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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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Design intelligent maximum power point tracking for photovoltaic at Universitas Airlangga Herlambang Setiadi; Firdaus Bima Firmansyah; Prisma Megantoro; Tahta Amrillah; Herri Trilaksana; Galih Bangga; Muhammad Abdillah; Awan Uji Krismanto
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.pp1212-1222

Abstract

Rooftop photovoltaic (PV) plant is one ot the independent electricity that us favorable in recent year. Rooftop PV plant can be used as the source of smart building as well as fast charging station. Although rooftop PV plant could provide clean and sustainable energy from solar, they also come with disadvantages in term of intermittent power output. This intermittent power output is due to the uncertainty of the source. To tackle this problem, maximum power point tracking method is essential. Maximum power point tracking (MPPT) method can be used to extract maximum power from the solar cell in all conditions. This paper proposes an intelligent method for designing DC-DC MPPT based on fruit fly optimization (FFO) on realistic rooftop PV plant. Practical rooftop PV plant in Universitas Airlangga is employed as the testing system. The proposed method's efficacy is evaluated using time domain simulation. According to the simulation results, the proposed method can significantly extract power from PV.
Auto electronic recognition of the Arabic letters sound Omar Ibrahim Alsaif; Kifaa Hadi Thanoon; Asmaa Hadi Al_bayati
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.pp769-776

Abstract

In this research Arabic speech sounds have been studied and investigated, so as to find the distinctive features of each articulated sound. Therefore, certain Arabic sound which share certain approximate distinctive significant features have been chosen for study the ability of distinguishing among them through abstracting characteristic features for them. The signals of speech for the sounds have been recorded through the microphone which represented in a binary matrix. This procedure was implemented so as prepare these signals for processing operation through which two features for the co-occurrence matrix (contrast, energy) have been counted. The values of these features were studied and compared from one person to another to discover the certain speech sounds properties sharing certain common distinguishing features approximate in their articulation one another. The results analysis for this study gave the ability of the dependence to these features for distinguish the sound of speaker, in addition to the high ability which provided to distinguish among the arabic letters, where no connect between both co-occurrence matrix elements and the features of signaling of any arabic letters.
An efficient automated vehicle license plate recognition system under image processing Dilshad Islam; Tanjim Mahmud; Tanjia Chowdhury
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.pp1055-1062

Abstract

An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.
Security system using mobile image processing and color recognition for the visually impaired Eugene Rhee; Junhee Cho
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.pp1363-1368

Abstract

Voice technology at traffic lights or bus stops is emerging for the independent daily life of the blind, but there are few technologies that efficiently help the blind, such as knowing the color of clothes to wear before going out or entering the bus stop at once. To support such difficulties, this paper proposes a method that can be helped by using a smartphone application to distinguish the color of outdoor clothes. Smartphones, which are hardware-based for applications, have the advantage of predictable results, ease of transportation, independence from direct use, and personal support for the blind through various applications. However, there are very few applications to help the blind. This paper proposes the development of an application that can efficiently and independently recognize colors and images at anytime, anywhere by scanning images using smartphone cameras and converting them into bitmap images. Finally, the effects that can be expected through the application proposed in this study are described.
A new design of a printed reconfigurable coplanar multiband antenna Fatima Ouberri; Abdelali Tajmouati; Jamal Zbitou; Mohamed Latrach
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.pp234-240

Abstract

The demand for multi-functional components has increased enormously in recent years. Advances in integrated technology have enabled researchers to adapt diverse applications operating at different frequencies in a single wireless device. A compact broadband coplanar waveguide (CPW)-fed square aperture monopole antenna with inverted-L grounded strips is first described. The proposed antenna has a small size of 60×60×0.74 mm3 and has excellent performances which include a good input impedance matching with a much wider operating bandwidth, an excitation of the circular polarization at 2.45 GHz, and a stable omnidirectional radiation pattern. Then, a multiband reconfigurable antenna design is developed from this structure. The frequency reconfigurable approach is obtained using a varactor diode. In this work, it is observed that frequency diversity can be obtained by varying the value of the capacity by leaving the dimensions antenna unchanged. The results are given using CST microwave studio and show good performances in terms of return loss, bandwidth, gain and radiation pattern and demonstrate that the proposed antenna offers a reconfigurable solution for multi-standard wireless communication applications.
Deep learning application for real-time prediction of COVID-19 outbreak with susceptible-infected-recovered-deceased model Hoang-Sy Nguyen; Thu Ngan Phan Thi; Cong-Danh Huynh
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.pp567-576

