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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 9,174 Documents
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Modified Harris Hawks optimizer for feature selection and support vector machine kernels Hadeel Tariq Ibrahim; Wamidh Jalil Mazher; Enas Mahmood Jassim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp942-953

Abstract

The support vector machine (SVM), one of the most effective learning algorithms, has many real-world applications. The kernel type and its parameters have a significant impact on the SVM algorithm's effectiveness and performance. In machine learning, choosing the feature subset is a crucial step, especially when working with high-dimensional data sets. These crucial criteria were treated independently in the majority of earlier studies. In this research, we suggest a hybrid strategy based on the Harris Hawk optimization (HHO) algorithm. HHO is one of the lately suggested metaheuristic algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. The suggested method optimizes the SVM model parameters while also locating the optimal features subset. We ran the proposed approach HHO-SVM on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. The experimental results demonstrate the supremacy of the proposed HHO-SVM in terms of three performance metrics: feature selection accuracy, runtime, and number of selected features. The suggested method is contrasted with four well-known algorithms for verification: firefly (FF) algorithm, genetic algorithm (GA), grasshopper optimization algorithm (GOA), and particle swarm algorithm (PSO). The implementation of the proposed HHO-SVM approach reveals 99.967% average accuracy.
Enhancement of voltage generation for thermoelectric generator using parabolic pulsed heating Piyapat Panmuang; Chonlatee Photong
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1248-1255

Abstract

This paper presents the results of investigations of the impact of parabolic pulsed heating on output voltage levels of a thermoelectric generator. The experiment was set up and tested under different test conditions. The output voltage levels were investigated. The experimental results showed that applying parabolic pulsed heating of 40, 60, 80 and 100 °C significantly maintained the output voltage levels of thermoelectric generator at about 80-95% which was different from steady heating case which was about 10-30% of maximum voltage level.  Moreover, the parabolic pulse heating technique allows the heatsink to not be heated continuously and then the high temperature could be released out from a heat exchanger outside the heating period. This causes the next heating period to have a temperature difference between both sides of the device, and for that reason, could provide more power and efficiency.
Injected power control for grid-connected converter based on particle swarm optimization Safa Sfoog Oleiwi; Abdulrahim Thiab Humod; Fadhil Abbas Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1199-1211

Abstract

Inductance–capacitance–inductance (LCL) filters are very attractive candidates for renewable energy system applications due to their high efficiency, high harmonic reduction, small bulk, and improved harmonic distortion (THD). These papers take advantage of the capabilities of renewable energy sources and inject them into the network by using an inverter when it enters work at high loads at certain times. Therefore, it is necessary to control power with certain controllers. The proportional-integral controller (PI) is used; conventional methods for tuning the controller parameters cannot give satisfactory performance due to the high instability of the closed-loop system. This paper presents the particle swarm optimization (PSO) method for tuning the controller's parameters to achieve optimum performance associated with sufficient stability margin. The mathematical models for the LCL filter and the frequency response were investigated by using the bode-plot. The proposed approach shows effective results for both power control and harmonic reduction. The proposed PI-PSO controller gives overshoot (1.08%), settling time (0.03 sec), rise time (0.00035 sec) and improved THD from 10.29% to 1.67% with compared to using the trial and error method, which gives (1.035%), (0.015) and (0.003) and THD from 10.23% to 1.575%, respectively.
A systematic literature review of automatic ontology construction Zayanah Zafirah Zulkipli; Ruhaila Maskat; Noor Hasimah Ibrahim Teo
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp878-889

Abstract

In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.
Investigation of electrical properties of developed indigenous natural ester liquid used as alternate to transformer insulation Doddasiddavanahalli Mukundappa Srinivasa; Usha Surendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp609-617

