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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
Prediksi Harga Emas Mengunakan Jaringan Saraf Tiruan Algoritma Backpropagation Yupita Sari; Andri Anto Tri Susilo; Lukman Sunardi
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.v5i4.566

Abstract

Gold is a precious metal with high value that is often used as an investment commodity due to its stability and tendency to increase in price compared to other assets, such as stocks. In the global economy, gold is also an important part of international reserves in national banks. However, public awareness of the benefits of gold investment remains low. One solution to increase interest and understanding of gold investment is to predict gold prices using accurate forecasting techniques. Forecasting utilizes historical data that is analyzed to project future trends, making it an important component in strategic decision-making. This study uses the backpropagation algorithm in artificial neural networks to predict gold prices. This algorithm minimizes errors in the data training process, improves model accuracy, and provides better results in prediction classification. Additionally, this algorithm is efficient in processing large amounts of training data, resulting in a reliable prediction model. The study aims to evaluate the performance of the backpropagation algorithm in predicting gold prices, including comparing the accuracy and correlation of predictions with other algorithms. The results of the study are expected to contribute to the development of a more accurate gold price prediction model, support investment decision-making, and increase public understanding of the benefits of investing in gold. This study successfully developed an Artificial Neural Network (ANN) model to predict gold futures prices based on historical data, including features such as opening price, high, low, and trading volume. The model was trained using the Backpropagation algorithm to capture non-linear patterns in complex data. The research results encompass three main aspects: Data Preprocessing, where data was effectively processed, including converting values to numerical format and normalizing features to accelerate model convergence; Model Training, where the model was trained using 80% of the training data and tested with 20% of the testing data; Monitoring train loss and validation loss shows that the model is learning well, although there are indications of overfitting risk. Evaluation and Prediction: The model is able to predict gold prices with good accuracy on the test data. Evaluation metrics such as MAE (Mean Absolute Error) show that the prediction results are quite close to the actual values, although there is still room for improvement. Overall, this model demonstrates satisfactory performance in predicting short-term gold prices and can be used as a tool in gold price analysis based on historical data.
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.
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.

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