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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
March 2022 | Vol. 100 No.05

Title:	A GENERALIZED SYSTEM WITH LOSSES LOOK AHEAD ECONOMIC DISPATCH IS INVESTIGATED BY BIO IN-SPIRED ALGORITHMS
Author:	V. SAI GEETHA LAKSHMI, Dr. M. VANITHA SRI, Dr. M. VENU GOPALA RAO
Abstract:	Now a days utilization of power for domestic and industrial purposes are increasing day by day. As load increases that is demand increase production should increase, but due to cost oriented point of view at de-mand side it is very important by including number of units which used for to meet demand. In this paper it considered as economic dispatching the load with optimized way to meet demand with less cost. Here to get optimized output a bio inspired algorithms are used. The bio inspired algorithms such as antlion algo-rithms (ALA), quantum particle swarm optimization (QPSO) genetic algorithm (GA) is utilized and com-pared with each other. In this MATLAB programming has been used for getting output. Here the general-ized system includes generating units with losses and objective of this work is to meet demand with less cost.
Keywords:	Economic load dispatch, bio inspired algorithms, generating units, losses
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	WHAT AFFECTS CUSTOMER'S INTENTION TO WRITE AN ONLINE REVIEW?
Author:	MONICA CHANDRA, VIANY UTAMI TJHIN
Abstract:	Technology affects almost every sector and has also transformed the way people do business. Increasing internet penetration is one of the drivers of digital transformation in Indonesia. Along with the increase in internet users, there is a shift from conventional to digital sales. One of the most popular online sales media today is e-marketplace. The wide choice of products and sellers in the e-marketplace, makes the role of product reviews as decision support in the process of buying products online. But in reality, not all consumers write reviews on every product they have purchased. Whereas the latest reviews from buyers can help sellers to increase sales and face increasingly fierce competition in e-marketplace. Therefore, this study examines the factors that can influence consumers' intentions to write online reviews. The data was collected through an online questionnaire and obtained from 218 respondents. The results of data processing using SMARTPLS show that E-marketplace Service Quality, Customer Satisfaction, and Customer Engagement have a significant effect on the intention to write online reviews.
Keywords:	Online Reviews, E-commerce, E-marketplace Service Quality, Customer Engagement, Customer Satisfaction
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	FACIAL EXPRESSION RECOGNITION USING CNN FOR HEALTHCARE
Author:	CHRISMORGAN SHINTARO, WILLIAM, FELIX NOVANDO, AZANI CEMPAKA SARI
Abstract:	Human emotion is the way how human express anything from themselves. We can extract many informations from a human emotion alone like temper and health condition. Due to in Indonesia, people usually don’t visit doctor just to seeing their own face condition. We can use a computer vision task to do so. This paper is aim to train a computer vision model that can recognize a human face emotion. Inspired by Convolutional Neural Networks (CNNs) that capable of doing image classification, we proposed our own designed multiple CNN classification model with different layer settings to do facial expression recognition. Next we applied the model on the FER2013 dataset. The best training accuracy achieved by the second model in 92.40% accuracy and the best validation accuracy achieved by third model in 67.43% accuracy.
Keywords:	Computer vision, CNN, Facial Expression Recognition, Health, Classification
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	BIG SCHOLARLY DATA TECHNIQUES, ISSUES, AND CHALLENGES SURVEY
Author:	NAGWA YASEEN HEGAZY , MOHAMED HELMY KHAFAGY , AYMAN ELSAYED KHDER
Abstract:	Researchers around the world always request a research article relevant to their topic that satisfies their information need. The academic research environment generates an excessive amount of data called big scholarly data. Scholarly data usually includes millions of raw data represented in authors, papers, citations, and publication venues as well as author’s information and affiliation. The enormous amount of valuable data generated by academic research has attracted researchers to explore this problem domain using different methodologies. Finding the most important articles in the field is considered a critical issue for researchers and journals as well as academic institutions. Ranking systems have become a very popular topic in the academic environment due to their importance in hiring, promotions, grants and award procedures. An accurate ranking system leads to an efficient recommendation system. This paper describes the background for big scholarly data and technologies. It also reviews the most important ranking systems and their algorithms. Recommendation systems approaches are presented for academic research, and the characteristics of highly cited papers are highlighted to help researchers improve their paper citations. Finally, this paper introduces an overview of big scholarly data visualization techniques and existing tools.
Keywords:	Big Scholarly Data (BSD), Ranking Systems, Recommendation System, Citation Network.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	DEEP LEARNING AND TIME SERIES ANALYSIS APPLICATION ON TRAFFIC FLOW FORECASTING
Author:	NADIA SLIMANI, MUSTAPHA AMGHAR, NAWAL SBITI
Abstract:	The use of new information and communication technologies is an important aid to solving transportation problems. This is commonly known as ITS (Intelligent Transport Systems) that can provide effective information for travelers and traffic managers. Road traffic prediction has led to a growing research area debated by several researchers affiliated to a range of disciplines. Recently, a significant amount of research efforts has been devoted to deep learning methods, greatly advancing traffic prediction abilities. The purpose of this paper is to meet a practical need: forecasting highway traffic volume by payment method (manual/ electronic toll) over a long time horizon based on historical observations. A variety of methods were used to make this forecast, and the results were compared and interpreted to identify model limitations and improve the approach. We propose to forecast daily highway traffic using four methods: Seasonal Autoregressive Integrated Moving Average with additional lagged values (SARIMAX), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and at least we perform combination method which has been shown to be efficient in the literature and which, applied to our real case study, will confirm these performances. Indeed, the best prediction results were obtained using the hybridization CNN-LSTM which reached an R-squared value of 95%. In the present study, such an effort resulted in considerable improvement in long terme forecasting accuracy when compared with the existing models’ performance previously adopted in the field of traffic forecasting.
