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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. 1 (2025): EMACS" : 14 Documents clear
Developing Algorithm of Music Concepts and Operations Using The Modular Arithmetic Minor, Kelvin
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12562

Abstract

The rapid development of digital music technology is closely intertwined with advancements in both music theory and mathematical formalism. This study aims to bridge the gap between these fields by exploring how mathematical concepts can enhance the understanding and analysis of music theory. Specifically, the research focuses on the application of modular arithmetic to analyze the circular structure of the chromatic scale, a key concept in music. Modular arithmetic enables the identification of patterns in pitch relationships and the manipulation of musical elements like transposition and interval calculations. In addition to modular arithmetic, the study also highlights the role of regular expressions in music theory. Regular expressions provide powerful tools for pattern matching, which can be applied to recognize and categorize musical components, such as enharmonic equivalents (notes that sound the same but are named differently). These tools allow for the development of algorithms capable of generating chords from given notes or identifying chords from existing sets of notes. By integrating modular arithmetic and regular expressions, the study proposes a framework for developing mathematical models and algorithms to facilitate digital music analysis. This approach not only enhances the theoretical understanding of music but also holds practical applications in digital music production and education.
Indoor Positioning System using Gaussian Mixture Model on BLE Fingerprint Lie, Maximilianus Maria Kolbe; Jabar, Bakti Amirul
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12581

Abstract

After the release of Bluetooth Low Energy (BLE), people have been trying to use Bluetooth as an alternative source to solve indoor positioning. Unfortunately, due to the nature of Bluetooth about proximity, the propagated signal is very fluctuating. This decreases the accuracy considerably and has become one of the main problems in using Bluetooth. To combat the signal fluctuations, we propose a fingerprinting-based concept of using received signal strength (RSS) frequency distribution values as the data in the radio map, which is termed Frequency Distribution Radio Map (FDRM). We also propose a probabilistic fingerprinting-based algorithm utilizing FDRM using Gaussian Mixture Model (GMM) as the probability distribution function (PDF). In the offline phase, we compare 2 methods: k-Means only, and k-Means with Expectation-Maximization (EM); to learn the FDRM. This resulting a probability distribution function (PDF) of the RSS in each reference points for each BLEs. In the online phase, k-Nearest Neighbour (KNN) and weighted average are used to estimate the receiver’s location. The proposed method is validated over 3 different datasets taken from a 4 m x 6 m classroom equipped with chairs and tables. The experiment shows that the proposed fingerprint and model are better in capturing the environment, including the signal fluctuation. By using only k-Means in obtaining the GMM, it achieved mean error of 98.18 cm and standard deviation of 56.11 cm. By adding EM, there will be a trade-off between mean error with better standard deviation and lower computing time. It achieved standard deviation of 47.99 cm and mean error of 112.24 cm.
Effectiveness Analysis of RoBERTa and DistilBERT in Emotion Classification Task on Social Media Text Data Nabiilah, Ghinaa Zain
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12618

Abstract

The development of social media provides various benefits in various ways, especially in the dissemination of information and communication. Through social media, users can express their opinions, or even their feelings. In this regard, sometimes users also convey information or opinions according to the user's feelings or emotions. This triggers the impact of aggressive online behavior, including cyberbullying, which triggers unhealthy debates on social media. The development of deep learning models has also been developed in several ways, especially emotion classification. In addition to using deep learning models, the development of classification tasks has also been carried out using transformer architectures, such as BERT. The development of the BERT model continues to be carried out, so this study will analyze and explore the application of BERT model development, such as RoBERTa and DistilBERT. The optimal result of this study is with an accuracy value of 92.69% using the RoBERTa model.
A Horizontally Scalable WebSocket Architecture for Cost-Effective Online Examination Proctoring System on AWS Cloud Infrastructure Nugroho, Eko Cahyo
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12770

Abstract

In this research work we present the cost-effective prototype of a WebSocket server with a horizontal scaling feature on AWS Cloud Service. AWS API Gateway for establishing WebSocket connections also works but is exceedingly expensive for schools. The solution presented in this study proposes an on-premise WebSocket server deployed at AWS EC2 instances. The server utilizes Node. js's cluster module to make the most out of the CPU's cores and has also implemented a Redis pub/sub mechanism to easily horizontal scale it to many EC2 instances. The system architecture utilizes DynamoDB to store students' proctoring status recorded on the first attempt at the quiz. Then, the real status update is delivered by WebSocket message. The implementation shows effective real-time monitoring capabilities for online examinations, including student activity tracking, automated disconnection detection, and proctor-student interaction features. The results show improved cost efficiency compared to API Gateway as the WebSocket server. This solution provides schools with a cost-effective and reliable proctoring feature in LMS for implementing online examination proctoring systems at scale.

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