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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
Rancang Bangun Sistem Informasi Penjualan Kura-Kura Berbasis Website Menggunakan Metode Prototype Wijaya Kusuma, Renaldi; Sinduningrum, Estu
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.v5i4.604

Abstract

Advances in information technology, especially the internet, have had a major impact on various fields, including the business world. The ease of access to technology encourages business people to utilize digital platforms, such as e-commerce, in operational activities. Turtle Cileungsi Store, a turtle sales business in the Bogor area, that the store still uses a manual system, namely the buyer must come to the Turtle Cileungsi store to search, select and purchase the desired turtle, sometimes the shop owner must first see the availability of turtle stock that the buyer will look for, thus making the buyer have to wait even longer. From these problems, a Kura-Kura sales system application is needed that can help deal with existing problems, such as preventing the loss of sales data, reducing errors in transactions, and facilitating direct sales access. To answer these problems, a web-based sales information system was designed and developed using the prototype method. This system was built using the PHP programming language and MySQL database. The implementation results show that the system is able to speed up the sales recording process, simplify report generation, and minimize data input errors. In addition, the system supports decision-making by providing more accurate and real-time information. With the implementation of this system, the management of store operations has become more efficient and effective, and has helped improve service quality and business productivity. Testing using the black-box testing method shows that all the main functions of the system run as expected. The test results from black-box testing show that there is a 20% increase in system efficiency, which indicates that system improvement and development is effective. With this test, the system can be more optimal, stable, and safe to use. Meanwhile, the test results with the System usability scale (SUS) obtained an average score of 80, which is included in the good category. Thus, it is expected that this system will be able to support the sales process more effectively, efficiently, and structured.
Analisis Perbandingan Metode DBSCAN dan Meanshift dalam Klasterisasi Data Gempa Bumi di Indonesia MHD Ade Setiawan; Fitri Insani; Yelfi Vitriani; Yusra
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.v5i4.605

Abstract

Indonesia is one of the countries with a high vulnerability to earthquakes due to its location at the convergence of three major tectonic plates: the Indo-Australian, Eurasian, and Pacific plates. As a result of this interaction, seismic activity is highly frequent across various regions. Understanding the distribution patterns of earthquakes is essential for disaster risk mitigation. One approach used to analyze these patterns is clustering, particularly using the DBSCAN  and Meanshift algorithms, which can group spatial data without predefining the number of clusters. This study aims to compare the effectiveness of both algorithms in clustering earthquake data based on spatial parameters, namely latitude and longitude. Evaluation was conducted using cluster visualization and the Silhouette Score as the clustering validity metric. The results show that DBSCAN  produces more optimal clustering with a Silhouette Score of 0.930028, higher than Meanshift's score of 0.90103. DBSCAN  is also capable of detecting relevant outliers in earthquake analysis, while Meanshift generates more clusters but with less separation. Using spatial parameters such as latitude and longitude, DBSCAN  is considered more effective in identifying the spatial distribution patterns of seismic activity in Indonesia based on earthquake data. This research supports the development of decision support systems for earthquake disaster mitigation and serves as a reference for selecting appropriate clustering methods for spatial data analysis.
Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means Wenny Tarisa Oktaviany; Fitri Insani; Alwis Nazir; Pizaini
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.v5i4.608

Abstract

Floods are one of the natural disasters that often occur in Indonesia, especially during the rainy season. This disaster is caused by various factors, both natural and caused by human activities, such as high rainfall, poor drainage systems, land conversion, and suboptimal spatial planning. The impact of floods is very detrimental, both physically and psychologically, including loss of life and damage to property. Therefore, a method is needed to group areas based on their level of vulnerability to flooding. This study aims to group flood disaster areas in Indonesia using the K-Means algorithm. The data used comes from the BNPB Geoportal covering flood events from January 2020 to December 2024, with a total of 7,487 events from 498 areas. Based on the test results obtained using the Silhouette Coefficient, it shows that 2 clusters were selected as the best number of clusters with a Silhouette Coefficient value of 0.8461 which is included in the strong clustering structure. Of the 2 clusters obtained, cluster 1 is a high-risk category consisting of 35 areas, while cluster 2 is a low-risk category consisting of 463 areas. The results of this study can provide information for related parties to improve the efficiency of flood disaster management.
Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine Alwaliyanto; Siska Kurnia Gusti; Iis Afrianty; Fadhilah Syafria
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.v5i4.612

