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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 62 Documents
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Search results for , issue "Vol 34, No 1: April 2024" : 62 Documents clear
The development of color histogram method to identify air quality index based on sky images Sofika Enggari; Sumijan Sumijan; Muhammad Tajuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp186-196

Abstract

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.
For wireless LAN application, microstrip patch antenna design in S-band Md. Sohel Rana; Md. Soriful Islam Sourav; Md. Abdulla Al Mamun; Omer Faruk; Md. Mominur Rahaman; Md. Shehab Uddin Shahriar; Sukanto Halder; Md. Toukir Ahmed; Imran Chowdhury; Omar Faruq; Saikat Mondal; Md. Hasibul Islam; Shubhra Kanti Sinha Shuva
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp383-395

Abstract

This article presents a 3.5 GHz rectangular microstrip patch antenna (RMPA) designed, studied, and analyzed for wireless LAN applications. Using Fr-4 as substrate material, whose dielectric permittivity is 4.3, patch thickness is 1.65 mm, and loss tangent is 0.025. A feeding line with an impedance of 50 Ω is utilized to supply the antenna with power. Computer simulation technology (CST) software has been used to design the antenna and origin pro software has been used to display the resulting figures from the simulation. The antenna simulation showed that the return loss is -56.82 dB; the directivity gain is 6.02 dBi, the bandwidth is 0.148 GHz, and the voltage standing wave ratio (VSWR) is 1.0028. The paper aims to increase the return loss, develop a standard VSWR, increase the directivity gain of the antenna, and improve the antenna bandwidth. The results of the proposed antenna were much better than previously published papers, which were suitable for wireless applications. This proposed antenna can be used for future wireless LAN applications.
Automatic segmentation of human ear in the wild Rahul Lahkar; Khurshid Alam Borbora
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp333-341

Abstract

Ear biometrics has been a challenging and distinctive research area in recent times. The human ear possesses unique promising attributes that are being used by the researchers to carry out significant improvements in the field of human recognition using ear as a biometric. In order to achieve efficiency on any ear biometric system, the detection and segmentation of the human ear need to be performed precisely. Feeding accurately segmented images to the recognition system will result in higher recognition accuracy. In this paper, we present our work of segmentation of human ears from the images captured in unconstrained environment by employing the U-Net architecture on our own dataset and presented the results of ear segmentation. The U-Net model is also tested on the annotated web ears (AWE) segmentation dataset. We obtained 92.38% accuracy and 79.33% intersection over union (IoU) on the test data on our own dataset and 76.2% IoU on AWE segmentation dataset.
CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease Navaneeth Bhaskar; Ratnaprabha Ravindra Borhade; Sheetal Barekar; Mrinal Bachute; Vinayak Bairagi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp144-151

Abstract

An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach.
Modeling agricultural and methane emission data: a finite mixture regression approach Pattharaporn Thongnim; Ekkapot Charoenwanit
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp534-547

Abstract

In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation–maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place.
Two-stage HOG/SVM for license plate detection and recognition Lakhdar Djelloul Mazouz; Abdelkrim Meche; Abdelaziz Ouamri; Abdel Wahab Ait Darna
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp210-223

Abstract

Automatic license plate recognition (ALPR) is one of the technologies used in intelligent transport systems (ITS) to read vehicle license plates automatically. The extracted information has various potential applications, including but not limited to an electronic payment gateway, a system for paying parking fees, road surveillance, and managing traffic flow. In this paper, we propose an efficient method to detect and identify the Algerian license plate (LP). This method consists of a two-stage algorithm that combines the histogram of oriented gradients (HOG) with the support vector machine (SVM) classifier. The purpose of the first stage of HOG/SVM is the detection of the LP, while the recognition of the digits is accomplished by the second stage of HOG/SVM. As first contribution, a dataset of standard Algerian LP not available elsewhere is built (DZLP dataset), The second is a proposal of a very efficient pre-processing step for LP detection and digit recognition. Experimental results show that the proposed approach yields very high license plate and average digits recognition rates, which of 97.5% and 99.46%, respectively.
Investigating power scaling factor for pattern division multiple access Linda Meylani; Vinsensius Sigit Widhi Prabowo; Iswahyudi Hidayat; Nisa Alwiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp370-382

