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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 39, No 1: July 2025" : 65 Documents clear
Renewable energy conversion systems for global emission neutralization Suwarno, Suwarno; Cahyadi, Catra Indra; Manurung, Pardamean; Rahim, Abdul; Tanjung, Farhan; Birje, Herman; Syafni, Fadly; Kurnia, Muhammad Ridho; Faruqi, Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp79-88

Abstract

Fossil fuel power plants still play an essential role in providing energy worldwide, but their environmental impact will contribute significantly to emissions and environmental pollution. To reduce these emissions, renewable energy offers a solution to reduce global emissions. This study proposes a renewable energy modeling system using hybrid optimization of multiple energy resources (HOMER) simulation on renewable energy systems for economic savings. This simulation can combine photovoltaic (PV), wind power (WP), and converter systems. The hybrid combination of PV and WP is the most appropriate and economical choice at the research location. The results showed that the modeling of the renewable energy hybrid system made a significant contribution, with an initial investment cost of IDR 107,474.43 million and an annual operating cost of IDR 22,540.23 million, 41% lower on condition now with an estimated return on investment of 11 years. The results of this study can be used as recommendations for similar conditions in other places. Policymakers can use this model to provide incentives and have a positive impact on hybrid power plants (HPS) in neutralizing global emissions.
Study on neuromorphic computation and its applications Chature, Anjali; Raganna, A.; Venkateshappa, Venkateshappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp272-282

Abstract

Neuromorphic computing offers a promising alternative to traditional von Neumann architectures, especially for applications that require efficient processing in edge environments. The challenge lies in optimizing spiking neural networks (SNNs) for these environments to achieve high computational efficiency, particularly in event-driven applications. This paper investigates the integration of advanced simulation tools, such as Simeuro and SuperNeuro, to enhance SNN performance on edge devices. Through comprehensive studies of various SNN models, a novel SNN design with optimized hardware components is proposed, focusing on energy and communication efficiency. The results demonstrate significant improvements in computational efficiency and performance, validating the potential of neuromorphic architectures for executing event-driven scientific applications. The findings suggest that neuromorphic computing can transform the way edge devices handle event-driven tasks, offering a pathway for future innovations in diverse application domains.
Effective methods for employee performance assessment Himawan, Agatha Beny; Kridalukmana, Rinta; Prahasto, Toni
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp509-522

Abstract

This study aims to select the most effective multi-criteria decision-making method used in an employee performance appraisal system. The approach used in this study is a comparative experiment where three multi-criteria decision-making methods simple additive weighting (SAW), analytical hierarchy process (AHP), and technique for order preference similarity to an ideal solution (TOPSIS) are compared. The dataset involves 16 employees, considering input data such as work behavior scores, and performance targets (SKP). The criteria for evaluating work behavior include service quality, accountability, competence, harmony, loyalty, adaptability, collaboration, and achievement of targets. The comparison results were tested using a one-way ANOVA to evaluate whether there are significant differences among the three methods, as well as to provide supporting evidence for the conducted research. The results indicated that the SAW method provides the most accurate and relevant performance assessments while AHP yields less precise rankings as some employees received the same scores despite having different workloads. TOPSIS also produced rankings that did not accurately reflect the relative workloads. Implementing the SAW method in the employee performance information system enhances the assessment process, making it faster, more objective, transparent, and credible. Thus, SAW emerges as the most effective method for aligning performance scores with employee roles and responsibilities.
Autonomous driving system and system hacking protection using V2X communication Rhee, Eugene; Cho, Junhee
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp131-138

Abstract

In this paper, a new autonomous driving system is proposed and problems such as systematic errors that may occur in the autonomous driving system were solved through vehicle to everything (V2X) communication technology. In the actual driving environment, accidents caused by the absence of communication between drivers and communication with infrastructure are frequently exposed. To solve these problems, a system was established that linked V2X communication with a vehicle system. In order to predict and study how this technology works in real traffic situations, it requires a lot of time, manpower, and funds because it requires building an environment similar to real traffic situations and using measuring equipment. For this reason, the system was built with simple model, and the research was conducted through simple simulation. In addition, as network technology and sensing technology for autonomous vehicles develop, the risk of hacking is also increasing. In this paper, various expected attack paths and methods that can hack autonomous vehicles are explained, and methods for defending them are presented.
Banking security and performance of islamic banks in middle east: the role of regulatory quality Almomani, Mohammed Abd-Akarim; Al-Momani, Adai
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp691-699

Abstract

Transaction security is critical for the reputation and trust of banks. Few studies examined how transaction security can impact the financial performance of Islamic banks in developing countries with mixed results emerging in the literature. The research examines how transaction security affects bank financial performance. Three indicators are used to measure the financial performance and includes return on assets (ROA), return on equity (ROE), and Tobin’s Q. Regulatory quality are proposed as a moderating variable. Data was collected from 59 banks in MENA between 2015 and 2022. The results showed that transaction security affected positively ROE and Tobin’s Q. However, there is no significant effect on ROA. Regulatory quality moderated only the effect of transaction security on ROE and Tobin’s Q. Enhance the transaction security and improving the regulatory quality will enhance the financial performance of banks in MENA.
Enhancing TV program success prediction using machine learning by integrating people meter audience metrics with digital engagement metrics El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp353-363

