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International Journal of Electrical Engineering, Mathematics and Computer Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481910     EISSN : 30481945     DOI : 10.62951
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The scope of the this Journal covers the fields of Electrical Engineering, Mathematics and Computer Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 32 Documents
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Application of Machine Learning Algorithms for High-Accuracy Image Segmentation in Medical Imaging Fatima Ibrahim Al-Saad; Mohammed Abdullah Al-Hakim
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 1 (2024): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i1.72

Abstract

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.
Analyzing the Efficiency of Battery Storage Systems in Renewable Energy Grids Fatima Khalid Al-Rashid; Omar Youssef Al-Hassan; Layla Mahmoud Al-Zain
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 2 (2024): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i2.73

Abstract

Battery storage systems are essential for stabilizing renewable energy grids, especially for sources like solar and wind power that are inherently variable. This study evaluates various battery technologies and their effectiveness in storing and redistributing energy within renewable energy grids. Through simulations and analysis of performance metrics, the study offers insights into optimal operating conditions for different battery types, highlighting their role in enabling sustainable and stable energy distribution.
A Deep Learning Approach to Fault Detection in Industrial IoT Networks Alfina Herawati; Bagus Setyo
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 2 (2024): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i2.74

Abstract

Industrial IoT (IIoT) networks, critical for automation and smart manufacturing, are susceptible to faults due to their complexity and the large number of connected devices. This paper introduces a deep learning-based approach for early fault detection in IIoT networks. By leveraging recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the system effectively identifies anomalies in real-time, helping to reduce system downtime and enhance operational efficiency in industrial settings.
Mathematical Models for Predicting Electric Vehicle (EV) Charging Demand in Urban Areas Tiara Shella; Aura Sabila
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 2 (2024): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i2.75

Abstract

As electric vehicles (EVs) gain popularity, understanding the demand for EV charging infrastructure becomes essential for effective urban planning. This paper develops mathematical models to predict EV charging demand based on various factors, including population density, traffic patterns, and energy consumption data. The models provide valuable insights for city planners regarding the optimal placement and capacity of EV charging stations to meet future demand, facilitating the transition to a more sustainable urban environment.
Optimizing Data Transmission in Wireless Sensor Networks Using Machine Learning Olusegun Adebayo Johnson; Chukwuemeka Ayodele Obi
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 2 (2024): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i2.76

Abstract

Data transmission efficiency is crucial in wireless sensor networks (WSNs), where limited battery life and signal reliability are significant concerns. This research explores various machine learning algorithms aimed at optimizing data transmission in WSNs, focusing on reducing energy consumption and enhancing network stability. Simulation results indicate marked improvements in efficiency, making WSNs more viable for long-term deployment across diverse environments.
Quantum Cryptography for Enhanced Security in Cloud-Based Systems Kari Elisabeth Larsen; Lars Magnus Johansen; Olav Alexander Pedersen
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 2 (2024): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i2.77

Abstract

Cloud-based systems are increasingly vulnerable to a range of cybersecurity threats, driving the need for advanced encryption methods. This paper investigates the potential of quantum cryptography in securing cloud environments, focusing on the use of quantum key distribution (QKD) protocols. By leveraging the principles of quantum mechanics, the study demonstrates significant improvements in data security, offering enhanced protection against eavesdropping and paving the way for more resilient cloud security frameworks.
Analysis Management Risk Technology Information on CV. Aren Jaya Use ISO 31000:2018 Muhamad Rizky; Faaza Bil Amri
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.92

Abstract

CV. Aren Jaya is an AC service provider located in South Tangerang. In 2017 this shop began to develop by opening new and used AC sales services, AC installation services, used washing machine sales services and washing machine service services. In carrying out the company's business process activities, of course there will always be possible risks and potential risks that can threaten and disrupt business process activities. The need for a risk analysis of existing IS/IT resources in the company, using the ISO 31000:2018 method related to risk management. The purpose of this study is to minimize all possible risks that are currently being experienced or will also occur and provide appropriate recommendations regarding risks that may occur at any time. The research method used in the risk analysis uses the ISO 31000:2018 framework. The results of this risk analysis are in the form of an analysis of the possibility of existing risks, evaluations and risk mitigation plans so that they can produce improvements to existing risks. The final result of the research produces risk recommendations, so the company can adjust to the priorities of the existing risk level, so that it does not interfere with business activities at CV. Arena Jaya.  
Development of Hand Gesture Detection Application for Slap Mosquito Game Based on Image Processing Rajhaga Jevanya Meliala; Nur Indah Chasanah; Jonser Steven Rajali Manik; Anggito Rangkuti Bagas Muzaqi; Syah Bintang; Endang Purnama Giri; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.108

Abstract

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.
Analysis and Testing of the Combox Web Application System Using Black Box Testing with the Equivalence Partitioning Method Dini Nurul Azizah; Ibnu Aqil Mahendar; Muhammad Fillah Alfatih; Setiady Ibrahim Anwar; Nabil Malik Al Hapid; Aditya Wicaksono; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.118

Abstract

This research focuses on evaluating the Combox web application, a digital tool designed to help Food and Beverage (F&B) business owners strengthen their online presence. The analysis was carried out through Black Box Testing, specifically using the Equivalence Partitioning method, to assess core functionalities like login, logout, product management, and pagination. The findings reveal that while most features function as intended, there are issues with product addition and editing, as well as pagination when no data is available. These results highlight areas that need refinement to improve the application’s reliability and user experience. In summary, this research supports the advancement of a digital platform that enables F&B businesses to harness technology effectively in today’s competitive landscape.
Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN) Hakim, Ghaeril Juniawan Parel; Simangunsong, Gandi Abetnego; Rangga Wasita Ningrat; Jonathan Cristiano Rabika; Muhammad Rafi' Rusafni; Endang Purnama Giri; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.123

Abstract

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

Page 2 of 4 | Total Record : 32

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