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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 26, No 3: June 2022" : 64 Documents clear
An arrangement of the number of K-grams in the performance of Rabin Karp algorithm in text adjustment Yuli Astuti; Irma Rofni Wulandari
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1388-1394

Abstract

Rabin Karp algorithm is frequently used to determine the similarity between texts, using the hash function to compare the string identified and the substring in the text. The choice of the k value in the K-gram is often unrestricted. The number of k values used when cutting some terms will take longer if tried one by one. This research will perform a word cutting test on a script using K-gram 0 to 8. The results will cover the effect of the value of each K used on the similarity percentage produced. This research aims to determine the effect of the number of K-grams on the performance of Rabin Karp in text matching. The test underwent 20 sentences and 10 times using the dice coefficient for text similarity testing. The conclusion of this research should not use the K-gram 0 to 2 due to the K-gram basic principle: character deduction. Subsequently, if the character is 0,1,2, it does not have a meaning yet; thus, it gets a high similarity percentage. Based on trials by taking samples of K-gram 0 to 8 from 10 test data sets; the K-gram 3 is the best among K-grams 0 to 8.
Toward mobile learning at Jordanian higher education institutions Ahmad Shukri Mohd Noor; Marwan Nasser Yousef Atoom; Masita Abdul Jalil
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1538-1545

Abstract

Globally, teaching methods and tools in higher education institutions (HEIs) have changed nowadays. Many attempts have been made in Jordanian higher education institutions (JHEIs) in order to improve and continuity of the educational process, especially during coronavirus pandemic. The outbreak of this virus has become a major disruption where all Jordanian universities cancelled classes and moved toward online learning, and mobile learning (ML) has appeared as one of the possible solutions. ML is in its early stages at JHEIs, and it is academically unexplored enough. So, this study explores the ML experience at JHEIs during coronavirus disease 2019 (COVID-19) crisis. The data were collected using a web survey where 272 students in JHEIs participated. The results revealed that the smartphone is the most widely used mobile device for ML ML is easy to use, ML increases the interaction between the instructor and the students and among the students themselves, ML has a positive impact on students’ performance, and also students are willing to use ML in the future. The outcomes of the study support policy makers at JHEIs to make educational decisions relating ML phenomenon.
Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes Nur Ghaniaviyanto Ramadhan; Faisal Dharma Adhinata
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1765-1772

Abstract

Since the Coronavirus disease 2019 (COVID-19) pandemic hit the world, it had a significant negative impact on individuals, governments, and the global economy. One way to reduce the negative impact of COVID-19 is to vaccinate. Briefly, vaccination aims to enable the formed immune system to remember the characteristics of the targeted viral pathogen and be able to initiate an immune response that is rapid and strong enough to defeat future live viral pathogens. However, there are still many people in the world who are anti-vaccine. This certainly greatly hampers the process of accelerating the formation of the body's immune system widely in the community. Anti-vaccine people can be found on various social media platforms. Twitter was chosen as the data source because twitter is a common source of text for sentiment analysis. This study aims to analyze public sentiment on the COVID-19 vaccine through twitter in the form of tweets and retweets. This study uses the Gaussian Naïve Bayes method to see the results of the classification of sentiment analysis. The results obtained based on experiments prove that the Gaussian Naïve Bayes method can produce an average accuracy of 97.48% for each vaccine dataset used.
Development and performance evaluation of object and traffic light recognition model by way of deep learning Shweta Bali; Tapas Kumar; Shyam Sunder Tyagi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1486-1494

Abstract

Deep learning models have shown incredible achievement in the field of autonomous driving, covering different aspects ranging from recognizing traffic signs and traffic lighs, vehicle detection, license plate detection, pedestrian detection. Most of the algorithms perrform better when the traffic lights are bigger in size, but the performance degrades in case of small-sized traffic lights. In this paper, the main emphasis is on evaluating two most promising deep learning architectures: single shot detector (SSD) and faster region convolutinal network (Faster R-CNN) on “la route automatisée (LaRA) traffic light dataset” which contains small traffic lights as objects. The strengths and weaknesses are evaluated based on different parameters. The performance is compared in terms of mean average Precision (mAP@0.50) and average recall. The impact of data augmentation on the two architectures is also analyzed. ResNet50 V1 as feature extractor for Faster R-CNN achieved 96% mAP (mean average precision) which performed better than Original ResNet50 V1 Faster R-CNN pipeline. Also, different parameters such as batch size, learning rate and optimizer are tuned for detecting and classifying small traffic lights into different categories. 
Solution of load frequency control through jaya technique with unified power flow controller and redox flow battery Avinash Panwar; Vinesh Agarwal; Gulshan Sharma; Kayode Timothy Akindeji; Narayanan Krishnan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1247-1257

Abstract

This paper proposes the initial strategy of designing two degree of freedom proportional integral derivative (2DOF-PID) made load frequency control (LFC) action optimized using Jaya optimization algorithm for a hydro-hydro system. At first, the PID optimized through Jaya is used for hydro-dominated system, and the comparative analysis of all possible error definitions are carried out to show the benefits of selecting integral time absolute error (ITAE) for LFC. Then, 2DOF-PID is designed for the hydro governing system, and its performance is compared with other designs to show the efficacy of the present LFC about computed error values gain of the various models via graphical LFC. The results obtained through simulations are promising but oscillatory with greater settling time. Hence, the proposed controller is retuned by considering the unified power flow control (UPFC) in arrangement with the tie-line and redox flow battery (RFB) units in area-2, and it is further seen that the outcomes of the application show the prevalence of the proposed work.
Design and implementation of PC to PC data transmission using wireless visible light communication system Reyhane Jalil; Adnan Sabbar; Hassan Falah Fakhruldeen; Feryal Ibrahim Jabbar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1423-1428

