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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 64 Documents
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Search results for , issue "Vol 30, No 1: April 2023" : 64 Documents clear
Detection of harmful gases present in the environment Pratiksha Rai; Syed Hasan Saeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp70-80

Abstract

The electronic nose (e-nose) is demonstrated in this research for detecting and identifying several forms of hazardous gases. We describe an e-noses for detecting several gases, including butane, acetone, methane, and ethanol. For dimensionality reduction in 3D representation, data processing approaches are based on the partial least square (PLS) method. The suggested system can be utilised for sensor optimization since different sensors with varied operating temperatures can be tested in many devices to find the best array for a specific detection or application. The results reveal that, depending on the sensor array characteristics, varying success rates in classification can be attained when discriminating contaminants. The preceding criteria lead to a new search for a portable, dependable, low-cost, and most efficient gas sensor. The major purpose of this study is to create a gas sensor array that can detect and monitor toxic and poisonous gases in the environment, as well as warn against dangerous organic compounds. Our goal is to create a sensor system that can distinguish the most significant decontamination gases while also being highly responsive, precise, low-effort, and low-power demanding.
Service quality model analysis on the acceptance of information system users’ behavior Rina Fiati; Widowati Widowati; Dinar Mutiara Kusumo Nugraheni
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp444-450

Abstract

Website technology have created both opportunities and challenges for higher education. Information systems as online learning medium need to pay attention to access, quality and user needs in order to improve the quality of e-learning services. The research objective is to determine user acceptance of the system. The service quality method as identification in solving problems. The research focus on the analysis of five dimensions namely measurable, reliability, responsiveness, assurance and empathy. The research was conducted at the research college at Muria Kudus University. The results state that the assessment model of a system on the website can be completed properly. The level of effectiveness is carried out with respondents as users through the distribution of questionnaires. The results of the analysis with a statistical correlation performance test of 0.985 were declared accepted with a validity level of 97% indicating that the success of the system implemented was from the acceptance side. The higher the empathy with service quality and performance expectations, the greater the student's intention to receive online education services. This research is a reference for developing information systems on e-learning.
Real-time recognition of American sign language using long-short term memory neural network and hand detection Reham Mohamed Abdulhamied; Mona M. Nasr; Sarah N. Abdul Kader
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp545-556

Abstract

Sign language recognition is very important for deaf and mute people because it has many facilities for them, it converts hand gestures into text or speech. It also helps deaf and mute people to communicate and express mutual feelings. This paper's goal is to estimate sign language using action detection by predicting what action is being demonstrated at any given time without forcing the user to wear any external devices. We captured user signs with a webcam. For example; if we signed “thank you”, it will take the entire set of frames for that action to determine what sign is being demonstrated. The long short-term memory (LSTM) model is used to produce a real-time sign language detection and prediction flow. We also applied dropout layers for both training and testing dataset to handle overfitting in deep learning models which made a good improvement for the final result accuracy. We achieved a 99.35% accuracy after training and implementing the model which allows the deaf and mute communicate more easily with society.
Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization Adri Senen; Tri Wahyu Oktaviana Putri; Jasrul Jamani Jamani; Eko Supriyanto; Dwi Anggaini
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp33-45

Abstract

As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the electricity variable, so the results tend to be biased for areas experiencing rapid land use changes. Therefore, this paper proposes an ELPC approach based on micro-spatial. Microspatial analysis is done by dividing area in the form of the smallest grids involving various electrical, demographic, geographic and socio-economic variables, which are then grouped using adaptive clustering based on fuzzy C-means (FCM). The adaptive clustering algorithm is proven to be able to determine the degree of membership of each grid data against each cluster with the ability to determine the number of clusters automatically according to the attribute data provided. The ELPC results which consist of 5 clusters are then analyzed using descriptive statistic, plotted, and mapped to obtain more accurate and realistic load characteristics in accordance with the pattern and geographical conditions of the region, so that the results can be used as a reference in load forecasting, network development, and distributed generation (DG) integration.
Cryptocurrency price forecasting method using long short-term memory with time-varying parameters Laor Boongasame; Panida Songram
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp435-443

Abstract

Numerous research have been done to predict cryptocurrency prices since cryptocurrency prices affect global economic and monetary systems. However, investigations using linear connection approaches and technical analysis indicators frequently fall short of providing an explanation for changes in the pattern of BitCoin pricing. This paper is proposed to study time-varying parameters with long short-term memory (LSTM). The study is investigated on a dataset retrieved from Binance from March 2022 to April 2022. The proposed LSTM used a variety of hyperparameter settings, particularly time parameters, to predict the cryptocurrency price (BTC/USDT) on the dataset. Additionally, it is evaluated in terms of mean absolute percentage error (MAPE) in comparison to smooth moving average (SMA), weighted moving average (WMA), and exponential moving averages (EMA). From the investigation, using the previous 3 days for prediction gives the lowest of the MAPE values and the proposed LSTM outperformed the other models. When considering the last three days' value of pricing, the indicated LSTM offers the best accurate prediction, with a MAPE percentage of 0.0927%.
Efficient hardware implementation for lightweight Loong algorithm using FPGA Marwa Subhi Ibrahim; Yasir Amer Abbas; Mudhafar Hussein Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp451-459

