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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 56 Documents
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Search results for , issue "Vol. 5 No. 4 (2025): June 2025" : 56 Documents clear
Analisis Komparasi Kinerja LSTM dan CNN dalam Deteksi Spam Email Berbasis Deep learning Maugy Al Kautsar; Galet Guntoro Setiaji; Ahmad Rifa'i
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.v5i4.572

Abstract

Spam email remains a critical issue in digital communication due to its potential misuse in spreading false information and online fraud. This study aims to evaluate and compare the performance of two deep learning models Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for text-based spam email classification. The dataset used in this study was obtained from Kaggle and contains 5,572 labeled email entries categorized as spam and non-spam. The preprocessing stage included labeling, cleaning, lowercasing (casefolding), tokenization, stopword removal, and stemming. The data was split into training and testing sets with a 70:30 ratio. Both models were trained using the same configuration and evaluated using accuracy, loss, confusion matrix, and F1-score metrics. The results indicate that the LSTM model achieved the highest accuracy of 98.72% with a loss value of 0.0377, outperforming the CNN model, which achieved 87.78% accuracy and a loss of 0.3659. Based on these findings, LSTM demonstrated superior performance in detecting spam emails using text-based input. This research is expected to serve as a reference for developing more accurate and effective spam detection systems in the future.
Sentiment Analysis of Online Lending Services Using Support Vector Machine and Logistic Regression Rahayu, Ardita Isnanda; Kacung, Slamet
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.v5i4.574

Abstract

This research examines public sentiment toward online lending services in Indonesia by analyzing opinions from social media platforms, specifically YouTube and Twitter, collected from January 2021 to January 2024. The objective of this study is to develop an accurate sentiment classification system that can effectively categorize public opinions into positive, negative, and neutral sentiments, thereby providing valuable insights for regulatory bodies and service providers to understand consumer concerns and improve service quality. The collected data underwent thorough preprocessing, semi-automatic labeling, and Term Frequency-Inverse Document Frequency (TF-IDF) weighting. Four classification models were evaluated: Support Vector Machine (SVM) with Linear, Polynomial, and Radial Basis Function (RBF) kernels, and Logistic Regression. Results demonstrate that Linear SVM achieves the best performance with an accuracy of 90.17% and an F1-score of 0.902, effectively categorizing sentiments across all classes while excelling particularly in negative and neutral categories. The expected impact of this analysis is to provide evidence-based recommendations for policymakers in financial technology regulation and help online lending service providers understand consumer satisfaction levels to improve their service delivery. This study offers valuable insights for service providers and regulatory bodies seeking to better understand and address public concerns in this domain.
Klasifikasi Teks Komentar Pengguna Aplikasi Access By Kai di Google Play Store Menggunakan Metode Naïve Bayes Fahrul Afandi; Kurniawan, Rakhmat
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.v5i4.575

Abstract

The advancement of information technology has influenced various aspects of life, including transportation. The Access by KAI application provides digital train ticket booking services. With millions of users, analyzing the level of satisfaction through reviews on the Google Play Store is important to improve service quality. This study aims to classify user reviews using the Naïve Bayes algorithm to determine the level of satisfaction, group reviews based on certain categories, and evaluate the accuracy of the classification results. The study uses the Naive Bayes method to classify text where review data collection is carried out first through a scraping process from the Google Play Store with a total of 1000 reviews. Data is analyzed through pre-processing stages such as cleaning, case folding, tokenization, normalization, stopwords, steamming and sentiment labeling using InSetLexicon. Furthermore, reviews are grouped by category using the K-Means clustering method to group data into three categories, namely Features, Services, and Systems, to improve classification accuracy followed by classification using Naïve Bayes. Evaluation is carried out using a confusion matrix to measure accuracy, precision, recall, and F1-score. The classification results show that in the Feature category, precision is 79%, recall 99%, and F1-score 88%. In the Service category, precision reaches 100%, recall 56%, and F1-score 72%. For the System category, precision is 94%, recall 68%, and F1-score 79%. Overall, the model achieves an accuracy of 83%. The benefits of this study are to provide a deep understanding of user needs and become a reference for developers to improve the Access by KAI application service.
Klasifikasi Kondisi Janin Berdasarkan Data Kardiotogram Menggunakan Algoritma Naive Bayes Syah Utama, Isruel; Haerani, Elin; Wulandari, Fitri; Ramadhani, Siti
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.v5i4.584