Keywords:	Intelligent Transport Systems, Prediction Models, Deep Learning, Time Series Forecast, SARIMAX, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN).
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	THE INFLUENCE OF INTERDISCIPLINARY INTEGRATION OF INFORMATION TECHNOLOGIES ON THE EFFECTIVENESS OF IT TRAINING OF FUTURE TEACHERS
Author:	TAKIR BALYKBAYEV, ESEN BIDAIBEKOV, VADIM GRINSHKUN, NURGUL KURMANGALIYEVA
Abstract:	The problem of finding ways to integrate information technologies in education, as well as identifying new ways of their implementation, is relevant in the light of the emergence of new disciplines driven by the development of science and technology. The purpose of the study is to practically confirm the effectiveness of the proposed approaches to the selection of information technologies in the training of future teachers in the field of computer science within the framework of two successive courses, which are often taught separately. The integration of technologies and teaching aids can significantly increase the effectiveness of training students in these areas. The educational research was carried out by forming a control (64 people) and experimental (65 people) groups of first-, second-, and third-year students of teacher's university. In the experimental group, the ICT (Information and Communication Technologies) and DTE (Digital Technologies in Education) courses were conducted using the same software, selected following the specifics of teaching both disciplines. Assessment of student's knowledge was carried out using the mathematical statistics and specially developed tests. The proposed approaches to conducting classes based on the interdisciplinary integration of IT and preliminary selection of the necessary integrated teaching aids contribute to the personal development of future teachers and diversity of IT sphere. Interdisciplinary integration of information technologies needs comprehensive and balanced multidisciplinary training and integration in the classroom presents a significant challenge. In course of this study were given suggestions and methods for modern educators for coping with the increasing dividing of IT from other disciplines. The positive influence of the interdisciplinary integration on the effectiveness of training students in the interdependent areas of informatics and computing has been confirmed. Forming the correct attitude to digital resources as a teaching medium is a significant factor in the effectiveness of professional training of teachers.
Keywords:	Digital technologies, Educational technologies, Quality of education, Educational resources, Digital competence.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	INTERNET OF THINGS CONCEPT IMPLEMENTATION FOR MICROGREEN FARMING AUTOMATION WITH GAMIFICATION PRINCIPLES
Author:	ALBERT SEBASTIAN, RIYANTO JAYADI
Abstract:	Food security is one biggest challenge, especially in a big city. Price of fruits and vegetables are relatively higher than other surround cities. That happens because capabilities to produce fruits and vegetables decrease along with the increasing rate of urbanization. Therefore, households in big cities have to be able to produce their own fruits and vegetables. With Internet of Things concept (IoT), we can make farming process easier. In order to increase user interest, we can use gamification principle along with information system to collect and process data. Gamification has been utilized to increase user’s interest or loyalty. In this paper, researchers described a concept of gamification and IoT combination in agriculture. Researchers explain how it can be used for increasing user’s farming interest, especially urban community which have limited open fields.
Keywords:	Gamification, Internet of Things, Agriculture, Farming, Automation
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	DEVELOPMENT OF A MOBILE AUTOMATED AIR QUALITY MONITORING SYSTEM FOR USE IN PLACES OF TECHNOGENIC ACCIDENTS ON RAILWAY TRANSPORT
Author:	AKHMETOV B. , LAKHNO V., BLOZVA A., SHALABAYEVA M., ABUOVA A. SKLADANNYI P., SAGYNDYKOVA Sh
Abstract:	A mobile air quality monitoring system (MAQMS) has been designed and implemented at railway infrastructure facilities. The system (or MAQMS) consists of two main parts: a single data processing server and information collection devices. The transmitter is based on the ATmega328 microcontroller. For component devices of MAQMS, the operation of which depends on Wi-Fi, a transmitter based on an ESP8266 microcontroller is used, which ensures stable communication according to the 802.11n standard. This standard is the main data transfer protocol between environmental data collection devices and the MQTT server. In the implemented MAQMS, the data processing server receives information via the MQTT protocol from all devices about the status of each sensor and the location of the device at the site of a railway accident accompanied by environmental pollution. All data with a certain periodicity is written to the database on the server in the appropriate format with timestamps. To access the stored data, a WEB interface is used, which allows you to administer the MAQMS from all devices that have a web browser.