Abstract

In this paper a laser-based visible light communication system for PC to PC data transmission has been designed, simulated, and implemented. This type of communication uses light waves in the visible spectrum (380 nm to 750 nm) to deliver data. Visible light communication is any way of transmitting data using visible light. In order to avoid being detected by human eyes, this kind of communication sends information at a slower rate than human vision. Visible light communication is significantly more reliable and capable of high information transmission rates than existing wireless technologies such as Wi-Fi, Bluetooth, and others that use radio frequency spectrum. Laser-based visible light communication systems are low-cost, low-power, and do not require radio interference studies. A diode laser is frequently used to create the signal carrier. Due to its high efficiency, it can transmit data as well as illuminate. Light waves can't be intercepted because they can't penetrate opaque objects, signifying a very secure connection.
Management patients information based finger print Ahmed Bashar Fakhri; Huda Farooq Jameel; Mustafa Falah Mahmood
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1281-1289

Abstract

The fingerprint is certainly one of the distinguishing features of the human body that is easily available and identifies one individual from another. The fingerprint sensor increases this distinctiveness, which is a device that can automatically classify or identify a person. The fingerprint based medical system is a more efficient means of storing clinical data for patients. It makes take advantage of fingerprint recognition technology to quickly and easily for determine the patient's past medical history. The system consists of an Arduino UNO board, a fingerprint sensor, an secure digital (SD) card module, and a micro-SD card. The suggested technology allows the use of a micro-SD card to store patient information as well as send it by internet. When this system was compared to the manual technique, the results indicate that the main advantage is that the proposed device saves a significant amount of time that manual searching and enrolling requires. Patients' information is simply collected and managed with this system, which has enhanced dependability, durability, and efficiency. It provides improved speed and performance, as well as better data security because the data is stored within the device.
Selecting the appropriate size of the graph for self-diagnostic model with graph density Sutat Gammanee; Sunantha Sodsee
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1556-1563

Abstract

Self-diagnosis is the concept of self-diagnosing disease from symptoms. Wehavetheideatocreateself-diagnosticmodelsfromdiagnosticdata. Thedatatobeanalyzedwerefromamedium-sizedhospitalinThailand. Themodelisdividedbystructureddataandunstructureddata. Thefirststepistoprocessstructureddatawithclusteralgorithms. Thesecondstepistoevaluatetheunstructureddatatogroupsymptomsintoabipartitegraph. Afterthegraphwascreated,themodelwasdividedinto10levels,accordingtothelevelofsimilarity. Thisresearchaimstoapplytheconceptofdensitygraph,theKappasandmultiplelinegraphtoselectingtheappropriatediagnosismodel. Theresultsofallthreeexperimentsshowedthattheappropriatemodelwasatalevelofsimilarityat40%.Wehavetheideatocreateself-diagnosticmodelsfromdiagnosticdata. Thedatatobeanalyzedwerefromamedium-sizedhospitalinThailand. Themodelisdividedbystructureddataandunstructureddata. Thefirststepistoprocessstructureddatawithclusteralgorithms. Thesecondstepistoevaluatetheunstructureddatatogroupsymptomsintoabipartitegraph. Afterthegraphwascreated,themodelwasdividedinto10levels,accordingtothelevelofsimilarity. Thisresearchaimstoapplytheconceptofdensitygraph,theKappasandmultiplelinegraphtoselectingtheappropriatediagnosismodel. Theresultsofallthreeexperimentsshowedthattheappropriatemodelwasatalevelofsimilarityat40
Enhancement of code division multiple access system performance using raptor codes Ikhlas Mahmoud Farhan; Dhafer R. Zaghar; Hadeel Nasrat Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1460-1468

Abstract

Some kinds of communication systems work in very low signal-to-noise (LSNR) environments. For these systems to function reliably, specific techniques and methodologies have to be used to mitigate the degrading effects of the channel. The channel coding method is the key element in most LSNR communication systems, but emphasizing the code division multiple access (CDMA) is a new transmission technique in these channels. To enhance the CDMA scheme's system capacity and reach unprecedented ranges of LSNR values in wireless sensor network. This paper suggests combining CDMA with certain types of channel coding algorithms, such as the raptor codes. The raptor channel encoding technique has improved the CDMA system's performance when using binary phase-shift keying (BPSK) modulation in additive white gaussian noise (AWGN) channels. It has achieved a low bit error rate in range of 10-7 at Eb/No value of (-3) dB and about 10-6 at shannon's limit. The Raptor-coded CDMA scheme works well for channel signal to noise ration (SNR) values of greater than -9 dB, showing an improvement of about 7 dB compared with turbo/convolutional channel coding methods used with the CDMA system bit error rate (BER) and throughput. On the other hand, it has been shown that the convolutional encoder presents the weakest performance compared to both the turbo and raptor codes.
Implementation of eigenface method and support vector machine for face recognition absence information system Chakim Annubaha; Aris Puji Widodo; Kusworo Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1624-1633

Abstract

The student attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.

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