information that is easier to understand. The contribution of this study is the proposed Latent Dirichlet Allocation (LDA)-based topic modeling approach with hyperparameter tuning to identify and organize unstructured sexual harassment comments on the social media platform X into coherent and interpretable topic clusters. The resulting topic mapping provides valuable insights into the issues that receive the greatest public attention and can serve as a foundation for understanding public concerns. Furthermore, these findings have the potential to support the development of victim support services, including telemedicine-based systems.
Prediksi Konsentrasi CO(GT) Menggunakan Long Short-Term Memory pada Data Sensor Kualitas Udara IoT Asep Arwan Sulaeman; Candra Naya; Ahmad Turmudi Zy; Riyadi Riyadi
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.v6i4.1162

Abstract

Air quality deterioration has become a major challenge for public health and environmental management in urban areas. Internet of Things (IoT)-based monitoring systems continuously generate sensor data that can be exploited for air quality prediction; however, these datasets commonly contain missing values, noise, and temporal dependencies that may reduce prediction accuracy. This study proposes a Long Short-Term Memory (LSTM)-based model to predict carbon monoxide (CO(GT)) concentrations using the Air Quality UCI dataset, which consists of 9,357 observations and 15 attributes. During preprocessing, -200 values were identified as missing-value indicators, followed by invalid-data handling, Min-Max normalization, and sequence generation using a sliding-window approach with a window size of four. The processed data were divided into training and testing sets using an 80:20 ratio. The prediction model employs a single LSTM layer with 50 hidden units and a Dense output layer and is trained using the Adam optimizer for 50 epochs. Experimental results achieved a Mean Absolute Error (MAE) of 0.0389 and a Root Mean Squared Error (RMSE) of 0.0567, indicating that the proposed model effectively captures temporal patterns in air quality observations with relatively low prediction errors. These findings are consistent with previous studies reporting the effectiveness of LSTM for air quality forecasting and demonstrate its potential to support continuous IoT-based environmental monitoring systems. Future work may incorporate hyperparameter optimization and comparative evaluations with alternative deep learning architectures to further improve predictive performance.
Evaluasi Efektifitas Optimizer Adam dan SGD pada Klasifikasi Citra Dermoskopi dengan MobileNetV4 Ahmad Naufal; Nur Rachmat
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.v6i4.1165

Abstract

Skin disease is one of the most common health problems and requires fast and accurate diagnosis. The limited availability of dermatology specialists and the high subjectivity of conventional diagnosis have driven the development of artificial intelligence-based automatic classification systems. This study aims to compare the performance of the Adam and Stochastic Gradient Descent (SGD) optimizers on the MobileNetV4 architecture for classifying eight classes of skin diseases using the ISIC 2019 dataset. The dataset consists of 23,257 valid dermoscopic images after preprocessing, which includes duplicate image removal, hair artifact elimination using the blackhat morphology method, and an asymmetric sampling strategy in which majority classes were capped at a maximum of 2,000 images while minority classes were augmented to reach the target count, in order to address extreme class imbalance with a ratio of up to 53:1. The model was trained using a three-phase training strategy with gradual unfreezing of the MobileNetV4 backbone initialized with pretrained ImageNet weights. All training configurations were made identical for both optimizers except for the optimization algorithm and learning rate, ensuring a fair comparison. Evaluation results on the test set show that the Adam optimizer achieved an accuracy of 71.07% with a macro F1-score of 0.72, while SGD achieved an accuracy of 58.06% with a macro F1-score of 0.57. Adam outperformed SGD across all eight skin disease classes. The performance difference of 13.01% indicates that Adam's adaptive learning rate mechanism is more effective for dermoscopic datasets with imbalanced class distributions compared to SGD. Nevertheless, it should be noted that Adam requires greater computational memory than SGD due to the storage of first and second moment estimates per parameter, and therefore the computational efficiency trade-off should be considered when deploying the model on resource-constrained devices. This study provides empirical contribution in selecting the optimal optimizer for skin lesion classification based on lightweight architectures.
Sistem Informasi Monitoring Komoditas Sayur untuk Penentuan Harga Wajar Menggunakan Metode Standar Deviasi Muh Zia Ulkhaq; Rifki Figianto; Luthfi Nur Azizah
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.v6i4.1167

Abstract

The dynamic fluctuation of vegetable commodity prices often creates uncertainty in determining fair prices in traditional markets. This condition leads to information asymmetry between traders, buyers, and market managers, requiring a more objective approach to define fair price boundaries. This study aims to develop a statistical-based price monitoring model to identify a more measurable fair price range for vegetable commodities. The method used in this study is standard deviation as a statistical tool to measure the level of price dispersion based on historical price data. The mean and standard deviation values are used to construct the lower and upper bounds of the fair price range using the mean ± 1? approach. The data used in this study were obtained from the red chili commodity at the Banjarnegara Main Market as a case study.The results show that the standard deviation method is able to objectively represent price variability and classify market conditions into three categories, namely low price, normal price, and high price, based on the position of prices relative to the statistical range. In the case study of April 2026, the mean price was Rp63,300/kg and the standard deviation was Rp6,830/kg, resulting in a fair price range between Rp56,470/kg and Rp70,130/kg. This study concludes that the standard deviation approach is effective in identifying fair price boundaries based on historical data. The main contribution of this research is the development of a more objective price analysis model to support price transparency in traditional markets.

Page 45 of 47 | Total Record : 462

 43   44   45   46   47 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 5 (2026): August 2026 Vol. 6 No. 4 (2026): June 2026 Vol. 6 No. 3 (2026): April 2026 Vol. 6 No. 2 (2026): February 2026 Vol. 6 No. 1 (2025): December 2025 Vol. 5 No. 6 (2025): October 2025 Vol. 5 No. 5 (2025): August 2025 Vol. 5 No. 4 (2025): June 2025 Vol. 5 No. 3 (2025): April 2025 Vol. 5 No. 2 (2025): February 2025 Vol. 5 No. 1 (2024): December 2024 Vol. 4 No. 6 (2024): Oktober 2024 Vol. 4 No. 5 (2024): August 2024 Vol. 4 No. 4 (2024): June 2024 Vol. 4 No. 3 (2024): April 2024 Vol. 4 No. 2 (2024): Februari 2024 Vol. 4 No. 1 (2023): December 2023 Vol. 3 No. 6 (2023): October 2023 Vol. 3 No. 5 (2023): Agustus 2023 Vol. 3 No. 4 (2023): June 2023 Vol. 3 No. 3 (2023): April 2023 Vol. 3 No. 2 (2023): Februari 2023 Vol. 3 No. 1 (2022): Desember 2022 Vol. 2 No. 3 (2022): Agustus 2022 Vol. 2 No. 2 (2022): April 2022 Vol. 2 No. 1 (2021): Desember 2021 Vol. 1 No. 3 (2021): Agustus 2021 Vol. 1 No. 2 (2021): April 2021 Vol. 1 No. 1 (2020): Desember 2020 More Issue