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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 9,138 Documents
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Enhancing solar radiation forecasting using machine learning algorithms K. M., Mahesh Kumar; Soundharya, Uppuluri Lakshmi; Hemalatha, R.; C., Anjanappa; M. J., Suganya
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1463-1470

Abstract

With the increasing amount of photovoltaic (PV) generation, accurate solar radiation forecasting is essential to the safe operation of power systems. This work examines many machines learning (ML) techniques that use both exogenous and endogenous inputs to forecast sun radiation. In order to find pertinent input parameters and their values based on previous observations, the forecasting models’ performance is assessed using metrics like mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and root mean squared error (RMSE). Accurate power output forecasting is becoming more and more necessary as the need to switch to renewable energy sources (RES) like solar and wind power grows. There is a clear demand for more reliable solutions because current models frequently struggle with temporal complexity and noise. A revolutionary deep learning-based technique designed especially for green energy power forecasting was developed in response. The study uses time series smoothing and the autoregressive integrated moving average (ARIMA) model for casing in order to create a solid basis for analysis and modeling that is free of noise and outliers. The proposed method aims to address the limitations of existing forecasting methods and promote the creation of more accurate and reliable forecasts in the field of renewable energy.
Quick response code generation for e-invoicing in Saudi Arabia Sayed, Abdelrazek Wahba; Rabea, Zeinab
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1980-1989

Abstract

In the digital era, the emergence of quick response (QR) code technology has become a vital tool for enhancing the efficiency of electronic invoice management and promoting security and transparency in financial transactions, while reducing costs and ensuring compliance with regulations. This study focuses on QR code technology and electronic invoice requirements in the Kingdom of Saudi Arabia, by exploring the generation of QR codes for electronic invoices. The study begins by analyzing QR code technology and its role in encoding and decoding information. Subsequently, the electronic invoice requirements in Saudi Arabia are reviewed, with a focus on the applicable systems and regulations. The research also includes details on generating QR codes for electronic invoices, considering factors such as data encoding, security protocols, and compatibility standards using the Python programming language. Various steps of this process are explained. The study aims to provide a comprehensive understanding of the technology and requirements related to electronic invoices in Saudi Arabia and to develop a program for creating QR codes for electronic invoices to improve and develop the financial and technological infrastructure in the Kingdom of Saudi Arabia, thereby contributing to supporting the digital economy and promoting sustainable development.
IoT based intrusion detection data analysis using deep learning models Baich, Marwa; Sael, Nawal; Hamim, Touria
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1804-1818

Abstract

In both the academic and industrial domains, integration of the internet of things (IoT) is now universally accepted as a significant technical achievement. IoT offers a multitude of security issues despite its many advantages, such as protecting networks and devices, handling resourceconstrained network scenarios, and controlling threats to IoT networks. This article gives a state-of-the-art analysis on the application of multiple deep learning (DL) algorithms in IoT intrusion detection systems (IDS), covering the years 2020 to 2024. Moreover, two popular network datasets, NSL-KDD and UNSW-NB15, are used for an experimental evaluation. The study thoroughly examines and assesses the advantages of well-known deep learning algorithms, including DNN, CNN, RNN, LSTM, and FFDNN. The study demonstrates the exceptional performance of the DNN approach on both datasets, with 99.14% accuracy in multiclass classification in NSLKDD and 99.36% accuracy in binary classification. Furthermore, on UNSWNB15, 82.26% of multiclass classifications and 93.96% of binary classifications with a 42-second minimum running time were achieved, along with an excellent performance in reducing false alarms at a rate of 2.19%.
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.
Plagiarism detection using text-representing centroids techniques Nualnim, Sureeporn; Maliyaem, Maleerat; Unger, Herwig
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1722-1734

Abstract

This study addresses the limitations of traditional plagiarism detection methods by introducing the text-representing centroid (TRC) technique. TRC is designed to improve the accuracy of detecting semantic similarities and sophisticated forms of plagiarism. It utilizes a co-occurrence graph to identify centroid terms that represent the core meaning of text documents, effectively capturing the contextual associations between terms. Extensive experiments were conducted on a dataset of academic papers to assess TRC’s performance against traditional techniques across various categories of plagiarism, including near-copy, modified-copy, and paraphrasing. The results demonstrate the effectiveness of the TRC technique, achieving an average precision of 0.96 and a recall of 0.71. This performance surpasses methods such as Jaccard and Cosine similarity in accurately detecting more, complex forms of plagiarism. These findings highlight TRC’s potential as a robust tool for both academic and industry applications, helping to ensure integrity in textual content through precise and comprehensive plagiarism detection.
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.
Brain tumor classification for optimizing performance using hybrid RNN classifier Gari Kalavathi, Boya Nethappa; Ramamoorthy, Umadevi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1905-1913

Abstract

Tumor is the uncontrolled growth of cancer cells in any part of the human body. Brain tumoris the leading cause of cancer deaths worldwide among adults and childrens. Early detection of brain cancers is essential. To prevent more issues, early defect detection is essential. Healthcare physicians may discover and categorize brain tumors with the use of computational intelligence-focused tools. An essential task for diagnosing tumors and choosing the right type of therapy is classifying brain tumors. Brain tumor identification and segmentation using magnetic resonance imaging (MRI) scans is now recognized as one of the most significant and difficult research areas in the world of medical image processing. The field of medical imaging has gained greatly from the use of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL). DL has shown significant presentation, especially in the areas of brain tumor classification and segmentation. In this work, brain tumor classification for optimizing performance using hybrid recurrent neural network (RNN) classifier is presented. Different types of brain tumors are classified using a mix of RNN and inception residual neural network (ResNet). This strategy will produce improved F1-score, precision, accuracy, and recall scores.
Machine learning based strategies for managing employee retention: determining factors in hospitality industry Kaja Mytheen, Basari Kodi; Jeyakumar, Murugachandravel; Ramasamy, Kannan; Mani, Geetha; Jayamurugan, Prabhu; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1652-1660

Abstract

In order to boost performance and remain competitive, the Indian hospitality industry must recruit and retain employees if it wants to succeed in the long run. In order to do this, it will need to use a number of staff retention initiatives. It is suggested that effective employee retention tactics be analyzed using machine learning (ML) approaches for prediction. The results show that the hotel industry uses tactics to keep its employees, such as competitive compensation and benefits, opportunities for growth and recognition, safe and healthy workplaces, adaptable schedules, employment stability, and ongoing education and development. There is a noticeable disparity between the hotel industry’s demographics and retention tactics. In the hotel industry, there is a modestly negative correlation between employee desire to depart and employee retention methods. Pay and benefits, recognition and gratitude, a safe and healthy workplace, opportunities for professional growth, and development all play a role in how satisfied hospitality workers are with their jobs. The hotel sector has to implement strong welfare initiatives if it wants its workers to have a healthy work-life balance. The hotel business should promote the development of professional connections among its employees.

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