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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
 843   844   845   846   847 
Forecasting research influence: a recurrent neural network approach to citation prediction Jamal, Naser; Alauthman, Mohammad; Malhis, Muhannad; Ishtaiwi, Abdelraouf M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1070-1082

Abstract

As the volume of scientific publications continues to proliferate, effective evaluation tools to determine the impact and quality of research articles are increasingly necessary. Citations serve as a widely utilized metric for gauging scientific impact. However, accurately prognosticating the long-term citation impact of nascent published research presents a formidable challenge due to the intricacy and unpredictability innate to the scientific ecosystem. Sophisticated machine learning methodologies, particularly recurrent neural networks (RNNs), have recently demonstrated promising potential in addressing this task. This research proposes an RNN architecture leveraging encoder-decoder sequence modeling capabilities to ingest historical chronicles and predict succeeding evolution via latent temporal dynamics learning. Comparative analysis between the RNN approach and baselines, including random forest, support vector regression, and multi-layer perceptron, demonstrate superior performance on unseen test data and rigorous k-fold cross-validation. On a corpus from Petra University, the RNN methodology attained the lowest errors (root mean squared error (RMSE) 1.84) and highest accuracy (0.91), area under the curve (AUC) (0.96), and F1-score (0.92). Statistical tests further verify significant improvements. The findings validate our deep learning solution's efficacy, robustness, and real-world viability for long-term scientific impact quantification to aid stakeholders in research evaluation. The findings intimate that RNN-based predictive modeling constitutes a potent technology for citation-driven scientific impact quantification.
Pneumonia stage analyzes through image processing Chowdhury, Nishu; Choudhury, Pranto Protim; Moon, Shatabdi Roy
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1778-1786

Abstract

A physical examination and diagnostic imaging techniques including lung biopsies, ultrasounds, and chest X-rays are typically used to make the diagnosis of pneumonia infection, an infectious disease that has the potential to be life-threatening. The objective of this research is to categorize the stages of pneumonia through image processing methods. Before that, an ensemble model for diagnosing pneumonia infections is created utilizing the transfer learning algorithms ResNet50V2 and DenseNet201. The 5,857 images were taken from the PAUL MOONEY dataset for this research. The proposed ensemble averaging model recognizes lung infection appropriately and accurately. By applying a contour detection approach, the left and right chests are separated and the affected pixels from there to analyze the stage of pneumonia. It is very crucial to identify the stage for treatment purposes.
Investigation of digital rupiah acceptance using UTAUT-3 model Mau Bere, Alejandro Billyjoe; Putra, Richard Win; Wedari, Linda Kusumaning
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1710-1721

Abstract

The adoption of central bank digital currencies (CBDC) has been popular in many countries, especially Indonesia which currently develops its own CBDC called digital rupiah, due to its potential benefits such as financial inclusion. Despite the potential benefits of digital rupiah, there is a lack of understanding regarding factors that affect digital rupiah user acceptance. This research aims to investigate the potential factor affecting the user acceptance of digital rupiah using the unified theory of acceptance and use of technology (UTAUT-3) model, incorporating awareness and privacy as additional variables. There are 218 respondents to this study from five provinces in Indonesia: Jakarta, West Java, East Java, Central Java, and Yogyakarta. The data were analyzed using the SEM-PLS method. The results of this study found that performance expectancy, effort expectancy, habit, personal innovativeness, and awareness are the significant factors that affect the behavioral intention of digital rupiah meanwhile facilitating condition, habit, personal innovativeness, and behavioral intention are the factors that significantly affect the use behavior of digital rupiah. This study identifies key factors influencing the user acceptance of the digital rupiah, providing valuable insights for stakeholders seeking to promote its adoption and use in Indonesia.
Electronic document management systems implementation across industries: systematic analysis Anggraini, Dian; Adi, Kusworo; Suseno, Jatmiko Endro
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp264-273

Abstract

The construction sector’s pivotal role in the global economy faces challenges due to its dynamic nature. Inaccurate documentation impacts project cost management, underscoring the need for effective document management systems (DMS), including electronic document management systems (EDMS). This study conducts a systematic literature review to comprehensively examine EDMS implementation, utilization, and effectiveness across sectors. Analyzing peer-reviewed articles and scholarly sources reveals key themes, trends, and findings, providing insights into successful EDMS adoption and best practices. The review contributes evidence-based insights for practitioners, researchers, and policymakers, addressing gaps in knowledge and advancing understanding of EDMS in modern information management. Additionally, it presents a detailed breakdown of publication distribution across sectors, highlighting significant research areas like companies and businesses, education, and information technology and software. Furthermore, analysis of factors influencing employee behavior, including technical factors, employee’s personal characteristics, organizational factors, and trust, offers valuable insights into workplace dynamics. Overall, the study offers comprehensive insights into EDMS implementation, guiding future research, and organizational strategies.
Exploring the potential of DistilBERT architecture for automatic essay scoring task Ikiss, Soumia; Daoudi, Najima; Abourezq, Manar; Bellafkih, Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1234-1241