Keywords:	mobile monitoring system, software, railway transport, environmental monitoring,
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	ANALYSIS ON VOWEL /E/ IN MALAY LANGUAGE RECOGNITION VIA CONVOLUTION NEURAL NETWORK (CNN)
Author:	NIK MOHD ZARIFIE HASHIM, NIK ADILAH HANIN ZAHRI, MOHD JUZAILA ABD. LATIF, ROSTAM AFFENDI HAMZAH, NIK FARIZAL HASHIM, MAISARAH KAMAL, MAHMUD DWI SULISTIYO, AFIQAH IYLIA KAMARUDDIN
Abstract:	In recent years, the silent killer disease, defined as a non-communicable disease, has become a frequent topic discussed in many academic discussions. Although this disease is not transferable from one to another, starting from 1990, the increment trend was annually published by the world statistic data for this disease, e.g., heart attack and stroke. The more significant consequence of these two diseases is to disable one or more human capabilities. One of the stroke disease effects is becoming disabled from hearing. Speech disabilities are the focus of this proposed study in this paper. Since the person diagnosed as a stroke patient requires attending the recovery session or rehabilitation session, the rehabilitation center must prepare and provide a sound module and system to help the patient regain their capability. Rehabilitation is an alternative path to gradually giving routine practice to the patient to improve their capability back. For this purpose, the rehab center requires a quantity of time to provide the patient to attend the training session. The training, however, is conducted in two ways, physically and virtually. For the Malaysia stroke patient, the training for pronouncing the vowel in the Malay language is crucial in getting back the speaking capability. Since the Malay language has 6 types of vowels, which are /a/, /e/, /e ̂/, /i/, /u/, and /o/. Here, there is a limitation to smartly recognizing the difference between the two /e/ vowels. Malay's /e/ vowel is crucial as the similar spelling vocabulary conveys two different meanings. This study analyzed the differences in recognizing the two /e/ vowels using Convolution Neural Network (CNN) with the help of the existing sound-image dataset.
Keywords:	Convolutional Neural Network (CNN), /e/ vowel, Malay language, Non-Communicable Disease (NCD), Recognition, Rehabilitation, Stroke patient
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	AN ENHANCED PATIENT E-REFERRAL MODEL FOR A THREE-TIER HEALTH CARE SYSTEM
Author:	ETIM, OKORI DAVID, EDIM, AZOM EMMANUEL, ANA, PRINCE ONEBIENI, EMMANUEL U. OYO-ITA, INYANG, GABRIEL AKIBI, ETIM, ESU OYO-ITA
Abstract:	The patient referral system used in health care in Nigeria is paper based. The referral system lacks a defined structure for presenting information to enhance efficiency and timely service. Existing e-referral models have been implemented in two-tier health care systems and are not suitable for three-tier health care system in Nigeria. This study was carried out to develop an e-Referral model for a three-tier health care in order to provide a well define structure and language of presentation in each tier and enhance communication and cooperation among the three tiers of care providers. Ethical approvals were obtained for the participation of care providers during data collection. The data was used to design an e-referral model and implemented a web based application. A usability test was conducted with participants from the three tiers of health facilities. The results showed that participants scored 71.2, 75 and 80.7 for primary, secondary and tertiary health care participants, which is above standard usability score. Further results obtained after a period of six months from users who have been using the e-referral within the three tiers health care providers showed a decrease in error rate from 51.4% to 18%, which is an improvement in interactions accuracy and handling of referral cases in the health care centers.
Keywords:	Patient E-Referral model, Three-tier Health Care Delivery, Primary Health Care, Secondary Health Care, Tertiary Health Care.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	CLASSIFICATION OF ANDROID MALWARE TYPES USING SUPPORT VECTOR MACHINE
Author:	HENDRA SAPUTRA, AMALIA ZAHRA
Abstract:	In the past years, the amount of Android malware is on the increase. This statement is supported by the data from VirusShare showing an increase in the amount of malware each passing year. Hence it is necessary to classify the malware for identifying types of malware attacking smartphone which consequently will help resolve the issue easier. For addressing the issue, this research classifies Android malware based on their types. The attributes employed are activities, permission, and receiver located inside the Androidmanifest.xml file. This research obtains the malware data from VirusShare database in 2018. Classification algorithms used was Support Vector Machine (SVM) with RBF kernel and k-fold = 10. In addition, this research also employed gain ratio feature selection to minimize unnecessary attributes on the data. The accuracy of classification using feature selection was 72.5%. This number was 0.3 lower compared to the classification result without feature selection with the accuracy of 72.8%. However, the data classified using feature selection can reduce the process of classification model creation by 206 seconds.
Keywords:	Malware Android, SVM, Euphony, Gain Ratio, Virusshare, Virustotal
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	INFORMATION TECHNOLOGIES FOR THE SYNTHESIS OF RULE DATABASES OF AN INTELLIGENT LIGHTING CONTROL SYSTEM
Author:	VASYLYSHYN SVIATOSLAV, LAKHNO VALERII, ALIBIYEVA NURSULU, ALIBIYEVA ZHIBEK, SAUANOVA KLARA, PLESKACH VALENTYNA, LAKHNO MIROSLAV
Abstract:	The results on the development and research of information technology (IT) for the synthesis and optimization of effective rule databases (RDB) with an optimal set of consequents and an optimal number of rules for fuzzy systems of the Mamdani type are presented. The study of the information model of the structure of the intelligent lighting control system based on fuzzy logic is carried out. RDB study for Smart lighting system was carried out. The possibility of minimizing the number of rules for the Smart lighting system, their optimization is shown, which, as a result, makes it possible to significantly simplify the further hardware and software implementation of such a system for various customers.