Abstract

Due to the complex nature of a pandemic such as COVID-19, forecasting how it would behave is difficult, but it is indeed of utmost necessity. Furthermore, adapting predictive models to different data sets obtained from different countries and areas is necessary, as it can provide a wider view of the global pandemic situation and more information on how models can be improved. Therefore, we combine here the long-short-term memory (LSTM) model and the traditional susceptible-infected-recovered-deceased (SIRD) model for the COVID-19 prediction task in Ho Chi Minh City, Vietnam. In particular, LSTM shows its strength in processing and making accurate numerical predictions on a large set of historical input. Following the SIRD model, the whole population is divided into 4 states (S), (I), (R), and (D), and the changes from one state to another are governed by a parameter set. By assessing the numerical output and the corresponding parameter set, we could reveal more insights about the root causes of the changes. The predictive model updates every 10 days to produce an output that is closest to reality. In general, such a combination delivers transparent, accurate, and up-to-date predictions for human experts, which is important for research on COVID-19.
Machine learning prediction of video-based learning with technology acceptance model Rahayu Abdul Rahman; Suraya Masrom; Nor Hafiza Abd Samad; Rulfah M. Daud; Evi Meutia
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1560-1566

Abstract

COVID-19 outbreak has significant impacts on education system as almost all countries shift to new way of teaching and learning; online learning. In this new environment, various innovative teaching methods have been created to deliver educational material in ensuring the learning outcomes such as video content. Thus, this research aims to implement machine learning prediction models for video-based learning in higher education institutions. Using survey data from 103 final year accounting students at Malaysian public university, this paper presents the fundamental frameworks of evaluating three machine learning models namely generalized linear model, random forest and decision tree. Besides demography attributes, the performance of each machine learning algorithm on the video-based learning usage has been observed based on the attributes of technology acceptance model namely perceived ease of use, perceived usefulness and attitude. The findings revealed that the perceived ease of use has given the highest weight of contributions to the generalized linear model and random forest while the major effects in decision tree has been given by the attitude variable. However, generalized linear model outperformed the two algorithms in term of the prediction accuracy.
The significance of artificial intelligent technique in classifying various grades of agarwood oil Aqib Fawwaz Mohd Amidon; Siti Mariatul Hazwa Mohd Huzir; Zakiah Mohd Yusoff; Nurlaila Ismail; Mohd Nasir Taib
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.pp261-269

Abstract

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.
Cadmium sulfide doped with silver as CO2 gas sensor using pyrolysis technique Uday Ali Sabeeh Al-Jarah; Haidar Jawad Mohamad; Yahya Mustafa Abdul-Hussein
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.pp686-692

Abstract

The monitoring of CO2 in our life safety, industrial, and chemical laboratoryapplications make it an inspiring task. The chemical spray pyrolysis techniquewas used to prepare CdS/Ag thin films. The nanocrystalline cadmium sulfidethin films were doped with Silver at different doping concentrations (0%, 2%,and 4%). The morphologies, structures, and gas sensing properties of CdS/Agfilms are presented. The samples were characterized using X-ray diffraction(XRD) and atomic force microscope (AFM). The XRD results show that thefilms are a polycrystalline composition and hexagonal type with a favouredorientation along (111) direction. The average grain size (nm) of AFM isbetween 75 and 55 nm. As a result, Ag doping changes the sensitivity of thesamples respectively with the percentage of doping with time. The synthesissamples show controlling sensitivity and the small response of sensitivity arethe key point in this study.
Text prediction recurrent neural networks using long short-term memory-dropout Orlando Iparraguirre-Villanueva; Victor Guevara-Ponce; Daniel Ruiz-Alvarado; Saul Beltozar-Clemente; Fernando Sierra-Liñan; Joselyn Zapata-Paulini; Michael Cabanillas-Carbonell
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1758-1768

Abstract

Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem "La Ciudad y los perros" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.

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