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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 329 Documents
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Klasifikasi Citra Medis Penyakit Pneumonia dengan Metode Convotional Neural Network Khairudin; Bobi Agustian; Nursakinah, Badriah
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.v5i4.576

Abstract

Pneumonia is a pulmonary infection that remains one of the leading causes of death among children under five, especially in developing countries. Early detection and rapid diagnosis are critical in managing this disease, particularly in regions with limited access to medical professionals. This study aims to develop an automatic classification system for pediatric chest X-ray images using the Convolutional Neural Network (CNN) method to detect pneumonia. The dataset used consists of 5,863 pediatric chest X-ray images categorized into two classes: Pneumonia and Normal. The images underwent preprocessing stages including resizing, normalization, augmentation, and noise removal. The CNN architecture includes stacked convolutional layers, max pooling, dropout, and a fully connected layer with sigmoid activation. The model was trained using 80% of the data for training, 10% for validation, and 10% for testing. Performance was evaluated using accuracy, precision, recall, and F1-score metrics. Evaluation results showed that the model achieved over 93% accuracy, with 92.5% precision, 94.2% recall, and an F1-score of 93.3%. Transfer learning using pretrained models (VGG16 and ResNet50) further improved performance. These findings demonstrate that CNN is an effective tool for medical image classification and has strong potential to support fast and accurate pneumonia diagnosis, especially in resource-limited healthcare settings.
Klasifikasi Gender Berbasis Citra Wajah Menggunakan Clustering Dan Deep Learning Okky Prasetia; Syaeful Machfud; Rosyani, Perani; Bobi Agustian
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.v5i4.581

Abstract

Gender classification based on facial images is a significant challenge in the field of computer vision, especially when dealing with unstructured data sourced from social media platforms. This study proposes an integrated approach combining facial image preprocessing, clustering methods, and deep learning to enhance the accuracy of gender classification. The dataset used was obtained from a Big Data Competition and consists of male and female face images sourced from Instagram. Preprocessing was performed using OpenCV for face detection and cropping. Subsequently, the data were clustered using K-Means and DBSCAN algorithms to reduce noise and redundancy. Gender classification was then conducted using a sequential learning model based on Inception_v3, enhanced with Agglomerative Clustering for feature refinement. The evaluation of the system demonstrated strong performance with an accuracy of 92.97%, F1-score of 0.89556, precision of 0.97727, and recall of 0.83069. These results confirm that the integration of clustering techniques and deep learning significantly improves the effectiveness of gender classification based on facial images, especially for open-source and non-curated datasets.
Implementasi Algoritma K-Means Clustering untuk Identifikasi Lokasi Strategis Coffee Shop Rohman, Lahuri Gofarana; Cecep Nurul Alam; Beki Subaeki
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.v5i4.600

Abstract

The rapid growth of coffee shops in Bandung City has led to increasingly fierce competition among business owners, particularly in choosing strategic locations. Inappropriate location selection can negatively impact customer attraction and business sustainability. This study aims to identify strategic areas for coffee shop development in Bandung City using the spatial-based K-Means Clustering algorithm. The data used consists of active food establishment locations obtained from the Open Data Kota Bandung portal, which includes latitude and longitude information. The K-Means algorithm with K-Means++ initialization was used to group the restaurant locations into three clusters based on geographical proximity. The clustering process was carried out in two iterations, beginning with the initial centroid determination, distance calculation using the Euclidean formula, and centroid updates until convergence. Final results show that the areas of Jl. Aceh Cluster 0 at coordinates (-6.911431, 107.622713), Jl. Setiabudi Cluster 1 at coordinates (-6.879891, 107.600774), and Jl. Kebon Jati Cluster 2 at coordinates (-6.917228, 107.598990) have different strategic potentials suited to specific coffee shop concepts. Evaluation was conducted through spatial distribution visualization, minimum distance analysis, and cluster stability. This study confirms that the K-Means method is effective in supporting spatial-based decision-making for business development.
Analisis Perbandingan Metode Convolutional Neural Network (CNN) untuk Deteksi Warna pada Objek Prastita, Dila Ayu; Andika Setiawan; Ilham Firman Ashari
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.v5i4.617