Abstract

The performance of every electrical system depends on the different electrical devices especially transformers. Petroleum-based mineral oil is widely used for insulation and cooling purpose. The disadvantage of mineral oil is its low biodegradability and is a major threat to the ecosystem due to its poor oxidative stability. To remedy the drawbacks, focus on alternative fluids that can replace traditional mineral oil. Alternative liquids such as natural esters are used which do not panic the ecosystem. With the support of additives in natural esters liquids, the productivity of the oil can be increased, paving the path for the green conversion of liquids in high voltage applications. The purpose of this article is to analyze the electrical properties of the newly developed indigenous oil. The inhibited oil was insulating oil to which antioxidants were added such as 2,6-ditertiary-butylparacresol, butylated hydroxyl anisole and tertiary butyl hydro qunine to slow down the oxidation rate and to check the electrical properties. This article discusses the electrical properties of mineral oil, developed indigenous oil with and without antioxidants as per IEC62770 standards. A 1.1 kVA transformer was then designed in a laboratory for load tests and Indigenous oil performance under load was evaluated.
Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation Rahul Chingamtotattil; Rajamma Gopikakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1709-1719

Abstract

Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.
Measuring user emotional responses using Geneva Emotion Wheel towards learning management systems Wan Nooraishya Wan Ahmad; Ahmad Rizal Ahmad Rodzuan; Voon Mei Luan
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp315-327

Abstract

The learning management system (LMS) has been used widely in higher learning institutions for blended learning; this interaction involves user cognition and may induce emotional experience. Therefore, the user has to sustain a positive emotional experience towards using LMS to avoid difficulty in the learning process. However, the user emotions and the design elements that induced the emotions are yet to discover. This study aims to analyse user emotional responses to a learning management design and examine the emotional design features of a learning management system. Two versions of a higher learning institution LMS were used and investigated using Geneva Emotion Wheel (GEW). The findings show that both LMS versions lack-in activate the positive emotions, but prove the LMS designs reduce the activation of negative emotions. Furthermore, the emotional design elements that concern an LMS is explained.
How to determinate water quality using an artificial intelligent model based on grey clustering? Alexi Delgado; Carlos López; Noe Jacinto; Mario Chungas; Laberiano Andrade-Arenas
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp450-459

Abstract

Water quality is an important topic for countries like Peru, where the mining sector is one of the main economic activities, so the study of its impact on water quality is also necessary to have a regular control of benefits and dangers. In this way, to achieve this objective, the chosen methodology was grey clustering, which is based on artificial intelligent theory. Specifically, the central point triangular whitening weight function better known as CTWF, which is an approach from grey clustering, was used. The case study was focused on the Mashcon and Chonta rivers, located in the province of Cajamarca, Peru, these rivers are directly affected by an open pit mine. The study was carried out taking into account thirteen monitoring points taken by National Water Authority (ANA). The results showed that all the points considered were classified as not contaminated, A1 category, this using the parameters of the Peruvian government. With these results, the mining company was able to demonstrate that they are taking the water quality into account and that they are making an effort to keep these rivers as healthy as possible.
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data Nur Hanisah Abdul Malek; Wan Fairos Wan Yaacob; Yap Bee Wah; Syerina Azlin Md Nasir; Norshahida Shaadan; Sapto Wahyu Indratno
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp598-608

Abstract

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.
Development of health monitoring system using smart intelligent device Nagaraj Chinnamadha; Roshan Zameer Ahmed; Kumara Kalegowda
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1381-1387

Abstract

Electronic technology plays a vital role in healthcare, not only for sensory equipment but also for communication and recording. As a result, the Internet of Things (IoT) is the most recent communication breakthrough in healthcare. In this work, we present a system that tracks patient health using a Blynk application, a micro-controller as a communication gateway, and sensors. When the output of the detector changes, a buzzer is embedded into the controller to alert the nursing staff. The sensor connects to a micro-controller, which is then interfaced with the liquid-crystal display (LCD) panel and wireless local area network (LAN) to provide notifications. An alert will be sent to the doctor through IoT if the system detects a change in the patient’s pulse rate or blood pressure, and the patient’s heartbeat, blood pressure, and body temperature will be displayed in real-time via Cloud. As a result, an IoT-based patient health monitoring system could save lives by efficiently monitoring patients’ health in real- time.

Page 716 of 918 | Total Record : 9174

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