Abstract

Fetal health during pregnancy is a crucial aspect that can be monitored through cardiotocography (CTG) data; however, manual interpretation of this data often encounters challenges due to class imbalance. This study aims to develop a fetal condition classification model using the Naive Bayes algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE) to address the disparity in class distribution. The CTG dataset, obtained from Kaggle, consists of 2,126 records categorized into three target classes: Normal, Suspect, and Pathological. Data processing followed the Knowledge Discovery in Databases (KDD) framework, including data selection, cleaning, normalization, splitting into four ratios (70:30, 80:20, 85:15, and 90:10), SMOTE application, and model evaluation using accuracy and F1-Macro metrics. The results showed that the 80:20 ratio yielded the highest accuracy at 79.81%, while the 90:10 ratio produced the highest F1-Macro score of 0.6788. These findings indicate that although accuracy remained relatively stable, the F1-Macro metric provided a better representation of performance across all classes, especially minority ones. The application of SMOTE proved effective in balancing class distribution and enhancing model sensitivity. This study serves as a foundational step in developing a more reliable and adaptive fetal condition classification system and highlights opportunities for further exploration of alternative algorithms and SMOTE parameter optimization.
Klasifikasi Kondisi Janin Menggunakan Algoritma K-Nearest Neighbors dan Teknik SMOTE Berdasarkan Data Kardiotogram Dede Fadillah; Haerani, Elin; Wulandari, Fitri; Syafria, Fadhilah
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.v5i4.585

Abstract

Fetal health is a crucial aspect in reducing infant mortality rates, where cardiotocography (CTG) is used to monitor fetal condition through recordings of fetal heart rate and uterine contractions. However, manual interpretation of CTG data still faces challenges, particularly due to imbalanced class distribution. This study aims to develop a classification model for fetal conditions using the K-Nearest Neighbors (K-NN) algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset used, sourced from Kaggle, consists of 2,126 CTG examinations categorized into three classes: Normal, Suspect, and Pathological. The data processing follows the Knowledge Discovery in Databases (KDD) process, including data selection, cleaning, normalization, splitting, balancing with SMOTE, and classification using K-NN. The model was evaluated using four training-testing split ratios (70:30, 80:20, 85:15, and 90:10) with accuracy and macro F1-score as metrics. The results indicate that the 85:15 split ratio achieved the highest accuracy of 89.7%, while the 90:10 ratio yielded the highest macro F1-score of 0.83. These findings suggest that the 85:15 ratio offers an optimal balance between model training and evaluation, whereas the highest F1-score at 90:10 reflects greater model sensitivity to minority classes. The combination of K-NN and SMOTE proved effective in addressing data imbalance and supports model stability in the overall classification process of fetal conditions.
Analisis Perbandingan Algoritma SVM dan CNN dalam Mendeteksi Website Judi Online Berdasarkan Konten Teks Simanjuntak, Nurcahaya; Muhammad, Alva Hendi
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.v5i4.586

Abstract

This study aims to compare the effectiveness of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms in detecting Indonesian-language online gambling websites. With the increasing number of online gambling players in Indonesia, it is essential to develop effective methods for identifying gambling content. The dataset used consists of 34,336 gambling websites and 36,529 non-gambling websites, collected through web scraping. The SVM model demonstrated an accuracy of 99%, with evaluation metrics including a precision of 1.00, recall of 0.99, and F1-score of 0.99. In contrast, the CNN model achieved perfect accuracy of 100%, with precision, recall, and F1-score all at 1.00. However, it is important to note that this perfect accuracy was achieved under certain conditions, including a relatively clean dataset and optimal training processes. Evaluation results using cross-validation techniques indicated that SVM maintained a consistent accuracy of approximately 99%, while CNN exhibited an average accuracy of 99.61% with a very low standard deviation. This research emphasizes the importance of data pre-processing in enhancing model accuracy and highlights the advantages of CNN in capturing complex patterns within text. These findings contribute significantly to the development of detection methods for online gambling websites in Indonesia and open avenues for further research in this field.
Penggunaan Metode Design Thinking Untuk Perancangan Ulang UI/UX Pada Aplikasi BCA Mobile Saidatuzzahra, Putri; Akhmad Rizal Dzikrillah
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.v5i4.589

