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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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Unveiling critical factors of test automation adoption in software testing Al Fath, Miftahul Kahfi; Oktavia, Tanty
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1826-1836

Abstract

This paper aims to observe the adoption of test automation in Indonesia and examine the determining factors that influence the use of this technology in organizations. The study focuses on five critical factors: technology acceptance model, task-technology fit, managerial support (MS), individual performance, and organizational performance. A survey of 109 QA community members was conducted to collect data, and partial least squares structural equation modeling was used for data processing. Based on the study, Selenium is the top test automation framework used for organizations in Indonesia, followed by Appium and Postman. The result showed that out of twelve (12) examined relationships, nine (9) of them were accepted. This data indicates the strong influence of task technology fit (TTF), computer self-efficacy (CSE), perceived ease of use, perceived usefulness, and MS towards behavioral intention and actual use of test automation. Additionally, the actual use of test automation was found to have a positive impact on individual and organizational performance. The study contributes valuable insights for decision-makers by identifying critical factors influencing automation adoption and offers a replicable methodology for evaluating similar technologies.
Empirical analysis of Bitcoin investment strategy: a comparison of machine learning and deep learning approach Tripathy, Nrusingha; Manchala, Yugandhar; Ghosh, Rajesh Kumar; Dash, Biswajit; Rout, Archana; Swain, Nirmal Keshari; Nayak, Subrat Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1745-1754

Abstract

A digital currency known as a cryptocurrency uses blockchain technology to record transactions electronically, guaranteeing security and transparency. Cryptocurrencies, in contrast to conventional hard currency, are virtual or soft currencies; that do not exist in the actual world like coins or banknotes. Since all transactions occur digitally, cryptocurrencies are decentralized and frequently stand-alone from conventional financial institutions. Peer-to-peer transfers, increased anonymity, and often quicker transaction processing without middlemen are made possible by this. In this study, two machine learning models; autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and two deep learning models; long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM) were compared. By employing past Bitcoin data from 2012 to 2020, we evaluated the models' mean absolute error (MAE) and root mean squared error (RMSE). Compared to other models, the Bi-LSTM model yields minimal RMSE scores of 67.18 and MAE scores of 24.73. This aids in capturing all temporal correlations, which are important for forecasting the price of Bitcoin.
Graphene-based reconfigurable FSS for dynamic millimeterwave OAM beam generation Qasem, Nidal
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1608-1621

Abstract

This research paper explores the dynamic generation of orbital angular momentum (OAM) beams at millimeter-wave (mm-wave) frequencies using intelligent reconfigurable metasurfaces (IRM). The ability to dynamically control OAM properties is crucial for unlocking these beams’ full potential. This paper proposes a novel method utilizing a frequency-selective surface (FSS) integrated with reconfigurable graphene to generate an IRM. By carefully designing the FSS elements and controlling the graphene’s electrical conductivity, the system can generate and manipulate mm-wave OAM beams with different topological charges. With the suggested IRM structure, a conversion efficiency of nearly 80% can be achieved in converting the circularly polarized incident wave into its cross-polarized component at 30.7 GHz, with an overall thickness of 0.067 λ. This research has significant implications for advancing mm-wave communications by providing additional spatial dimensions for multiplexing and enhancing system capacity.
Simulation of reactive flow over a parabolic vertical plate using MATLAB Pushparaj, Sivakumar; Ramalingam, Balaji; Adhimoolam, Ramesh; Mohan Reddy, P. Venkata; Srinivasan, Andal; Rajamanickam, Muthucumaraswamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1673-1682

Abstract

This article examines how fluid flows around an infinitely large, parabolic-shaped vertical plate, which is heated at an exponentially accelerating rate and undergoes a chemical reaction with the fluid. The plate’s temperature increases at an exponential rate, adding complexity to the heat transfer process. Additionally, the fluid undergoes a chemical reaction in this environment, impacting both the flow and concentration of chemical species. The article includes graphs that show how different parameters such as the rate of temperature increase, strength of thermal radiation, and reaction rate, effect the flow, heat, and concentration profiles. This graphical analysis provides a visual understanding of how each parameter influences the behavior of the fluid.
Pairing mobile users using K-means algorithm on PD-NOMA-based mmWaves communications system Abdelkhaliq, Litim; Yassine, Bendimerad Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1595-1607

Abstract

In this research, we study the effectiveness of the K-means machine learning (ML) clustering approach for pairing mobile users on a power domain nonorthogonal multiple access (PD-NOMA) single input single output (SISO) downlink-based millimeter-wave (mmWave) communication system. The basic concept is to pair the mobile users by using a data set that contains essential information about the mobile users in the micro cell base station (BS) (e.g., the SNR, the distance between the mobile users and the BS, the channel gain, and the data rate of each mobile user). The study conducted in this paper demonstrates that the proposed K-means clustering-based scheme achieves a balance between computational complexity and performance metrics. It outperforms single carrier NOMA (SC-NOMA), the conventional NOMA pairing scheme, and time division multiple access (TDMA), offering an effective trade-off between system efficiency and implementation feasibility.
An innovative approach to Raga pattern identification Chakrabarty, Sudipta; Rai, Prativa; Islam, Md Ruhul; Deva Sarma, Hiren Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1865-1876