Keywords:	Information Technology, Smart Lighting, Fuzzy Logic
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	MULTI-CLASSIFICATION MODEL FOR COVID-19 PREDICTION USING IMBALANCED X-RAY DATASET BASED ON TRANSFER LEARNING AND CLASS WEIGHTING-SMOTE METHOD
Author:	OLA M. EL ZEIN, MONA M. SOLIMAN , A. K. ELKHOLY , NEVEEN I. GHALI
Abstract:	This paper proposes a deep neural networks model to predict COVID-19 patients automatically based on chest X-ray images. The model is trained using imbalance dataset with a new hybrid balancing technique proposed to solve this problem. The Deep Convolutional Neural VGG-16 is trained and utilized to extract features from a given chest X-ray image after some preprocessing steps. To overcome the data imbalance issue, a new hybrid Class Weights-SMOTE is applied to the extracted feature vector and compared with traditional balancing techniques. The feature vector is then classified utilizing a Fine-tuning VGG-16. The model provides a multi-classification for the input x-ray images into COVID-19, Normal, and Pneumonia. Comparison with existing methods shows that the proposed model achieves a superior classification accuracy and outperforms all other models, providing 98% accurate prediction and improving the models performance on minority-class samples to achieve high accuracy 100%. The findings of this study could be useful for diagnosing COVID-19 from chest X-ray images.
Keywords:	Coronavirus, Gaussian filter, Imbalanced data, Class Weight, SMOTE
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	A NEURAL NETWORK BASED DATA MINING APPROACH FOR RECOGNITION OF CHRONIC KIDNEY DISEASE
Author:	RAVINDRA BV, NARENDRA VG ,SHIVAPRASAD G
Abstract:	Chronic kidney failure occur when the regular kidney filtration functionalities fails which leads to accumulation of electrolytes, wastes and other fluids in the body. One has to go appropriate dialysis procedure for their survival. It is very critical to recognize the level of chronic kidney disease (CKD) for the nephrologist and further dialysis period cannot be predicted appropriately for individuals. Data mining approaches have shown a promising path in the last decade to develop automated decision making tool for clinical diagnosis. This specific research suggests the application of neural network as critical qualitative indicator to mine the kidney dialysis attributes for classification of CKD from non-chronic kidney disease (NCKD). Two datasets one from open source UCI machine learning repository CKD database and other local hospital were considered for this study. Initially clustering was applied to remove the inconsistency from the datasets. Numerical and nominal normalized data was employed to multilayer perceptron neural network (MLPNN) to perform the classification of CKD and NCKD.MLPNN was configured optimally by appropriate network parameters and was evaluated in terms of Specificity, Sensitivity and classification accuracy. Further other classifier performance metrics, such as, position and negative predictions, error rate, F-Score, MCC and Kappa test were also evaluated. Experimental simulation shows that the proposed pattern classifier yields a classification accuracy of 93.22% and 92.78% respectively for the two different data sets.
Keywords:	Chronic Kidney Disease, Non-Chronic Kidney Disease, Multilayer Perceptron Neural Network, Classification
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	DATA DISTRIBUTION OPTIMIZATION OVER MULTI CLOUD STORAGE
Author:	SAIF SAAD ALNUAIMI, ELANKOVAN A SUNDARARAJAN, AND ABDUL HADI ABD RAHMAN
Abstract:	Cloud storage is an essential matter for people's organization and growth. Unfortunately, it is too risky if the data and files are hosted only on a single cloud storage provider. Meanwhile, a possibility exists for insider attack to steal or corrupt the data. Using multi-cloud storage providers and distributing the data over is a possible solution to improve data security in such a context. However, the performance of uploading speed of a cloud server provider plays an influential role. In this study, we used multi-cloud storage with the optimization parameters to speed up the uploading time spent to store data in several cloud storage services. Slicing the data and sending the contrasting amount of data over multi-cloud storage according to the optimization result can provide better security features and upload faster. This work considers the upload time and access latency parameter to implement the optimization model. Our finding shows a 12% enhancement in distribution performance compared to traditional data slicing without optimization, if equally sized slices are sent over multi-cloud storage. In future work, the effectiveness of bandwidth should be included, especially on the optimization parameters.