Abstract

Recently low-resource devices such as radio frequency identification (RFID), internet of things (IoT), and wireless sensor networks (WSN) using lightweight cryptography (LWC) to protect devices. Created or design low-resource devices with a lightweight cryptographic technique should take into account important factors such as the battery life and the amount of data to be processed. This paper provides a new hardware designed for Loong lightweight cryptographic algorithm that takes into account the previously described constraints. The new hardware architecture for Loong algorithm with resource sharing to reduce system designed. The proposed approach is implemented using ISE Xilinx V14.7 using Virtex 4 field programmable gate array (FPGA) platform. The synthesis analysis for ISE showed the throughput of 851.264 Mbps with efficiency of 2.282 Mbps/slice, and a power consumption of 0.193 Watt. The implementation designed show the all-algorithms size consists of 373 slices, and the maximum possible operating frequency is 212.816 MHz. To the best of our knowledge, this is the first time that Loong algorithm has been implemented on FPGA using very high-speed integrated circuit hardware description language (VHDL).
Analysis of facial emotion recognition rate for real-time application using NVIDIA Jetson Nano in deep learning models Usen Dudekula; Purnachand Nalluri
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp598-605

Abstract

Detecting facial emotion expression is a classic research problem in image processing. Face expression detection can be used to help human users monitor their stress levels. Perceiving an individual's failure to communicate specific looks might help analyze early psychological disorders. several issues like lighting changes, rotations, occlusions, and accessories persist. These are not simply traditional image processing issues, yet additionally, action units that make gathering activity of facial acknowledgment troublesome look information, and order of the demeanor. In this study, we use Xception taking into account Xception and convolution neural network (CNN), which is easy to focus on incredible parts like the face, and visual geometric group (VGG-19) used to extract the facial feature using the OpenCV framework classifying the image into any of the basic facial emotions. NVIDIA Jetson Nano has a high video handling outline rate. Accomplishing preferable precision over the recently evolved models on software. The average accuracies for standard data set CK+,” on NVIDIA Jetson Nano, the accuracy rate is 97.1% in the Xception model in the convolutional neural network, 98.4% in VGG-19, and real-time environment accuracy using OpenCV, accuracy rate is 95.6%.
Evaluating face recognition with different texture descriptions and convolution neural network Wafaa Mohammed Saeed Hamzah Al-Hameed; Marwan B. Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp332-340

Abstract

Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution neural network (CNN) to implement face recognition. Such a design consists of two parts: a convolutional learning feature model and a classification model. The first one learns the important feature,while the second part produces a score class for each sample input. Many experiments are implemented on the known dataset once for the number of nearest neighbors (K value), and then decrease the number of expression samples for each individual the other time. The cross-validation method is used to provide a true picture of the accuracy of the face recognition system. In all experiment results, the center symmetric LBP with KNN outperforms the classic LBP. While significant progress in the results accuracy recognition ratio of the CNN model compared with other methods used.
Edge device for movement pattern classification using neural network algorithms Ricardo Yauri; Rafael Espino
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp229-236

Abstract

Portable electronic systems allow the analysis and monitoring of continuous time signals, such as human activity, integrating deep learning techniques with cloud computing, causing network traffic and high energy consumption. In addition, the use of algorithms based on neural networks are a very widespread solution in these applications, but they have a high computational cost, not suitable for edge devices. In this context, solutions are created that bring data analysis closer to the edge of the network, so in this paper models adapted to an edge device for the recognition of human activity are evaluated, considering characteristics such as inference time, memory, and precision. Two categories of models based on deep and convolutional neural networks are developed by implementing them in C language and comparing with the TensorFlow Lite platform. The results show that the implementations with libraries have a better accuracy result of 76% using principal component analysis inputs, obtaining an execution time of 9ms. Therefore, when evaluating the models, we must not only consider their accuracy but also the execution time and memory on the device.
Requirement engineering problems impacting the quality of software in Sub-Saharan Africa Andrew Quansah; Asiamah Emmanuel; Bright Kyeremateng; Esther Ntow Kesse
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp350-355

Abstract

Poor software quality has led to tremendous financial losses, necessitating the goal of this study. This study aimed to find out the major cause of poor quality of software and propose solutions to mitigate the problem. Histogram analysis was conducted using data from software development firms’ online applications used to track all defects and issues for each project, which are logged under a unique project ID. The requirement engineering stage was found to produce the most problems that directly or indirectly impact software quality. The capability maturity model integration, prototyping, ISO 9001, Walkthroughs, and Formal Inspections were proposed as solutions that could be used to mitigate the software quality problems that arise from the requirement engineering stage in the software development life cycle.

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