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All Journal ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS)
Alvina Aulia, Alvina
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Digital Game as A Media to Increase Cognitive Intelligence of 13-18 Years Old Teenagers　 Edbert, Ivan Sebastian; Tsaniya, Devita Azka; Constantino, Bernico; Riandy, Geary; Aulia, Alvina; Nadia, Nadia
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10848

Abstract

Nowadays, Cognitive Intelligence plays an essential role especially on making decisions. The growth of digital media makes public thinks that video games are addictive. They think that video games are addictive and damaging. Games are design to refresh, challenge and help people to train their problem solving. In this research, the researcher explored the cognitive development of teenagers aged 13-18 with a puzzle-based digital game. Participants were 15 students studying in junior and senior high school. Participants were given three tests: pre-test and post-test by IQ test and a Game Engagement Questionnaire (GEQ) to explore the game's engagement from the participants' perspective. The average of Pre-Test is 113.2, while the Post-Test is 118.33. This Show that after playing the games it increases the IQ of the students. The researcher also discovered that many factors could influence the outcome of participant IQ. The GEQ shows that the participants agreed that some of the puzzle-based game might be a good or bad influence on them. Keywords: Cognitive Intelligence; Digital Games, Formal Operational Game-based Learning, Jean Piaget's Theory
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.
Co-Authors Abednego, Abednego Chandra, Natalia Constantino, Bernico Edbert, Ivan Sebastian Irina, Angela Irma Kartika Wairooy, Irma Kartika Megawati - Nadia Nadia Oktovianus, Louis Riandy, Geary Tanriwan, Robert Tsaniya, Devita Azka