Keywords:	Distribution Optimization, Cloud Computing, Data Slicing, Multi-Cloud Storage.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	HYBRID GENETIC DISCRETIZATION MODEL WITH PARENTAL COMPARISON USING CORRELATION CLUSTERING FOR DISTRIBUTED DNA DATABASES
Author:	DR.VIJAY ARPUTHARAJ J, DR.M.ASHOK KUMAR, MR.M.PONSURESH, MS.PUSHPA REGA GANESAN
Abstract:	Parental comparison investigation is a technique for oppressing DNA grouping to precise strategies so as to realize the qualities character, setup, nature and attributes. Correlation Based Clustering and Modified Naive Bayesian Classification applied to quality succession information investigation, means to isolate ailing diabetic qualities from a huge stream of DNA quality arrangement components present in gathering of plentiful measurable information. This procedures endeavors to affirm, decide techniques and apparatuses for investigating sick quality successions. It likewise helps in characterization and translation of results precisely and seriously. This investigation is a mix of regulated and solo AI method for information examination. The grouping is finished by CBC though order done by MNBC procedures. It perceives quality articulations by confining affiliation rules as per bolster measure and certainty measure on the information informational collection. It will concentrate and channel required information into bunches dependent on CBC method in this way drafting affiliation rules. These are then applied on testing dataset to channel required (infected) quality groupings. At long last MLRC calculation is applied as order calculation to distinguish class marks of test qualities successions in a major dataset. This research has observed the results of three main phases. Phase-1 is concerned with Gene Sequence Analysis wherein analysis and classification of Basic Genetic Sequences (Introns and Exons) are done. Phase-2 of the studies deal with Medical Diagnosis - Disease Prediction which is associated to analysis and classification Protein Sequences that helps in disease prediction- mutation diabetics. Phase-3 is related to Parental Comparison during which analysis and classification of parental gene comparisons were carried out, this helps in forensic sciences.
Keywords:	Gene Sequence, Data mining, Classification, Correlation clustering, Parental Comparison.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	AN EFFICIENT ACCESS POLICY WITH MULTI-LINEAR SECRET-SHARING SCHEME IN CIPHERTEXT-POLICY ATTRIBUTE-BASED ENCRYPTION
Author:	ANCY P R, ADDAPALLI V N KRISHNA, BALACHANDRAN K, BALAMURUGAN M
Abstract:	Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a system in which attribute are used for users identity and data owner determine the access policy to the data to be encrypt. Here access policy are attached with the ciphertext. In the form of a monotone Boolean formula monotone access structure, an access policy can be interpreted and a linear secret-sharing scheme (LSSS) can be implemented. In recent CP-ABE schemes, LSSS is a matrix whose row represent attributes and there exist a general algorithm which is proposed by Lewko and Waters it transforms a Boolean formula into corresponding LSSS matrix. But we may want to transform the monotone Boolean formula to an analogous but compressed formula first before applying the algorithm. This is a very complex procedure and require efficient optimization algorithm for obtaining equivalent but smaller size Boolean formula. So in this paper we are introducing an extended LSSS called multi-linear secret-sharing scheme where we can eliminate above optimization algorithm and directly convert any Boolean formula to multi-linear secret-sharing scheme.
Keywords:	Ciphertext Policy Attribute Based Encryption, Access Policy, Multi-Linear Secret-Sharing Scheme, Encryption
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	A LITERATURE REVIEW OF VARIOUS STEGANOGRAPHY METHODS
Author:	MUSTAFA MUNEEB TAHER, ABD RAHIM BIN HJ AHMAD, RANA SAMI HAMEED, SITI SALASIAH MOKRI
Abstract:	Nowadays, the volume of data shared over the Internet is growing. As a result, data security is referred to as a major issue while processing data communications through the Internet. During communication procedures, everyone requires their data to remain secure. Steganography is the science and art of embedding audio, message, video, or image into another audio, image, video, or message to conceal it. It is used to secure confidential information from harmful attacks. This research offers a classification of digital steganography based on cover object categories, as well as a classification of steganalysis art. Image visual quality, structural similarity (SSIM), mean square error, Image Fidelity (IF), payload capacity, Normalized Cross-Correlation (NCC), and robustness are some of the important aspects of steganography. Researchers have made tremendous advances in the realm of digital steganography. Nonetheless, it is vital to emphasize the advantages and disadvantages of modern steganography techniques. The purpose of this research is to examine and compare several steganography methods using characteristics such as PSNR, MSE, and Robustness. This study arrived at 15 possible research directions for developing high-quality stego objects, strong stenography techniques, and high payload based on the analysis of the studied parameters.
Keywords:	Information hidings, Audio Steganography, Image Steganography, Video Steganography, DNA Steganography, Network Steganography.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	CYBER VIOLENCE AND BULLYING IN ONLINE GAME ADDICTION: A PHENOMENOLOGICAL STUDY
Author:	Z. HIDAYAT, CHRISTINA BEATRIX PERMATASARI, LA MANI
Abstract:	Addiction to online games is increasingly widespread and evenly experienced by teenagers, children, and even adults. This study explores and analyzes violent content in online games and cyberbullying on social media. A qualitative approach with a phenomenological method was carried out to determine the depth of individual experiences in the student community who play online games in cafes, at home, and on mobile. Interviews and observations were conducted on 22 participants in various internet cafes in Jakarta. The semi-structured interview at the beginning of data collection aims to identify the participants' addiction levels. Then, in-depth interviews were conducted with participants who experienced high average online games addiction. The results show that adolescents with a high level of online game addiction do cyberbully and become victims of cyberbullying from others. Cyberbullying perpetrators are exposed to violence in online games and carry out verbal violence either directly or through chatting on social media to other people. Online verbal violence is carried out and received from their peers, and they even experience physical harassment and violence in their association. The implications of this study recommend solutions for educators and families to anticipate types of online games for children and adolescents.