Abstract

Automatic assessment of writing essays, or the process of using computers to evaluate and assign grades to written text, is very needed in the education system as an alternative to reduce human burden and time consumption, especially for large-scale tests. This task has received more attention in the last few years, being one of the major uses for natural language processing (NLP). Traditional automatic scoring systems typically rely on handcrafted features, whereas recent studies have used deep neural networks. Since the advent of transformers, pre-trained language models have performed well in many downstream tasks. We utilize the Kaggle benchmarking automated student assessment prize dataset to fine-tune the pre-trained DistilBERT in three different scenarios, and we compare results with the existing neural network-based approaches to achieve improved performance in the automatic essay scoring task. We utilize quadratic weighted Kappa (QWK) as the main metric to evaluate the performance of our proposed method. Results show that fine-tuning DistilBERT gives good results, especially with the scenario of training all parameters, which achieve 0.90 of QWK and outperform neural network models.
Cattle weight prediction model using convolutional neural network and artificial neural network Yulianingsih, Yulianingsih; Nurdiati, Sri; Sukoco, Heru; Sumantri, Cece
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp441-449

Abstract

The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved.
Identification and characterisation of earthquake clusters from seismic historical data Markhaba, Karmenova; Aizhan, Tlebaldinova; Karlygash, Alibekkyzy; Zheniskul, Zhantassova; Indira, Karymsakova
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1594-1604

Abstract

New approaches and methods based on machine learning technologies make it possible to identify not only the spread of earthquakes, but also to establish hidden patterns that allow further assessment of any risks associated with their occurrence. In this article, the clustering algorithms of K-means and K-medoids are applied for the analysis of seismic data recorded on the territory of the Republic of Kazakhstan. Using the Elbow and Silhouette methods, the optimal value of K clusters was determined, which was later used in classifying a data set using cluster analysis methods. The results of seismic data classification by clustering algorithms are in line with expectations. However, when measuring the quality of clustering, the accuracy of the model by the K-means method exceeded the accuracy of the K-medoids model, and the scoring value by the K-means method is ahead of the value by the K-medoids method. In addition, the presented results of descriptive statistics allowed to carry out a more in-depth analysis of the characteristics of each cluster.
Sqrt-Loglogish CNN and Markov model for 5G spectrum sensing application Rajanna, Anupama; Kulkarni, Srimannarayana; Prasad, Sarappadi Narasimha
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1480-1490

Abstract

The research presents innovative methods for spectrum sensing in 5G networks, using the Sqrt-Loglogish convolutional neural network (SL-CNN) and hidden orthogonal intuitionistic fuzzy Markov model (HOIFMM). The proposed methods aim to tackle issues related to detecting principal user signals accurately, mitigating interference, and efficiently utilizing the spectrum in wideband spectrum environments due to their diverse and ever changing characteristics. The Sqrt-Loglogish CNN improves spectrum sensing by addressing static threshold dependency and potential overfitting. The HOIFMM offers a complex framework for predicting sparsity levels and primary user patterns. The results highlight the effectiveness of the suggested techniques in differentiating primary user signals from noise and interference, resulting in enhanced interference management tactics and overall network performance. MATLAB simulation is performed and compared the proposed methods performance with existing state-of-the-art methods such as CNN, deep neural network (DNN), long short-term memory (LSTM) and artificial neural networks (ANN). The proposed method has outperformed existing methods in terms of sensitivity, accuracy, and precision. Future endeavors include improving these methods, investigating sophisticated machine learning algorithms, and doing real world validations to guarantee scalability and resilience in various 5G deployment situations. This research advances the spectrum sensing capabilities in 5G networks, potentially improving efficiency, reliability, and quality of service.
Comparative study of wind turbine emulator control using an asynchronous motor: IRFOC and DTC Zekraoui, Hana; Ouchbel, Taoufik; El Hafyani, Mohamed Larbi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp174-187

Abstract

This work is an overview of one of the renewable energy sources, wind power. The high cost of testing wind turbines and analyzing their characteristics in research laboratories prompted us to create the Wind Emulator. In this article, we will proceed with the development of an emulator of a wind turbine conversion chain based on an asynchronous machine. This emulator would be capable of faithfully reproducing the dynamic characteristics of a real wind turbine and of integrating them optimally into a real electrical system so as to be able to study its operation at laboratory level, so our objective is to have an emulator that will provide the characteristics (speed-torque-current) in real time and with realistic conditions. Our development approach is based on the use of two classical control strategies under the MATLAB/Simulink closed-loop environment: direct torque control (DTC) and indirect rotor flux vector control (IRFOC) in dynamic and static regimes. The simulation results presented and discussed in this work enable us to determine the operating limits of our proposed wind emulator, in order to validate the most suitable emulator model. Ultimately, this model will be integrated into an intelligent computing board such as the DSP1104.
Colour sorting ROS-based robot evaluation under different lights and camera angles Saaid, Mohammad Farid; Thamrin, Norashikin M.; Misnan, Mohamad Farid; Mohamad, Roslina; Romli, Nurul A’qilah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1807-1815

Abstract

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.

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