Abstract

Stroke is one of the leading causes of death and disability worldwide, making it essential to develop classification models that can assist in early and accurate diagnosis. This study aims to implement the Support Vector Machine (SVM) algorithm with three types of kernels linear, polynomial, and Radial Basis Function (RBF) to classify stroke disease data. The Adaptive Synthetic Sampling (ADASYN) method is employed to address the class imbalance problem, while model training and evaluation are carried out using 5-Fold Cross-Validation to ensure stable and reliable results. The findings indicate that ADASYN successfully improves the model’s sensitivity to stroke cases (the minority class), as reflected by an increase in recall and F1-score, despite a slight decrease in overall accuracy a common trade-off in handling imbalanced data. The linear kernel (after ADASYN) achieved the best performance after imbalance handling, with an average AUC-ROC of 0.8333, recall of 0.7827, and F1-score of 0.2181 for the stroke class. Although the F1-score remains relatively low, it improved compared to the pre-ADASYN results, indicating better detection of stroke cases. The implementation was conducted using Google Colab, which also contributed to efficient data processing and visualization. Overall, the results demonstrate that the combination of SVM and ADASYN is effective in enhancing the model’s sensitivity to minority classes and is well-suited for medical data classification tasks, particularly in the early diagnosis of stroke using machine learning approaches.
Klasifikasi Sentimen Pada Dataset yang Terbatas Menggunakan Algoritma Convolutional Neural Network Saputra, M Ridho; Surya Agustian; Jasril; Novriyanto
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.v5i4.613

Abstract

This study aims to analyze public responses to the appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) using a sentiment classification approach based on the Convolutional Neural Network (CNN) algorithm. The primary dataset consists of 300 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. The limited size of the training data presents a major challenge, as it can hinder the model's ability to generalize. To address this issue, data augmentation was carried out by incorporating external datasets with Covid-19 and Open Topic themes. The preprocessing stages include text cleaning, normalization, and tokenization. The developed CNN model utilizes a layered architecture and applies regularization techniques such as L2 and dropout to reduce the risk of overfitting. Accuracy, F1-score, precision, and recall were used as performance evaluation metrics. Experimental results show that the best performance was achieved when the Kaesang and Covid-19 datasets were combined, yielding an F1-score of 0.62 on the validation set and 0.51 on the test set. These findings indicate that adding external data can improve classification accuracy, even under limited data conditions. This study contributes to the development of deep learning-based sentiment classification methods for Indonesian-language texts.
Perancangan Ulang Desain UI/UX pada Aplikasi ibisPaint X dengan Penerapan Metode The Wheel Novianti, Eka; Kamayani, Mia
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.v5i4.614

Abstract

Rapid technological developments have made smartphones not only function as communication devices, but can also be used as a place to draw digitally. Mobile-based graphic design applications, namely ibisPaint X. found problems experienced by users, such as difficulty in using the application, features that are difficult to find, and confusing displays. This study uses the wheel method to overcome problems faced by users which has four main stages, namely analysis, design, prototype, and evaluation focusing on user needs analysis, prototype design using figma, and usability testing using a system usability scale to measure the effectiveness of the resulting design. The results of this study showed a score before the redesign of 38.33, far from the minimum average value of the system usability scale of 68. After the redesign, the score increased very well, the score obtained was 82 indicating success in improving user experience and needs. Changes occurred on the login page, main menu, and the presence of a brush type search feature to make it easier for users when using the ibisPaint X application. This study provides a good contribution to the development of the ibisPaint X application in meeting user needs and is expected to be a reference in order to compete in the increasingly advanced digital era.
Klasifikasi Sentimen Masyarakat Terhadap Revisi Undang-Undang Tentara Nasional Indonesia Menggunakan Naïve Bayes Classifier Abdul Haris Kurnia Sandi Harahap; Haerani, Elin; Oktavia, Lola; Okfalisa; Kurnia, Fitra
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.v5i4.615

