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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Enhancing El Niño-Southern oscillation prediction using an attention-based sequence-to-sequence architecture Setiawan, Karli Eka; Fredyan, Renaldy; Alam, Islam Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7057-7066

Abstract

The ability to accurately predict the EI Nino-Southern oscillation (ENSO) is essential for seasonal climate forecasting. Monitoring the Pacific Ocean's surface temperature has many benefits for human life, including a better understanding of climate and weather, the ability to predict summer and winter, the ability to manage natural resources, serving as a reference for maritime transportation and navigation needs, serving as a reference for climate change monitoring needs, and even serving as a renewable energy source by utilizing high sea surface temperatures. This study introduces a deep learning (DL) model with AttentionSeq2Luong model as our proposed model to the ENSO research community. The present study showcases the capability of our proposed model to effectively forecast the forthcoming monthly average Nino index compared to the baseline seq2seq architecture model. For the dataset, this study utilized monthly observations of Nino 12, Nino 3, Nino 34, and Nino 4 between January 1870 and August 2022. The brief result of our experiment was that applying Luong Attention in the seq2seq model reduced the RMSE error by around 0.03494, 0.04635, 0.03853, and 0.03892 for forecasting Nino 12, Nino 3, Nino 34, and Nino 4, respectively.
Transient response mitigation using type-2 fuzzy controller optimized by grey wolf optimizer in converter high voltage direct current Ginarsa, I Made; Nrartha, I Made Ari; Muljono, Agung Budi; Zebua, Osea
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1274-1286

Abstract

Long high voltage direct current (HVDC) transmission link is commonly used to transmit electrical energy via land or under-sea cable. The long HVDC avoids reactive power losses (RPL) and power stability problems (PSP). On the contrary, the RPL and PSP phenomena occur in long high voltage alternative current-link (HVAC) caused by the high reactive component in the HVAC-link. However, the HVDC produces a high and slow transient current response (TCR) on the high value of the up-ramp rate. Interval type-2 fuzzy (IT2F) control on converter-side HVDC is proposed to mitigate this TCR problem. The IT2F is optimized by grey wolf optimizer (GWO) to adjust input-output IT2F parameters optimally. The performance of IT2F-GWO is assessed by the minimum value of integral time squared error (ITSE), peak overshoot, and settling time of the TCR. The IT2FC-GWO performance is validated by the performance of IT2F control that is optimized by genetic algorithm (IT2F-GA) and proportional integral (PI) controller. Simulation results show that the IT2F-GWO performs better with small ITSE, low peak overshoot, and shorter settling times than competing controllers.
Optimizing heart disease prediction through ensemble and hybrid machine learning techniques Reddy, Nomula Nagarjuna; Nipun, Lingadally; Baba, MD Uzair; Rishindra, Nyalakanti; Shilpa, Thoutireddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5744-5754

Abstract

In this modern era, heart diseases have surfaced as the leading factor of fatalities, accounting for around 17.9 million lives annually. Global deaths from heart diseases have surged by 60% over the last 30 years, primarily because of limited human and logistical resources. Early detection is crucial for effective management through counseling and medication. Earlier studies have identified key elements for heart disease diagnosis, including genetic predispositions and lifestyle factors such as age, gender, smoking habits, stress, diastolic blood pressure, troponin levels, and electrocardiogram (ECG). This project aims to develop a model that can identify the best machine learning (ML) algorithm for predicting heart diseases with high accuracy, precision, and the least misclassification. Various ML techniques were evaluated using selected features from the heart disease dataset. Among these techniques, a combination of random forest (RF), multi-layer perceptron (MLP), XGBoost, and LightGBM employing an ensemble method with a stacking classifier, along with logistic regression (LR) as a metamodel, achieved the highest accuracy rate of 95.8%. This surpasses the efficiency of other techniques. The suggested method provides an encouraging framework for early prediction, with the overarching goal of reducing global mortality rates associated with these conditions.
Graph embedding approach to analyze sentiments on cryptocurrency Moudhich, Ihab; Fennan, Abdelhadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp690-697

Abstract

This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.
Modeling and simulation for flashover location determination on 150 kV insulator string Sitti Amalia; Sitti Amalia; Warmi, Yusreni; Amalia, Sitti; Zulkarnaini, Zulkarnaini; Dasman, Dasman; Bachtiar, Antonov; Anthony, Zuriman; Azhar, Hamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3716-3728

