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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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Energy and cost-aware workload scheduler for heterogeneous cloud platform Shivanandappa, Manjunatha; Chowdaiah, Naveen Kumar; Devaraje Gowda, Swetha Mysore; Shivaswamy, Rashmi; Ramasamy, Vadivel; Prabhu Vijay, Subramani Suryakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp546-554

Abstract

Parallel scientific workloads, often represented as directed acyclic graphs (DAGs), consist of interdependent tasks that require significant data exchange and are executed on distributed clusters. The communication overhead between tasks running on different nodes can lead to substantial increases in makespan, energy usage, and monetary costs. Therefore, there is potential to balance communication and computation to reduce these costs. In this paper, we introduce an energy and cost-aware workload scheduler (ECAWS) tailored for executing parallel scientific workloads, generated by the internet of things (IoT), in a heterogeneous cloud environment. The performance of the proposed ECAWS model is evaluated against existing models using the Inspiral scientific workload. Results indicate that ECAWS outperforms other models in reducing makespan, costs, and energy consumption.
Leveraging 3D convolutional networks for effective video feature extraction in video summarization Kadam, Bhakti Deepak; Deshpande, Ashwini Mangesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 3: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i3.pp1616-1625

Abstract

Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation.
Computer simulation of transition modes in flow reactors considering the multistage and reactions non-perfectness Kalbayeva, Aizhan Tazhikhanovna; Umarova, Zhanat Rysbayevna; Kurakbayeva, Sevara Dzhumagaliyevna; Musabekova, Leyla Mukhamedjanovna; Amandikov, Madamin A.; Arystanbayev, Kuttybek E.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp486-495

Abstract

Due to the variety of reaction types and schemes in chemical-technological apparatuses, a general engineering methodology to assessing how the transient modes and reactions multi-stage act the kinetics in conditions of occurrence of moving reaction fronts in flow apparatuses has not yet been developed. The paper devotes to constructing the mathematical models for several important cases of the problem mentioned, namely: for theoretically study the kinetic dynamics of the conversion process in a three-stage chemical reaction with an autocatalytic first stage and the presence of a mass source of one of the components. An original mathematical model for describing the chemisorption dynamics at the initial stage of forming a moving reaction front in flow-through apparatuses has been developed. A special algorithm and numerical solution for the initial absorption period have been constructed, and appropriate computer simulation has been implemented. The significant influence of multistage on the formation and on stability types of stationary states has been established. Expressions to evaluate the characteristics of the emerging oscillatory modes have been obtained too. The results can be used to assess the influence of control parameters on the reaction front movement speed.
Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application Asri, Yessy; Kuswardani, Dwina; Suliyanti, Widya Nita; Manullang, Yosef Owen; Ansyari, Atikah Rifdah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp677-688

Abstract

PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the application. In response, a study was conducted to analyze the sentiment of user reviews from January to June 2022, by collecting 1,000 review samples from the Google Play Store. The data was collected using web scraping techniques and then processed into a dataset through text pre-processing methods. Sentiments were analyzed using an automatic labeling method in Indonesian based on a lexicon known as INSET (Indonesia sentiment), which resulted in 482 positive reviews, 144 negative reviews, and 374 neutral reviews. The next step is classification using Indonesian bidirectional encoder representations from transformers (IndoBERT). In this process, the data was divided into testing, training, and validation sets with a ratio of 80:10:10. The analysis managed to achieve an impressive accuracy rate of 81%.
Photoplethysmograph-based time-frequency and machine learning applications on biomedical signal analysis for medical diagnosis Jana, Soumyadip; Pal, Partha Sarathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp145-160

Abstract

Machine learning (ML) integration in biomedical signal processing and medical diagnosis has the potential to revolutionize healthcare by improving diagnostic accuracy. This paper focuses on the applications of different ML algorithms for analyzing real-time physiological data collected from Photoplethysmography (PPG) sensors. Heart rate variability (HRV) analysis using electrocardiography (ECG) signals makes the process longer and bulky. Therefore, this paper demonstrates the real-time generation of HRV signals using a simple, low-cost, and non-invasive PPG sensor which is further processed using the Arduino ATMEGA328P microcontroller and then interfaced to a PC for display to investigate the usefulness of HRV feature analysis. HRV features have been computed using time domain analysis (TA), and frequency domain analysis (FA). At last, these TA and FA indices have been given to different ML models that could predict the gender, age group, and physiological conditions of a human being. Prediction of the physiological conditions using TA, FA, and ML models simultaneously makes the proposed approach more novel than the other existing methods. Comparative analysis of different ML approaches using ROC curves and confusion matrices has been shown to find the effectiveness and precision of different proposed models. It shows random forest ML approach has achieved 91% accuracy in identifying the physiological conditions. This simple yet accurate real-time PPG-based time-frequency ML system might be useful in medical assessment with faster response.
Study on postal life insurance attributes and its growth prediction using machine learning algorithms Rajasekaran, Thangavelu Ananadaraj; Vijayalakshmi, Pichamuthu; Rajendran, Velayutham
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp622-631