Keywords:	Adolescence, Cyberbullying, Internet addiction, Media violence, Online games
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	ANALYSIS OF GAMIFICATION FOR TRAINER PERFORMANCE USING MDA FRAMEWORK AND ARCS MODEL
Author:	WENNY, GUNAWAN WANG
Abstract:	In the current era of knowledge-based technology development, human resources are an essential and valuable asset for the company, so it is necessary to manage them properly and not cause a decrease in productivity. Gamification has been widely used to increase user motivation and performance in various fields, such as education and health. The latest trend is the application of gamification of work, which aims to incorporate game design elements into the workplace to increase employee productivity and work motivation. This study will analyze problems related to the absence of a standard trainer rating system, making it difficult for managers to find trainer profiles that match the training to be held. The gamification method combines the MDA framework and the ARCS motivation model to analyze relevant game elements to improve coach performance and assist administrators in assigning training to coaches according to the criteria. The analysis in this study mapping the Mechanics Dynamics Aesthetics of the MDA framework with twelve ARCS (Attention, Relevance, Confidence, Satisfaction) subcategories. The results obtained from the relevant mechanics game elements are Achievements (Badges, Rewards), Points (Score), Leaderboard, Level System, Progress Bar, Mission (Task), Tutorial, Feedback, and Chat.
Keywords:	Gamification, MDA Framework, ARCS Model, Trainer Performance, Knowledge Management
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	THE FORMATION OF INTERCULTURAL COMPETENCE BASED ON INFORMATION AND ANALYTICAL TECHNOLOGIES
Author:	AINAGUL ZHUNUSSOVA, ABRAHAM ALTHONAYAN , ALEFTINA GOLOVCHUN , ERKAN KULEKCI
Abstract:	The choice of a profession is a crucial part of our life. It can be claimed that nowadays many new professions have appeared connected with information and data analysis. Besides, one should take into account the importance of intercultural and communicative competence (ICC) and emphasize the need to consider ICC based on the information and analytical technologies as a positive influence for the future career. The aim of this research is to investigate the cognitive and communicative process of foreign language education at the profile or specialized level of a secondary general school. In this regard we set specific tasks to determine the nomenclature of sub-competencies that are part of the ICC of specialized schools students; draw up a system of communicative tasks aiming at the formation of ICC of specialized schools students; substantiate the relevance of integrating information and analytical technologies in the formation of specialized schools students' ICC. In this paper, we demonstrate a model for the formation of intercultural and communicative competence of specialized school students based on the information and analytical technologies. All approaches, stages, and experimental techniques conducted at a specialized school in the city of Karaganda are detailed.
Keywords:	Intercultural competence, Communication, Specialized schools, Students, Model, STEM.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	THE INFLUENCE OF MOBILE APPLICATION-BASED LABORATORY INFORMATION MANAGEMENT SYSTEMS ON CUSTOMER LOYALTY
Author:	HERDIANSYAH, Ir. TOGAR ALAM NAPITUPULU
Abstract:	The trend of mobile application development is not only focused on the business to consumers (B2C) industry dominated by mobile e-commerce applications. Still, it has also become a trend in the business-to-business (B2B) industrial sector. Several studies show the significant influence of this mobile application on customer loyalty. As a part of the business-to-business (B2B) industry, the third-party accredited laboratory services have the challenge to maintain and manage customer loyalty in today's era of intense competition. Nowadays, the Laboratory Information Management System (LIMS) function is not just testing operational tools from the sample receiving to testing report. More than that, it must have the ability as a customer relationship management tool. Therefore, one of the LIMS optimizations is providing a mobile application version. This study aims to identify the influence of a mobile application-based Laboratory Management Information System on customer loyalty as one of the accredited third-party testing laboratory solutions, primarily to manage customer retention. The study shows that the variable information accessibility and customer engagement of mobile application-based Laboratory Management Information System significantly affect customer loyalty.
Keywords:	Lims, Mobile Application, B2b, Customer Loyalty
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	ITEM DEVELOPMENT FOR SOCIAL TAGGING IN THE STUDY OF USAGE IN SOCIAL MEDIA COMMUNITY
Author:	MAZWANI AYU MAZLAN, MOHD SAZILI BIN SHAHIBI, MOHD RIDWAN SEMAN@KAMARUZZAMAN
Abstract:	Recent research has shown that extensive use of tagging in social media or better known by social metadata leads to emergent semantics. Much has been learnt in recent years about how to capture the data provided by taggers for the purpose of visibility and needs to trending that beneficial as marketing for businesses and public or targeted engagement such as social media community. However, little progress has been made on other issues, such as understanding the usage of tagging within specified community. which is essential for, among others, identifying social media usage relationships between concepts, this study intended to address that void. Starting from a review of metadata definitions to social media users, introduce validity of items created for the framework, applying partial least squares structural (PLS) to measure the level composite reliability, variance inflation factor (VIF), and Heterotrait-monotrait ratio (HTMT) of items of social metadata dimension. Evaluation are done by comparing with grounded measures. Results suggest that the generality of tags in social tagging systems as social metadata can be approximated with simple measurement of the newly created items. The discussion of the results leads to discovery of major findings entrench with the hypothesises.
