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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 14 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): EMACS" : 14 Documents clear
Implementation of Microservices Architecture in a Retail Web Application Using Apache Kafka as a Message Broker Daeli, Stefanus; Lase, Kristian Juri Damai; Sumihar, Yoel Pieter
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13932

Abstract

Web-based applications are often initially developed using monolithic architecture due to its simplicity and ease of deployment. However, as application complexity grows, monolithic systems face critical limitations in scalability, flexibility, and performance. This research applies a microservices architecture to a Retail Web divided into four core services: user, product, transaction, and notification management. Apache Kafka is integrated as a message broker to support asynchronous, real-time communication across services. A total of 2,001 requests were recorded during system testing using Prometheus. The srv_tulityretailaccounts service achieved an average response time of 122.8 ms, and the srv_tulityretailtransactions service maintained 188.1 ms with a 98% success rate. The srv_tulityretailproducts service also demonstrated stable performance with consistently low response times and no error spikes. Meanwhile, the srv_tulityretailnotifications service showed the highest efficiency with an average response time of 28.5 ms, CPU usage at 12.75% (1.53 of 12 cores), and memory usage at 2.07 GB (56.5%) of 3.66 GB. Throughout testing, no service exhibited resource saturation or degradation, even under concurrent load conditions. This confirms the system’s horizontal scalability, where each service can independently scale without impacting others. Overall, the microservices approach has proven effective in enhancing performance, modularity, and production-readiness, while laying a strong foundation for continuous integration, deployment automation, and future feature expansion.
A Study of Machine Learning Approach to Predict the Out performance Market of Japan’s Stock Price Amadea, Charissa; Karina, Karina; Addina, Kanisha; Adara, Keisha; Pasaribu, Asysta
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.12905

Abstract

This study employs machine learning algorithms to estimate the stock performance of Japanese companies in 2022, with a focus on examining the relationship between significant financial factors—Market Capitalization (Market Cap), Price-to-Earnings Ratio (PER), Price-to-Book Value (PBV), Return on Equity (ROE), and Debt-to-Equity Ratio (DER)—and stock performance, classified as either high-performing or low-performing. These factors are designated as independent variables. The dataset comprises 1,000 publicly listed companies in Japan and is analyzed using a logistic regression model. The dependent variable in this study is stock performance. The Akaike Information Criterion (AIC) guided model selection to optimize predictive accuracy and model complexity. The dataset was split into 70% for training and 30% for testing to ensure robust model validation. The best-performing model achieved a prediction accuracy of 62.67%, demonstrating strong sensitivity (88.83%) but weak specificity (18.75%). An AUC value of 0.6226 indicates moderate discriminatory power. The model shows good capability in detecting underperforming stocks, while its limitation lies in classifying well-performing stocks. The study suggests enhancing prediction accuracy by incorporating additional relevant variables such as macroeconomic indicators or market trends, as well as employing more complex machine learning algorithms like Random Forest or Gradient Boosting. These findings not only contribute to the literature on stock market prediction but also offer practical insights for investors in making investment decisions.
Sentiment Analysis of Slang Language Trends in Generation Alpha on Social Media Using BERT Priccilia, Shania; Erin, Erin
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13368

Abstract

Generation Alpha is a group growing up in an era of rapid digital technology advancement. Unlike previous generations who experienced a transition into technology, Generation Alpha demonstrates unique communication characteristics, particularly in their frequent use of slang, which is often difficult for older generations to understand. This gap in language understanding can lead to miscommunication, especially when the meaning of slang is misinterpreted. This phenomenon presents a challenge in establishing intergenerational communication, especially in digital and social media contexts where informal language is dominant. This study aims to explore the effectiveness of AI models in analyzing the sentiment of slang language used by Generation Alpha. Three BERT-based models were utilized in this research: BERT, RoBERTa, and DistilBERT. These models were selected based on their performance and efficiency in natural language processing (NLP) tasks, particularly in text classification and sentiment analysis. The dataset consists of 24,958 slang-based posts collected from users on the social media platform X. The analysis shows that DistilBERT achieved the highest accuracy score of 0.83, followed by BERT (0.82) and RoBERTa (0.81). These findings suggest that BERT-based models, especially DistilBERT, perform reliably in identifying the sentiment behind slang expressions used by Generation Alpha and hold potential for implementation in AI-based moderation or social media monitoring systems.
Wearable Sensors for Health Monitoring: Technologies, Applications, Challenges, and Future Perspectives Abrori, Syauqi Abdurrahman
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13765

Abstract

This article presents the state of the art and future outlook for wearable sensors for health monitoring, with emphasis on their roles in tracking physiological, biochemical, motion, and environmental parameters. Wearable sensors have moved beyond activity monitoring to facilitate clinical applications like chronic disease management, remote monitoring, and mental health evaluation. Four sensors are presented, with the sensing principle, formats, and actual-world application. System architectural elements like data acquisition, wireless communication, on-device and cloud processing, and user interface are addressed. The latest advancements like multi-modal sensor fusion, self-sustaining platforms, integration of machine learning, and skin-conformable electronics are also outlined. Wearable technology holds promise and is plagued with accuracy, battery life, privacy of data, and compatibility with health information systems. These hindrances need to be overcome if broader clinical integration and global accessibility are to take place. Avenues for development include energy-autonomous sensors, personalized feedback systems, and digital twin integration, which have promising potential for making early intervention, preventive care, and decentralized healthcare delivery possible. This overview provides a general background to researchers, developers, and clinicians striving for the next generation of digital health solutions.

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