Abstract

The oldest insurer in the country, since 1884, is Postal Insurance. For today's livelihood, the citizens of India's life-saving coverage and insurance have become necessary. For customers to overcome difficult situations, life insurance is crucial in creating confidence. This is one of the highlights of the Postal organization. Under postal life insurance (PLI), the volume of new policies is enrolled throughout India, and a supervised machine learning (ML) process for finding the business cluster is carried out based on this data, which is discussed. A ML algorithm that predicts the growth for the future, using a suitable algorithm for accessing the features and process to identify the prediction model, has been developed, which is the main goal of this study. Simulation results show that expected is one of the most important variables used to predict and that both random forest (RF) and logistic regression outperformed the other two models. The RF model is the most effective and fastest in predicting the system's future state, and it shows the highest value for the PLI product.
Performance enhancement of a terahertz patch antenna with metamaterials for 6G and biomedical applications Younes, Siraj; Alaoui, Kaoutar Saidi; Foshi, Jaouad
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp193-203

Abstract

This paper introduces a novel approach for enhancing the performance of a terahertz (THz) patch antenna through the integration of metamaterials (MTM). The proposed design features a rectangular slotted patch antenna with a partial ground structure (DGS) that operates at 3.56 THz. The radiating element is situated on a substrate composed of silicon dioxide (SiO2) with a dielectric of 4 and a thickness of 2 µm. The proposed MTM is a 6×5 elements with a FR4 substrate characterized by a dielectric of 4.2 and a thickness of 2 µm. The MTM is integrated beneath the antenna as a strategic technique to enhance its performance. The results confirm the significant impact of this integration. The MTM improves impedance matching and makes the antenna more directional. Consequently, the reflection coefficient is improved from -18.06 dB to -52.50 dB, the gain is increased from 1.72 dB to 3.49 dB, and the directivity also is enhanced from 3.69 dB to 5.10 dB. All results were obtained using HFSS software.
HCRF: an improved random forest algorithm based on hierarchical clustering Zhuo, Wang; Ahmad, Azlin
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp578-586

Abstract

Random forest (RF) selects feature subsets randomly. Useless and redundant features will lower the quality of the selected features and subsequently affect the overall classification accuracy of the RF. This study proposes an improved RF algorithm based on hierarchical clustering (HCRF). The algorithm uses hierarchical clustering algorithms to optimize the feature selection process, by establishing similar feature groups based on the GINI index, and then selecting features from each group proportionally to construct the feature subset. The feature subset is then used to construct a single classifier. This process increases the filtering of feature subsets, reducing the negative impact of useless and redundant features on the model, and improving the model's generalization ability and overall performance. In the experimental verification, ten datasets of different sizes and domains were selected, and the accuracy, precision, recall, F1 score, and running time of HCRF, support vector machine (SVM), RF, classification and regression tree (CART) were compared using 10-fold cross-validation. Combining all the results, the HCRF algorithm showed significant improvements in all evaluation indicators, proving that its performance is superior to the other three classifiers. Therefore, this algorithm has broad application areas and value, and effectively improves the overall performance of the classifier within a lower complexity range.
An improved WOA of PI control for three phase PWM rectifier G, Shwetha; K P, Guruswamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp39-49

Abstract

In the empire of electric vehicle (EV) propulsion systems, efficient energy conversion is paramount for extending driving range and enhancing overall performance. Rectifiers play a crucial role in converting AC from the grid into DC for battery charging and motor operation. However, the performance of rectifiers is heavily influenced by the control algorithms employed. This work presents an optimized proportional-integral (PI) controller design for rectifiers in EV applications. The proposed controller aims to achieve high efficiency, fast response, and robustness to variations in load and input voltage. The optimization process incorporated in this work utilized whale optimization topology for tuning the PI controller parameters. The objective is to minimize cost function that represents deviation of rectifier output from desired characteristics under various operating conditions. The outcomes of simulation demonstrate that suggested controller works to provide greater accuracy than traditional control techniques. Moreover, experimental validation verifies the proposed controller's reliability and efficiency in practical EV applications. The optimized PI controller contributes to maximizing energy efficiency, extending battery life, and enhancing the overall reliability of electric vehicle propulsion systems.
Advanced tourist arrival forecasting: a synergistic approach using LSTM, Hilbert-Huang transform, and random forest Mukhtar, Harun; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Wan Salihin Wong, Khairul Nizar Syazwan; Ridhollah, Farhan; Deprizon, Deprizon; Soni, Soni; Lisman, Muhammad; Amran, Hasanatul Fu'adah; Sunanto, Sunanto; Ismanto, Edi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp517-526

Abstract

An advanced synergistic approach for forecasting tourist arrivals is presented, integrating long short-term memory (LSTM), Hilbert-Huang transform (HHT), and random forest (RF). LSTM is leveraged for its capability to capture long-term dependencies in sequential data. Additional data from Google Trends (GT) is processed with HHT for feature extraction, followed by feature selection using the RF algorithm. The combined HHT-RF-LSTM model delivers highly accurate forecasts. Evaluation employs regression analysis with metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE), highlighting the effectiveness of this innovative approach in predicting tourist arrivals. This methodology provides a robust framework for handling limited datasets and improving forecast reliability. By incorporating diverse data sources and advanced preprocessing techniques, the model enhances prediction performance, demonstrating the strong performance of RF in feature selection.

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