Keywords:	Social Metadata, Tagging Data, Social Media Tagging, Social Tagging, Hashtag
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	A CRITICAL REVIEW OF DEEP LEARNING ALGORITHM IN ASSOCIATION RULE MINING
Author:	WAN AEZWANI WAN ABU BAKAR, MUHAMAD AMIERUSYAHMI ZUHAIRI, MUSTAFA MAN, JULAILY AIDA JUSOH, YAYA SUDARYA TRIANA
Abstract:	Data mining, an urging requirement within the current era and whose scope of research is predicted to be for upcoming decades. Among the competent techniques of data mining association rule mining plays an amazing role. This technique indicates on curious association, correlations and frequent patterns from the given data sources to be mined. The primary goal of association mining is to find common patterns and investigate association rules. There are a variety of association rule mining algorithms available, each with its own set of performance factors. Advanced of the association rules data structure format based on horizontal or vertical. Both structure formats are extensively applied in several association rule algorithm to attain the least execution time and minimum memory consumption. One of the established algorithms for association is Equivalence Class Transformation (Eclat). Deep learning (DL) has exploded as the current technology for mining of large amount of data from sources such as social media, internet, e-commerce, and online movie theatres. This massive volume of information is easily accessible and can share via cloud computing. In response to big data mining issues, the DL algorithm is recognized to be the most potential techniques when it reaches to association rule pattern generation. In this paper, we reviewed and analyzed the fundamental Eclat algorithm in DL. These reviews would determine some alternative approaches of deep learning techniques may be adopted in Eclat to boost the least execution time and reduce memory.
Keywords:	Association Rule Mining; Deep learning (DL), Eclat algorithm, Frequent itemset
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	EXPLORATORY BIG DATA STATISTICAL ANALYSIS THE IMPACT OF PEOPLE LIFES CHARACTERISTICS ON THEIR EDUCATIONAL LEVEL
Author:	SALWA ZAKI ABD ELHADY, NEVEEN I. GHALI , AFAF ABO-ELFETOH, AMIRA M. IDREES
Abstract:	Big data and cloud computing become the most important technologies in the most of fields over the world. That because their wide range of applicability in the main drivers of our life. Education is one of the most significant big data application area. Although there are many researches of analyzing big data in education sector as tools, performance and methods of teaching and how to enhancement them in all levels of education, there is absence of studies for applying big data analysis in census education data to study the impact of other people life's characteristics on their educational level. This research paper introduces exploratory big data analysis methods for categorical variables using python language to analyze the educational data in Egypt census (2017), and discuss the relations between educational data features as indicators of educational levels of Egyptians in the future. The implementation of exploratory big data analysis module (EBDA) displayed in this paper and the regression model used as a traditional statistical method to categorical data analysis, Also, there is comparison between two results. This exploration of data analysis considered as the step for predictions, suggestions and recommendations of enhancement people educational level.
Keywords:	Big Data, Educational Data, Exploratory Analysis, Census Data, Python, Categorical Variables, Association Rules.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	IMPACT OF SCM SYSTEM OPERATION STRATEGY ON SCM PERFORMANCE AND MEDIATING EFFECT OF PROCESS INNOVATION
Author:	SU SUNG JEON, ROK LEE
Abstract:	This study aims to identify the influence of the supply chain management (SCM) system operating strategy on process innovation and SCM performance. To this end, this research empirically investigated a total of 243 small and medium businesses in South Korea that executed a process innovation program. The introduction of SCM systems and process innovation positively impacted SCM performance. Furthermore, a positive impact in the mediating effect of process innovation on SCM performance was validated. The results indicate that introducing and applying systems for optimized logistics management, as in supplier-initiated stock management and enterprise resource planning, boosted innovation between different processes, which ultimately positively impacted SCM and simultaneously contributed to improving logistics management functions and performance enhancement. This finding empirically proved that applying an integrated logistics between each system rather than isolated synching and application in a single system, as was the case in the past could develop an interlinked synergy effect, thereby promoting innovation in logistics between different processes and subsequently improving SCM performance.
Keywords:	Supply Chain Management, Operation, Strategy, Process Innovation, Performance
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	DEMYSTIFYING DARK DATA CHARACTERISTICS IN SMALL AND MEDIUM ENTERPRISES: A MALAYSIAN EXPERIENCE
Author:	AHMAD FUZI MD AJIS, SOHAIMI ZAKARIA, ABDUL RAHMAN AHMAD
Abstract:	The paper reports a study on the dark data phenomenon experienced by Small & Medium Enterprise in Malaysia in relation to characteristics of dark data from SME perspectives. Qualitative research was conducted upon 13 cases which derived from the Inductive Grounded & Emergent Theory Sampling, implemented to identify appropriate samples for the study. Data was collected using semi-structured interviews which were recorded, transcribed, and analyzed using the Grounded Theory Methodology. The research findings highlighted the characteristics and types of dark data typically resides in SME repositories. The study further shows that the field is lacking in literature on dealing with dark data which depicts dark data epistemology are still evolving. Thus, based on Malaysian SMEs' experience a theory was suggested to demystify dark data management.