Abstract

The revision of the Indonesian National Armed Forces Bill (RUU TNI) has become a hot topic in Indonesian public policy and has sparked controversy among the public due to its sudden emergence and lack of open planning process. This has raised concerns about the potential for military domination and the return of the dual function of the ABRI (Indonesian Armed Forces). The classification of public sentiment towards the RUU TNI is the focus of this study. Comments are categorized into two types of sentiment classes, namely positive and negative. The research stages include data collection, sentiment labeling, data cleaning, text normalization to lowercase letters, sentence or document segmentation into smaller parts, text data normalization, negation handling, stopword removal, and stemming, weighting using the TF-IDF technique, model classification development, and evaluation of the model's performance. The Naïve Bayes Classifier method classified 1,547 comment data points collected from two Instagram social media accounts. The Naïve Bayes Classifier model achieved an accuracy of 83.74%, precision of 81.17%, recall of 87.86%, and an F1-score of 84.38%. This study has limitations, including the limited amount of data collected. These include an imbalance in the amount of data between sentiment categories, data from only one social media platform, and the suboptimal identification of positive and negative sentiments. It is recommended that future research compare this method with other classification methods, expand the dataset, broaden the scope of data collection by involving various social media platforms over a wider time span, thereby providing a more comprehensive picture of public opinion, and test a wider range of algorithm combinations. This study can serve as an initial indicator for rapid policy evaluation, where positive or negative comments from the public on social media can provide important input in assessing the effectiveness of a policy.
Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra; Muhammad Fikry; Surya Agustian; Siti Ramadhani
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.v5i4.626

Abstract

Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
Implementasi Fuzzy Sugeno Berbasis IoT untuk Peringatan Kualitas Air Akuarium Ikan Mas Koki Rahman, Muhammad Taufikur; Yanto, Febi; Haerani, Elin
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.v5i4.627

Abstract

The manual monitoring of aquarium water quality is often ineffective due to time constraints and the potential delays in detecting critical parameter changes that can threaten fish health. This research develops a real-time water quality monitoring system for goldfish aquariums based on the Internet of Things (IoT) using the Sugeno fuzzy logic method. The system utilizes an Arduino Uno R4 WiFi microcontroller to process data from turbidity, Total Dissolved Solids (TDS), and water temperature sensors. The Sugeno fuzzy method is chosen for its ability to produce precise numerical outputs based on fuzzy rules. To assess water quality, the sensor data undergoes fuzzification, rule evaluation, implication/aggregation function application, and defuzzification stages. The measurement results are then processed in real-time and sent via WiFi connection to the Blynk application, which serves as a monitoring medium and sender of warning notifications to users when water quality falls outside safe limits, while information is also displayed on the OLED screen of the system. Water quality assessment is classified based on fuzzy output values into several condition categories: 0-20 (Very Good), 21-40 (Good), 41-60 (Fair), 61-80 (Poor), 81-100 (Very Poor). Based on the test results, the system has been proven to effectively detect and classify water quality conditions with high accuracy, as well as provide effective warning notifications. This system is expected to assist aquarium owners in maintaining optimal environmental conditions for the health of goldfish in an automatic, sustainable, and efficient manner.
Analisa Pengaruh Technology Readiness Index Pada Penggunaan E-Wallet Hardianto, Arvin; Fronita, Mona; Rahmawita M, Medyantiwi; Marsal, Arif
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.v5i4.632

Abstract

Current technological developments have also driven transformation in payment systems, one of which is through the implementation of a non-cash lifestyle digital wallet, such as DANA, an application used as a medium for storing and transacting electronic money via smartphone devices. Although the number of DANA users and transaction values ??have shown a significant increase, the adoption of technology does not always have a positive impact. There are still problems, one of which is that some users report delays in incoming balances. The success of implementing technological innovation is influenced by various factors, one of which can be measured using the Technology Readiness Index (TRI) instrument developed by Parasuraman. This study aims to identify factors that inhibit the use of DANA Digital Wallets in Indonesia. The results of the analysis using SPSS version 26 software show that the TRI value is at 3.00994 which is included in the moderate category, proving that the resulting influence is standard, with the highest value on the Discomfort variable (0.77892) and the lowest value on the Insecurity variable (0.72978).

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