Abstract

Effective user interface (UI) and user experience (UX) design are essential in improving the convenience and efficiency of using digital applications. This study aims to redesign the UI/UX of the BCA Mobile application by applying the Design Thinking method to create a design that is more relevant to user needs. The research method begins with a literature study and data collection through the User Experience Questionnaire (UEQ) questionnaire filled out by 10 respondents to identify user problems and expectations. Furthermore, five stages of Design Thinking are applied, namely Empathize, Define, Ideate, Prototype, and Testing. The design prototype was retested with respondents using the UEQ questionnaire to evaluate aspects of attractiveness, efficiency, clarity, dependability, stimulation, and novelty. The results showed a significant increase in UEQ scores on all scales, with an average increase of 3.55 points from a range of ?3 to +3. This shows that the Design Thinking approach is able to produce a UI/UX design that is more responsive and in accordance with user expectations, as well as improving the quality of user interaction with the application. This research is expected to be a reference in the development of UI/UX for other digital banking applications.
Penerapan Model Waterfall dalam Proyek Sistem Informasi Penjualan Komoditas Agribisnis Berbasis Web Jumlat, Kamal; Trisswandi, Sendi; Haryani, Risa; Anggraeni Putri, Sukmawati; Setia Budi, Eko
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.v5i4.590

Abstract

PT. Cahaya Gunung Permai is a national company operating in the agribusiness sector including planting, breeding, management and marketing. This project aims to develop a web-based agribusiness commodity sales information system for PT. Cahaya Gunung Permai (taniku.co.id), with a focus on increasing efficiency and accessibility in the sales and marketing process of agribusiness products. This system is designed to provide integrated solutions that enable companies to reach a wider market, reduce dependence on intermediaries, and increase profit margins. By utilizing web technology, this system provides a user-friendly platform for users, including inventory management, order processing and real-time reporting features. The project also places great emphasis on data security and user privacy to ensure the protection of sensitive information. Through comprehensive training and technical support, companies are expected to be able to optimize the use of this system. As a result, PT. Cahaya Gunung Permai can increase its competitiveness in the digital-based agribusiness market, with better operational efficiency and access to more in-depth information for strategic decision making. This project is an important step in the company's digital transformation and has the potential to have a significant positive impact on the entire agribusiness value chain.
Penerapan Jaringan Hotspot Berbasis Mikrotik Menggunakan Metode PPDIO (Prepare, Plan, Design, Implement, Operate, Optimize) Al Muzaki, Dimas Fauzi; Tino Feri Efendi; Muqorobin
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.v5i4.592

Abstract

Toko Emas Purnama faces challenges in building an efficient, secure, and operationally appropriate internet network infrastructure, particularly to support digital systems such as cloud-based transactions, internal communication, IP Cameras, and hotspot services for customers. This research discusses the implementation of a Mikrotik-based hotspot network at the Purnama Gold Shop area using the PPDIOO (Prepare, Plan, Design, Implement, Operate, and Optimize) method. The objective is to provide a stable, secure, and efficient internet network to support both operational activities and customer needs within the store environment. The PPDIOO methodology is employed as a systematic approach to network planning and deployment, covering the stages from requirement preparation to system performance optimization. The results show that the Mikrotik-based hotspot network offers reliable connectivity with improved bandwidth management and security. Through the PPDIOO method, the network system is developed in a structured and efficient manner, enhancing digital service quality at the Purnama Gold Shop.
Perancangan Ulang UI/UX Dengan Penerapan Metode Design Thinking Pada Aplikasi M-Paspor Khoirunnisa; Hanif, Isa Faqihuddin
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.v5i4.596

Abstract

M-Paspor is a digital application developed by the Directorate General of Immigration to facilitate the online passport application process. Based on initial evaluation results using the System Usability Scale (SUS) method, the application scored 28,5, which is still below the usability standard of 68. Therefore, the objective of this study is to redesign the user interface and user experience (UI/UX) using the Design Thinking approach, which includes the stages of empathize, define, ideate, prototype, and test. After the redesign, the SUS scored increased to 81,4, falling into the “good” category, indicating that the user experience has improved. The results demonstrate that Design Thinking techniques can comprehensively identify user needs and generate more efficient, user-friendly design solutions that meet user expectations for public service applications.

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