Keywords:	Dark Data Types, Dark Data Characteristics, Dark Data Management, Grounded Theory, Malaysia.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	INFORMATION SECURITY POLICY COMPLIANCE BEHAVIOR MODELS, THEORIES, AND INFLUENCING FACTORS: A SYSTEMATIC LITERATURE REVIEW
Author:	PUSPADEVI KUPPUSAMY, GANTHAN NARAYANA SAMY, NURAZEAN MAAROP, BHARANIDHARAN SHANMUGAM, SUNDRESAN PERUMAL
Abstract:	The paper aims to identify information security policy compliance behavior models, their respected theories, and influencing factors. This is the first and most current comprehensive systematic review of information security policy compliance models, theories, and influencing factors. A systematic review of empirical studies from twelve online databases was conducted. This review resulted in thirty-two (32) information security policy compliance behavior models proposed in different domains comprising various theories, concepts, and influencing factors. The results showed the importance of this issue among the researchers and a major limitation found was generalizability. Twenty (20) primary theories were extracted from the identified studies and found the theory of planned behavior and the protection motivation theory are the most trusted and reliable theories in information security policy compliance behavior models. Further analyses identified sixty (60) influencing factors and their alternative names and definitions. The most promising factors (high usage) of importance in descending orders are subjective norms, self-efficacy, attitudes, perceived benefits, threat vulnerability, threat severity, response efficacy, response cost, and experience. Besides that, factors such as self-efficacy, attitude, perceived benefit, threat severity, response efficacy, sanction severity, personal norms, experience, and training support were found and proved to be positively associated with the intention of compliance and considered robust for increasing information security compliance intention behavior. The results of this research can offer valuable information to fellow researchers in listing the models, their limitations, theories that are trustable, and influence factors that are critical for building a better model in the future.
Keywords:	Information Security Policy, Cybersecurity Policy; Security Compliance; Security Behavior; Systematic Literature Review
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	BROADBAND SPECTRUM CHANNELS SENSING FOR CO-OPERATIVE NETWORK USING SUB SAMPLING SUBSPACE ESTIMATION APPROACH
Author:	ANAND RANJAN, O.P. SINGH, HIMANSHU KATIYAR
Abstract:	Focus of this article on the scheme of sensing of spectrum for co-operative communication for cognitive radio applications to face issues of sensing of spectrum at sampling rate with high values signal having broadband. Under broadband sensing of the spectrum scenario a model developed in for applying concept of a scheme based on sub sampling for reducing the need of high sampling frequency in sensing of spectrum. Noise added to transmission antenna side and the finite samples correlation evaluation performed for finding the channels occupancy in estimation of subspace of spectrum.
Keywords:	Co-Operative Network, Sensing Of Spectrum, Allocation Of Band, Cognitive Radio
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	LOW-COST ENERGY HARVESTING FOR SMART GARNUS
Author:	MOCHAMMAD HALDI WIDIANTO, ANNISA ISTIQOMAH ARRAHMAH, SAMUEL JASON SANTOSA, GREGORIO KURNIAWAN
Abstract:	Recently, several countries have experienced an energy crisis due to a lack of energy supply. one of them is the low power-based Garden Binus (GARNUS) system in energy harvesting. This Study is used to determine the energy consumption needs of the Internet of Things (IoT). One method of harvesting energy uses solar cells because of their availability during the day. Energy harvesting can be used as a source to enable the Internet of Things (IoT) systems, especially using low-cost materials. Several previous studies did not use IoT tools based on energy harvesting by utilizing the increment waterfall method to make low-cost solar cell-based energy harvesting. It is still rare for research to combine IoT such as Smart GARNUS tools with platforms that produce their own energy or what is called energy harvesting, especially low-cost energy harvesting for $8. This paper will provide many benefits: the use of IoT devices combined with low-cost energy harvesting (IoT with clean resources) and some energy demand data on IoT devices. This data can be used for future IoT studies. The aim of this paper is the application of low-cost energy harvesting to operate the Smart GARNUS platform. Energy harvesting using solar cells can charge up to 1,88 Wh of free energy from sunlight. At the same time, the IoT power requirement of the Smart GARNUS is 0.33. So it can meet the energy needs of more than 5 hours. This is expected to help this study in caring for support ornamental plants and support renewable energy processes.
Keywords:	Energy Harvesting, Smart GARNUS, IoT, Low-Cost
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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Title:	BLOCKCHAIN-BASED E-VOTING SYSTEM FOR ELECTIONS IN JORDAN
Author:	SARAH AL-MAAITAH, ABDULLAH QUZMAR, MOHAMMAD QATAWNEH
Abstract:	Any democracy must have a clear voting system that fits the demands of the people in order to deliver power to the proper person. Furthermore, current traditional voting methods have significant flaws, including a lack of security and transparency. This paper looks at how Blockchain (BC) technology may be used in E-voting systems to improve the voting process by addressing concerns like trust, privacy, and security. The proposed system uses a Hyper Ledger Fabric as a platform for creating Blockchain-based apps, software, and services with plug-and-play components including consensus, privacy, and membership services. We have analyzed the latency, response time, and throughput to make sure the system is performing well. As an outcome of the proposed system, we realized that proposed framework exceeds any other system in performance situations.
Keywords:	E-Voting, Blockchain, Voters, Voting System, Securit, Hyper Ledger Fabric.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2022 -- Vol. 100. No. 05 -- 2022
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