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Engineering, Mathematics and Computer Science Journal (EMACS)
Published by Universitas Bina Nusantara
ISSN : -     EISSN : 26862573     DOI : https://doi.org/10.21512/emacs
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics
Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 165 Documents
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A Random Forest Approach For Gender Classification Based on Keystroke Dynamics Maulina, Ayu; Charisma, Rifqi Alfinnur
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13445

Abstract

The purpose of this study is to improve gender categorization by examining the usage of keyboard dynamics, with enhanced model performance through data standardization and appropriate feature selection. Features including gender, age, handedness, language, education, and metrics measuring typing behavior like mean_latency, std_latency, and frequency are all included in the dataset. Correlation analysis served as the foundation for the feature selection procedure, and data normalization was performed to guarantee consistency among the characteristics that were chosen. Because of its stability and capacity to handle complicated data, the Random Forest classifier was selected. The findings demonstrate that the Random Forest model performed better than benchmark models, such as SVM, in terms of F1-score, recall, accuracy, and precision. The results emphasize how important it is to choose the appropriate characteristics and standardize the data in order to increase predictive accuracy. By showcasing keystroke dynamics' capacity for gender categorization, this study advances the area and creates opportunities for further research in user experience improvement, digital service customisation, and online behavioral analysis. All things considered, the study highlights how crucial feature engineering and model tuning are to getting better categorization outcomes.
Cost Analysis of Construction Cost Planning for Landfill Site Junaedi, Nurhayati; Bayuaji, Ridho; Susilo, Alfred Jonathan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13800

Abstract

Accurate construction cost planning is essential to ensure project success, as inaccurate estimates may lead to delays, cost overruns, and reduced quality. Landfill construction, which is more complex than many other infrastructure projects, includes several components such as landfill work, leachate treatment facilities, and supporting infrastructure. The purpose of this study is to determine the major cost factors that have a major impact on the overall cost of building landfill sites.  With the aid of SPSS software, a regression analysis was carried out using cost data from six landfill projects in Java Island that were completed between 2013 and 2018.  With a Sig value of 0.000 (<0.05) and a very strong correlation (Pearson Correlation 0.991, within the 0.8–1.0 interval), the results show that landfill work (X3) significantly affects total costs (Y).  Leachate treatment facilities (X4) are another crucial element in a number of situations, but landfill work (X3) consistently represents the largest portion of construction costs, according to proportion analysis. These results demonstrate the growing significance of environmental facilities and point to landfill work as the main factor influencing landfill construction costs.  The study offers contractors and federal and local governments useful information for creating more precise cost estimates, maximizing budgetary allotments, and enhancing planning and development efficiency for landfill projects.
Rice Hull Management System: A Mobile-based Application Tool for Cooperatives Usita, Maricris; Timalog, Cris Ann Fogusa; Javier, Maychiel; Indap, Jhune Carlo; Calera, Gricelyn; Ramos, Jessa Jane
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.13839

Abstract

This study focuses on the design and thorough assessment of the Rice Hull Management System RHMS for mobile devices, which targets cooperatives located specifically in Occidental Mindoro, Philippines. The Philippines is well-known for its rice production; however, there are severe concerns regarding environmental sustainability due to the poor management of rice hulls. To offer a solution, the Rice Hull Management System mobile application was created for user cooperatives from Occidental Mindoro. The application was developed using the RAD methodology with React Native and Firebase, enabling the system to be responsive, scalable, and secure. Through the utilization of RHMS, rice hull transactions are processed more efficiently with automated summaries, precision reports, advanced analytics, and real-time updates, all of which facilitate information-based decision-making and foster eco-friendly agricultural practices within and outside the region. The implementation evaluation of the system was conducted using a combination of surveys, usability tests, and performance benchmarks, which included IT specialists, cooperative staff, and members. System reliability was demonstrated to be high, with a Cronbach’s alpha greater than 0.80 and high user satisfaction, with grand mean scores ranging from 4.08 to 4.23 (“Very Good”). Evaluated criteria for the RHMS included efficiency, integration, usability, reliability, safety, and mobility, all of which received excellent ratings, confirming the system's effectiveness in resolving operational manual inefficiencies and enhancing transparency. This study focuses on the application of technology in rice hull waste management to promote environmental sustainability while meeting the requirements of agricultural cooperatives. The RHMS showcases considerable promise for development and implementation across agricultural supply chains, given its secure, easy-to-use, and flexible interface for users, administrators, and cooperatives. Proposed recommendations included continuous system enhancements, compliance with health regulations, integration with other platforms, and training programs to foster sustained system utilization and impact.
Comparative Analysis of CNN, LSTM, and CNN–LSTM for Indonesian Stock Prediction Joddy, Setiawan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14326

Abstract

Predicting the stock market remains a challenging task brought by the nonlinear, volatile, and dynamic nature of financial time series. While deep learning techniques have been widely applied in developed markets, studies in emerging markets such as Indonesia remain scarce. This study conducts a comparative analysis of three deep learning models—Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN–LSTM—on five randomly selected constituents of the IDX30 index. The data range from January 2020 to December 2024, providing a general view of stock movement in recent years. The models were trained on daily OHLCV (Open, High, Low, Close, Volume) data, which was formatted using a sliding-window approach. Results show that LSTM achieved the lowest RMSE of 0.0222 ± 0.0030, MAE of 0.0172 ± 0.0015, and the highest R² of 0.889 ± 0.068. The Hybrid model delivered intermediate performance, improving upon CNN but not surpassing LSTM. These findings confirm that LSTM networks are particularly effective for stock price forecasting in the Indonesian market, while hybrid CNN–LSTM architectures can provide complementary strengths by balancing short-term feature learning with long-term temporal dependencies.
Integration of Multi-Architecture Deep Learning Models for Pneumonia Detection Based on Chest X-Ray Imaging Edbert, Ivan Sebastian; Oktovianus, Louis; Tanriwan, Robert; Aulia, Alvina
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14336

Abstract

Pneumonia remains a leading cause of child mortality worldwide, particularly in resource-limited settings where diagnostic tools and expertise are scarce. Recent advances in deep learning offer an opportunity to enhance pneumonia detection through automated analysis of chest X-ray images. This study evaluates the performance of ten state-of-the-art deep learning architectures, including VGG16, ResNet50, DenseNet121, and MobileNetV2, for pneumonia detection using the widely recognized "Chest X-Ray Images (Pneumonia)" dataset. The dataset underwent rigorous preprocessing, including image resizing, data augmentation, and class balancing, to optimize model training and improve generalization. Performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC were utilized to assess model effectiveness. Among the evaluated architectures, MobileNetV2 demonstrated the best performance with an accuracy of 97.51% and an AUC of 0.9941, highlighting its potential for reliable diagnostic applications. The results also emphasize the trade-offs between sensitivity and specificity across models, offering useful insights for real-world deployment. This study underscores the importance of leveraging deep learning models in clinical diagnostics, particularly in environments with limited healthcare resources. Beyond evaluating models, the findings provide evidence-based recommendations for selecting efficient architectures that balance accuracy and computational efficiency. Future work will focus on integrating multimodal datasets, improving explainability, and validating these models in diverse clinical environments to ensure scalability, trust, and generalizability for global health applications.

Page 17 of 17 | Total Record : 165

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