Abstract

Pattern division multiple access (PDMA) is a one type of multi-domain non-orthogonal multiple access (NOMA) that support massive connectivity and can improve spectral efficiency. The unique pattern is used by each user to map its transmitting data into a group of resource, which consist of frequency, code and spatial domain or combination of these resources. Power scaling and phase shifting are used to resolve ambiguity as consequence non uniform distribution of the received combined constellation. In this paper, we propose investigation on power scaling factor for each user in PDMA matrix to increase sum rate transmission and propose combine successive interference cancellation (SIC) based on diversity order and power scaling factor for each user. The simulation results confirm that the proper implement power scaling factor in pattern type 2 show best performance in Rician fading channels.
Automatic facial expression recognition under partial occlusion based on motion reconstruction using a denoising autoencoder Abdelaali Kemmou; Adil El Makrani; Ikram El Azami; Moulay Hafid Aabidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp276-289

Abstract

Automatic facial expression recognition (FER) plays a valuable role in various fields, including health, road safety, and marketing, where providing feedback on the user’s condition is crucial. While significant progress has been made in controlled environments (such as frontal, unconcluded, and well-lit conditions), recognizing facial expressions in unconstrained environments (natural settings) remains challenging. The presence of occlusions poses a particular difficulty as they obscure parts of the facial information captured in the image. To address this issue, researchers have proposed different solutions, broadly categorized into two approaches: those focusing on visible regions of the face and those attempting to reconstruct hidden parts. Currently, most solutions rely on texture or geometry-based methods, with only a few utilizing motion-based approaches. However, incorporating motion appears to be particularly promising in adapting to occlusions due to its unique characteristics, such as close-range propagation and local coherence. In this paper, our focus lies on leveraging motion to overcome the challenges posed by occlusions in FER tasks.
Clustering method for criminal crime acts using K-means and principal component analysis Ratih Hafsarah Maharrani; Prih Diantono Abda’u; Muhammad Nur Faiz
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp224-232

Abstract

Criminality is an act of violating the values and norms of society that causes a lot of harm. Much of the criminal data is often just a collection of data that has no information. Analysis of crime data is key in efforts to reduce crime rates that provide an overview of the incidence of crime, patterns, levels of vulnerability, and the level of security of an area. This research proposes data analysis that provides an understanding of crime using data mining techniques, especially the K-means cluster method, both traditional and with principal component analysis (PCA) dimension reduction. Before the PCA process, the values are transformed first with Z score normalization. From the processing through the davies bouldin index (DBI) performance test with 3 clusters, it is concluded that traditional K-means produces a DBI Index value of 0.019 and K-means PCA of 0.299. Meanwhile, to see the optimal cluster, several iterations were performed and resulted in the most optimal DBI index of 4 clusters in K-means of 0.014 and K-means PCA of 0.172. From the performance test value, it means that in the context of clustering the traditional criminal K-means data is declared more optimal than K-means PCA.
Comparison of power system flow analysis methods of IEEE 5-bus system Harpreet Kaur Channi; Ramandeep Sandhu; Nimay Chandra Giri; Parminder Singh; Fathy Abdelaziz Syam
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp11-18

Abstract

Load flow analysis is a crucial tool used by electrical engineers for simulating the power system. It is aimed at examining the most possible way of operating and controlling a power system and the exchange of power flow within the power system. For the economic and optimal operation of power systems, the most essential task is to find the most feasible solution technique suitable and efficient for the study of power generation, transmission, and distribution. There are various power flow study solution techniques, and for some solution techniques, the simulation of the system can take a long time, which prevents the simulator from attaining a higher accuracy result for the power flow simulation due to the interrupting rise and fall in power demand from the consumer, which also affects the power generation as well. This paper discusses the comparison of various techniques used in load flow studies with the assistance of a small power system with five buses. The numerical solution techniques used are the fast decoupled load flow solution technique, the Gauss-Seidel solution technique, and the Newton-Raphson solution technique for a power flow study solution on an IEEE 5-bus using MATLAB/Simulink.

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