Abstract

This research aims to evaluate and compare the performance of three Convolutional Neural Network (CNN) architectures, namely VGG16, Xception, and NASNet Mobile, in detecting colors on objects. The main problem in this research is to determine the architecture with the most effective and efficient combination of hyperparameters to detect colors on objects. The research process includes problem identification, object color dataset collection, image preprocessing, training of three CNN models (VGG16, Xception, and NASNet Mobile), and performance evaluation using accuracy, precision, recall, and f1-score metrics. In addition, a comparative analysis of the performance of each model based on the combination of hyperparameters used, such as optimizer, batch size, and learning rate. The analysis also includes evaluating computational efficiency by measuring the training time and prediction time of each model, as well as examining the relationship between architectural complexity and classification performance. The results of the analysis are used to determine the most optimal model that is feasible to implement in an object color detection system. The test results show that NASNet Mobile provides the best performance with an accuracy of 88% and a prediction time of 2 minutes 22 seconds for 2904 images. The Xception model produced an accuracy of 86% with a prediction time of 4 minutes 22 seconds, while VGG16 recorded an accuracy of 90% with a prediction time of 10 minutes 9 seconds.
Analisis Sentimen Publik terhadap ‘Save Raja Ampat’ di Media Sosial Menggunakan Model IndoBERT Eko Putro, Dimas; Juarsa, Doris; Putra Hermana, BP; Bagastian, Bagastian; Sulistiani, Heni
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.621

Abstract

The "Save Raja Ampat" campaign has emerged as a significant environmental issue that has garnered widespread public attention on social media platforms, particularly TikTok and YouTube. Videos tagged with #SaveRajaAmpat have sparked various public responses, ranging from full support to criticism of natural resource exploitation. This phenomenon highlights the importance of understanding public sentiment as an indicator of the campaign's effectiveness. This study aims to analyze public sentiment toward the campaign using a language modeling approach based on artificial intelligence, namely IndoBERT. The data were obtained from user comments on TikTok videos promoting the “Save Raja Ampat” campaign, totaling 10,000 comments. The analysis process involved several stages, including data preprocessing, sentiment labeling (positive, negative, neutral), and the training and evaluation of the IndoBERT model. Preliminary results indicate that the majority of public sentiment toward the campaign is positive, with the model achieving an accuracy rate of 71% in sentiment classification. This study contributes to understanding public perception of environmental issues and demonstrates the effectiveness of using the IndoBERT model in the context of social media.
Klasifikasi Tingkat Risiko Gempa di Indonesia Menggunakan Pola Spasial dan Temporal Berbasis Decision Tree Prasetio, Mugi; Sulistiani, Heni; Inonu, Onassis Yusuf; Magda, Kardita; Santosa, Budi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.624

Abstract

Indonesia is an area that is very vulnerable to earthquakes due to its location in the meeting zone of active tectonic plates. This study aims to classify the level of earthquake risk based on spatial and temporal patterns using the Decision Tree method as a solution in predicting potential earthquake hazards. The data used is earthquake data in Indonesia from 2015 to 2023 obtained from public datasets, including location information (latitude and longitude), event time (year and month), and earthquake magnitude. Earthquakes are categorized into three risk classes: Low (M < 4.0), Medium (4.0 ? M < 6.0), and High (M ? 6.0). The Decision Tree model was successfully built with an average accuracy of 88% on the test data. The results show that earthquakes mostly occur in active subduction zones such as the Sunda Subduction Zone (Sumatra and Java), Banda Arc (Nusa Tenggara, Maluku, Seram), Sulawesi, and Papua. Temporal analysis also shows fluctuations in the number of earthquakes by year and season, with increased activity in certain months. The spatial visualization reinforces the finding that the eastern region of Indonesia is more seismically active than the western region. This research proves that machine learning approaches can be used to support earthquake disaster mitigation through historical data-based risk identification.
Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung Ht. Barat, Ade Ismiaty Ramadhona; Astuti, Wiwik Sri; Wanto, Anjar; Solikhun, Solikhun
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.631

