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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Edbert, Ivan Sebastian
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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An Experiment to Prevent Malicious Actors from Compromising Private Digital Assets Over a Public Network Hartanto, Feliks; Budiman, Budiman; Gwei, Eldwin; Gunawan, Alexander Agung Santoso; Edbert, Ivan Sebastian
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.v6i1.10389

Abstract

In the current millennium, human society has immensely improved its ability to obtain and distribute information. This change on the other hand, has caused the majority of daily routines to actively involve the usage of computers and mobile devices, which in turn has made people rely heavily on the availability of internet access. This fact was taken advantage of, causing a massive increase in public networks by people or businesses to draw in customers or just as simple public service. This increase gives both ease and risks which this paper will address, specifically on the security measures in network devices that are nearby, and the solution proposed to provide complementary insight on securing the technologies. The authors of this paper supply the main point of the research through experimental efforts i.e., by testing the solution in a real-life scenario. The solution itself involves the configuration of a Raspberry Pi into a VPN server and rerouting all traffic into the Raspberry server so that it will be encrypted and safe from the dangers that will be mentioned in later parts of this paper. The result of the experiment shows that the proposed solution can successfully encrypt the targeted packet so it can’t be read by malicious attackers. Although the solution works it can’t be simply applied to every public network due to internet connection protocols and its inconvenience. Future research will involve the improvement or rework of the solution until the issues mentioned above are solved.
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
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 Alvina Aulia, Alvina Budiman Budiman Constantino, Bernico Gunawan, Alexander Agung Santoso Gwei, Eldwin Hartanto, Feliks Nadia Nadia Oktovianus, Louis Riandy, Geary Tanriwan, Robert Tsaniya, Devita Azka