Abstract

The 150 kV Payakumbuh-Koto Panjang transmission line in West Sumatra is located in an area with high lightning activity. Based on Meteorological, Climatological, and Geophysical Agency (BMKG) data (2017-2023), the average number of lightning days per year (IKL: isokeraunic level) reaches 165-173 days/year, and 79% of the transmission towers are located in hilly and rocky areas. This causes contamination of the insulator, which can reduce its performance and cause flashovers in the insulator circuit. However, in the field, finding flash points in insulators is still a challenge. Therefore, simulation must be used as a tool to determine the location of flashover in an insulator circuit that is affected by temperature and humidity. Simulation by modeling flashover provides an effective solution for determining the location of flashover in insulator circuits, which is the novelty of this research. This research compares laboratory test results with manual calculations modeled using Visual Basic 6. The research results show that temperature and humidity have a significant influence on determining the flashover voltage value on the insulator. The flashover locations during the test are the same as the calculated flashover locations, as shown by these simulations and modeling.
Negation handling for sentiment analysis task: approaches and performance analysis Ilmawan, Lutfi Budi; Muladi, Muladi; Prasetya, Didik Dwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3382-3393

Abstract

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, end-to-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures.
Moderating roles of user’s intention to use LINE official account in healthcare context: body mass index Trakulmaykee, Numtip; Choksuchat, Chidchanok; Jetwanna, Korakot Wichitsa-nguan; Inthanuchit, Kochakorn Sukjan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6807-6816

Abstract

This study aimed to investigate the extended factors based on the technology acceptance model, and the moderating roles of customer behavioral intention factors to use information technology. This research is a questionnaire-based survey with convenience sampling approach where 386 cases were collected from healthcare customers. For statistical analysis, the study used SmartPLS as a tool for regression analysis and descriptive statistics. The findings revealed the influence of social norm on customer behavioral intention to use information technology in the healthcare context as significant factors at 0.001. In addition, the results indicated the small effect of two moderating variables in the proposed model. First, the problematic body mass index (BMI) can be a moderator on the relationship between social norm and customer behavioral intention to use technology at a significant level of 0.05. Second, the technology experience can moderate the relationship between perceived ease of use and customer behavioral intention to use technology at a significant level of 0.05. The proposed model may guide for future exploration, especially information services in healthcare businesses and developers.
Image enhancement in palmprint recognition: a novel approach for improved biometric authentication Kusban, Muhammad; Budiman, Aris; Hari Purwoto, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1299-1307

Abstract

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.
Wind-powered water pumping system for corn plantations under the food estate program on Sumba Island, Indonesia Aziz, Amiral; Rostyono, Didik; Zaky, Toha; Hesty, Nurry Widya; Ifanda, Ifanda; Fauziah, Khotimatul; Prasetyo, Ridwan Budi; Wijayanto, Rudi Purwo; Witjakso, Ario; Syawitri, Taurista Perdana; Mayasari, Agustina Putri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4940-4955

Abstract

The Food and Agriculture Organization (FAO) released a communiqué in March 2020 cautioning about the possibility of a worldwide food emergency due to coronavirus disease (COVID-19). As a response to the food shortages brought on by the COVID-19 outbreak, the authorities of Indonesia initiated a nationwide program aimed at improving the country's food supply known as the food estate (FE), which was later incorporated into national strategic programs. The climate and availability of surface water sources in this region make establishing an FE area in the Central Sumba Regency difficult. Sumba, on the other hand, possesses wind energy resources that can be transformed into electrical energy and used to pump underground water for agricultural purposes. A wind-powered water pump (WPW) is being developed in this study to provide water for maize plantations in the FE region in Central Sumba District, Indonesia. The study on the levelized cost of energy (LCOE) for water pumping indicates that the wind-powered system is more economically viable than the diesel-powered alternative. The LCOE for a WPW pumping system is 6,994 IDR/kWh, whereas the LCOE for a diesel-powered system is 16,667 IDR/kWh. The overall net present value of WPW and diesel-powered systems is 708,667,200 IDR and 2,158,349,000 IDR, respectively. This study contributes significantly to informed decision-making for enhancing the performance viability of the wind water pumping system for the food estate program in Indonesia.
Enhancing healthcare services through cloud service: a systematic review Guo, Bo; Shukor, Nur Syufiza Ahmad; Ishak, Irny Suzila
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1135-1146

Abstract

Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.

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