Abstract

Corn is one of the major agricultural commodities that plays a strategic role in national food security. However, its productivity often declines due to leaf diseases such as Blight, Common Rust, and Gray Leaf Spot. Manual disease detection is considered inefficient and prone to human error, especially on a large scale. This study aims to develop an automated deep learning-based system for accurate classification of corn leaf diseases. The proposed model utilizes the Convolutional Neural Network (CNN) architecture VGG16 with a transfer learning approach. The dataset comprises 1,200 labeled images of corn leaves categorized into four disease classes, obtained from Kaggle. Image augmentation techniques were applied to improve data diversity and enhance model generalization. The performance of VGG16 was compared with VGG16 Baseline architecture and MobileNetV2. Experimental results show that VGG16 with transfer learning achieved the highest classification accuracy of 96.25%, outperforming the baseline VGG16 (92.92%) and MobileNetV2 (84.58%). These findings demonstrate the effectiveness of VGG16-based transfer learning in automating corn leaf disease detection, supporting the implementation of precision agriculture technology.
Sistem Pendukung Keputusan Penentuan Kelayakan Pemberian Kredit UMKM Menggunakan Metode AHP dan Weighted Product Putra, Andhita Firman Syah; Purnomo, A Sidiq
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.646

Abstract

Microfinance institutions often conduct manual evaluations of creditworthiness for UMKM, resulting in subjective and inconsistent decisions. This study aims to develop a decision support system for determining creditworthiness for UMKM using the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods. AHP is used to evaluate the relative importance of criteria through pairwise comparisons. This process generates weights for each criterion with a consistency matrix as a validation tool. The results of the weighting are then used in the WP method, which calculates the final score for each prospective borrower by multiplying the performance value against the criteria and weights. The case study in this research is at the Koperasi Simpan Pinjam (KSP) Makmur Jaya. The results obtained show that the system is capable of producing objective alternative rankings, where prospective borrowers with the highest VI values are considered the most eligible to receive credit. After testing, the system also demonstrated consistency with manual calculations. Overall, this study shows that the combination of the Analytical Hierarchy Process (AHP) and Weighted Product (WP) can be effectively applied in multi-criteria decision-making in the microfinance sector.
Rancang Bangun Aplikasi Edukasi Interaktif Pengenalan Pahlawan Indonesia Menggunakan Algoritma Fisher-Yates Nadhif Nandana Kartomo; Salmon; Rizky Zakariyya Rasyad
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.v5i4.672

Abstract

This research was conducted to develop an interactive media application for introducing the names of Indonesian national heroes. If successful, this research is expected to provide users with an easier way to learn and recognize Indonesian national heroes. The study was carried out at SMP 21 Samarinda. The data collection methods used were observation, in which direct observations were conducted at SMP 21 Samarinda, and interviews, involving direct question-and-answer sessions related to the objectives of the research. The system development tools used in this study were Adobe Flash CS6 and Adobe Photoshop CS6. The final result of this research is an interactive media application for introducing the names of Indonesian national heroes, implementing the Fisher-Yates shuffle algorithm to make the learning experience more engaging and easier to understand for users.
Perancangan Sistem Informasi Pengelolaan Akun Pada Aplikasi SAP dengan Metode Waterfall Hamdan Zulfa Rais; Dimas Febriawan
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.673

Abstract

The need for an account management application in the SAP system is crucial for maintaining the reliability of annual audit trails, which are an essential part of corporate data governance. This application is designed to facilitate access rights management and record every user activity within the SAP system, aiming to ensure the integrity and security of company data. Without a robust system for managing access rights and monitoring user activity, audit trails become vulnerable to risks such as data misuse, shifting of responsibilities, and invalid user credentials. This can lead to financial and reputational losses for the company. This research aims to design and build an effective SAP system account management application to enhance audit trail reliability and minimize risks that can occur in the SAP system's account management process. A good information system will enable companies to manage and secure data more effectively, support license audits, and meet applicable compliance standards. The Waterfall method is used to achieve these objectives, encompassing requirements analysis, system design, implementation, testing, and maintenance phases. Through this approach, the expected outcome is an SAP account management application that is effective, secure, and supports audit standards as well as corporate data security governance.

Page 23 of 33 | Total Record : 329

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