Abstract

With the emergence of numerous media services on the internet, television (TV) remains a highly demanded medium in terms of reliability and innovation, despite intense competition that compels us to devise strategies for maintaining audience engagement. A key indicator of a TV channel’s success is its reach, representing the percentage of the target audience that views the broadcasts. To aid TV channel managers, the industry is exploring new methods to predict TV reach with greater accuracy. This paper investigates the potential of advanced machine learning models in predicting TV program success by integrating people meter audience metrics with digital engagement metrics. Our approach combines convolutional neural networks (CNNs) for processing digital engagement data, long short-term memory (LSTM) networks for capturing temporal dependencies, and gaussian processes (GPs) for modeling uncertainties. Our results demonstrate that the best-performing hybrid model achieves a prediction accuracy of 95%. This study contributes to the field by addressing manual scheduling errors, financial losses, and decreased viewership, providing a more comprehensive understanding of audience behavior and enhancing predictive accuracy through the integration of diverse data sources and advanced machine learning techniques.
Advancements in gas leakage detection and risk assessment: a comprehensive survey Bhavani, Y.; Vodapally, Sanjusree; Bokka, Dinesh; Muddasani, Harshitha Varma; Kasturi, Deepika
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp614-624

Abstract

Gas leakage is the main problem that harms the environment, infrastructure and public safety. Technology is increasing rapidly nowadays. So, there must be advancements in the methods used. Many methods have been come across to solve this problem. This survey paper explores various methods and technology used to solve the problem. Many methodologies have been suggested to reduce the risk of gas leaks and improve detection systems. It investigates cutting-edge models for estimating the effects of liquefied natured gas (LNG) leakage accidents, comprehensive wireless sensor network (WSN) is set up for detecting gas leaks in advance, and neural network and Kalman filter-based gas leakage early warning systems. Current developments include factors like point of interest (PoI), human data movement and gas pipelines. As technology increases, there would be major threat of authentication. So, it also looks on methods for user authentication based on different patterns to mobile applications. Especially in smart home environments, there is a need to improve security. This survey provides complete understanding of present and future directions for the researchers in gas leakage detection and risk management through various methods and their evaluation.
PRDTinyML: deep learning-based TinyML-based pedestrian detection model in autonomous vehicles for smart cities Alajlan, Norah N.; Alhujaylan, Abeer I.; Ibrahim, Dina M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp283-309

Abstract

Detecting pedestrians and cars in smart cities is a major task for autonomous vehicles (AV) to prevent accidents. Occlusion, distortion, and multi-instance pictures make pedestrian and rider detection difficult. Recently, deep learning (DL) systems have shown promise for AV pedestrian identification. The restricted resources of internet of things (IoT) devices have made it difficult to integrate DL with pedestrian detection. Tiny machine learning (TinyML) was used to recognize pedestrians and cyclists in the EuroCity persons (ECP) dataset. After preliminary testing, we propose five microcontroller-deployable lightweight DL models in this study. We applied SqueezeNet, AlexNet, and convolution neural network (CNN) DL models. We also use two pre-trained models, MobileNet-V2 and MobileNet-V3, to determine the optimal size and accuracy model. Quantization aware training (QAT), full integer quantization (FIQ), and dynamic range quantization (DRQ) were used. The CNN model had the shortest size with 0.07 MB using the DRQ approach, followed by SqueezeNet, AlexNet, MobileNet-V2, and MobileNet-V2 with 0.161 MB, 0.69 MB, 1.824 MB, and 1.95 MB, respectively. The MobileNet-V3 model’s DRQ accuracy after optimization was 99.60% for day photos and 98.86% for night images, outperforming other models. The MobileNet-V2 model followed with DRQ accuracy of 99.27% and 98.24% for day and night images.
HangeulVR: an immersive and interactive Korean alphabet learning on virtual reality Nasikun, Ahmad; Mahendra, Muhammad Fadhil; Dessiar, Achmad Rio
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp545-553

Abstract

Learning a new foreign language promises numerous benefits, such are career advantage, culture exposure, and traveling opportunity. However, it comes with a cost of considerably significant efforts and time commitment. The challenge intensifies when dealing with languages characterized by distinctive scripts, such as Hangeul in Korean language. The requisite mastery of Hangeul characters precedes the exploration of fundamental linguistic elements, including grammar, pronunciation, speaking, and writing. In this research, we propose an innovative, immersive, and interactive methodology for Hangeul acquisition employing virtual reality (VR). Our study transports participants into a virtual environment, guided by a gamification framework designed to facilitate Hangeul learning. Participants are able to learn basic pronunciation, listening, and Hangeul writing, three fundamental aspects of learning the Korean alphabet. Empirical findings from our experiments show the potential of its usage, indicated by its system usability scale (SUS) of 74.4.
Core methodological classes of text extraction and localization-a snapshot of approaches Jayaram, Dayananda Kodala; Devegowda, Puttegowda
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp455-465

Abstract

The motivation to work on text extraction and localization is quite a substantial that potentially influences a larger area of application right from business intelligence to advanced data analytics. At present, there are massive archives of literatures addressing varying ranges of problems associated with text extraction and localization with an effective realization of respective contribution as well as on-going issues. However, problem statement is that all these massive implementation studies are further required to converge down in order to realize the core classes of methodologies involved in text extraction. Hence, this manuscript uses desk research methodology to address this issue by presenting a compact insight of core methodological classes where all the recent implementation work are converged down to understand its strength and weakness. The research outcome contributes towards facilitating information of current research trend and identified research gap. The proposed review study assists in undertaking decision of suitable approach of text extraction, localization, detection, recognition, and classification.

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