Abstract

Raga is a fundamental element of Indian classical music (ICM), crucial for identifying the unique characteristics of a given song. Recognizing the embedded Raga allows for various applications, including music therapy, and leveraging the therapeutic effects of different Ragas. The use of mathematical techniques such as fast fourier transform (FFT) and fundamental frequency measurement (FFM) in calculating note values has proven effective for Raga pattern recognition. Both methods yield nearly identical results, facilitating accurate identification of Ragas. Once identified, these Ragas can be used for specific therapeutic purposes, harnessing their healing potential.
Prediction of broiler shear force using near infrared spectroscopy with second derivative linear modeling Ghazali, Rashidah; Rahim, Herlina Abdul; Zulkifli, Syahidah Nurani
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1787-1794

Abstract

This study explores the use of linear predictive models, specifically principal component regression (PCR) and partial least squares (PLS), in combination with a cost-effective near infrared spectroscopy (NIRS) system to noninvasively assess the texture of raw broiler meat. The findings demonstrate that appropriate pre-processing techniques, such as excluding the visible spectrum and applying the second-order Savitzky-Golay (SG) derivative with an optimal filter length (FL), enhance model performance. Notably, the PLS model outperformed PCR, requiring fewer latent variables (LVs) to achieve accurate predictions. This suggests that PLS more effectively captures key spectral features associated with meat texture, making it a promising approach for assessing raw broiler meat quality in a practical, cost-efficient, and non-invasive manner. These results highlight the potential of integrating linear predictive models with NIRS technology for reliable texture analysis in the poultry industry.
Sentiment analysis resource of Libyan dialect for Libyan Airlines Ebrahem, Hassan Ali; Touati, Imen; Belguith, Lamia
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2001-2011

Abstract

Arabic lacks extensive corpora for natural language processing (NLP) when compared to other languages, namely in the Libyan dialect (LD). Therefore, this study proposes the first corpus of Arabic sentiment analysis (ASA) of the Libyan Dialect for the Airline Industry (ASALDA). It comprises 9,350 comments and tweets, annotating them manually depending on text polarity into three labels: positive, negative, and neutral, and utilized aspect-based sentiment analysis (SA) to annotate opinions regarding fifteen aspects. Also constructs a simple sentiment lexicon of the LD. The solution is based on the idea that the corpus and lexicon can be helpful models to improve classification for the LD. The approach has notable merits, namely creating a corpus and sentiment lexicon for the LD from comments and tweets of airline companies. A comprehensive verification using a statistical technique called the chi-square test is carried out with the corpus to determine if two aspects are related to one another. Based on the statistical work, we found that airlines should focus on improving their services in aspects where they are performing poorly, such as late flights, customer service, or price. The corpus and lexicon that we proposed can be utilized to perform many opinion mining and SA experimentations using machine learning and deep learning.
Machine learning approach for cost estimation in software project planning Jaiswal, Ajay; Raikwal, Jagdish
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1724-1735

Abstract

Successful organizing and handling of software projects depends extensively on accurate cost estimation. This study explores the effectiveness of machine learning models in estimating software project costs using datasets like Desharnais, Maxwell, and Kitchenham, aiming to prevent project delays and resource misallocation. It shows how model selection has a major impact on forecast accuracy through thorough assessment. An R-squared value (R2) of 0.804 indicates that the support vector machine (SVM) model performs exceptionally well in the Desharnais dataset. On the Maxwell dataset, linear regression (LR) stands out with a minimum mean absolute error (MAE) of 0.483 and the greatest R2 value of 0.607, while SVM has the lowest root mean squared error (RMSE) of 0.537. Similarly, on the Kitchenham dataset, LR and SVM are the top performers, with MAE of 0.201 and RMSE of 0.274, respectively, and R2 values of around 0.929. These findings highlight the importance of tailored model selection for accurate cost prediction, as LR and SVM continuously demonstrate reliability across varied datasets. ML techniques like LR and SVM can enhance software project planning and management by providing accurate cost estimation, with future research exploring ensemble learning and deep learning methodologies.
Energy-efficient and reliable data transmission to enhance the performance of wireless sensor networks using artificial intelligence Ch, Swapna; Budyal, Vijayashree R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1946-1954

Abstract

For many years, the area of wireless sensor networks (WSN) has been popular for its wide range of time-critical and potential applications. However, it has many challenges that require more attention from the research communities to improve the network’s operational efficiency. However, with consistently rising concerns for energy efficiency and optimized data transmission performance, most current research emphasises minimum power consumption and reliable data transmission aspects. The critical analysis and study of related works exhibit the shortcomings in existing data transmission schemes, which fail to cope with the dynamic conditions of WSNs on a larger scale and do not retain considerable energy performance. The study thereby introduces a unique approach to an energy-efficient and reliable data transmission framework that formulates machine learning-driven functional components to ensure effective data gathering, aggregation, and routing and dissemination strategies to properly balance energy and data transmission performance in WSN under dynamic conditions. The proposed framework's performance evaluation considers multiple metrics, such as analysis of network lifetime, Energy Consumption, Throughput, and Latency performance. The experimental outcome shows that the proposed system outperforms the existing baselines for the above performance metrics.

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