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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
September 2020 | Vol. 98 No.17

Title:	ENSEMBLE ADABOOST IN CLASSIFICATION AND REGRESSION TREES TO OVERCOME CLASS IMBALANCE IN CREDIT STATUS OF BANK CUSTOMERS
Author:	ACHMAD EFENDI, RAHMA FITRIANI, HAFIZH IMAN NAUFAL, BAYU RAHAYUDI
Abstract:	The Classification and Regression Trees (CART) is a popular classification method. Generally, at a bank, debtors who have delinquent loans (Non-performed Loan/NPL) have a small proportion compared to debtors who have smooth loan (Performed Loan/PL). Standard classification methods CART is not suitable for handling such cases as it is sensitive to classes with a high degree. Hence, additional methods are needed in order to improve classification accuracy in the case of class imbalance. This study aims at determining the results of the classification using the CART and Adaptive Boosting (Adaboost) CART methods on bank loan or credit collectability data where there is class imbalance. The data used for analysis are secondary data in the form of bank debtor credit collectability data with 9 predictor variables and one response variable. Simulations are also conducted to find out the consistency of the results of analysis and general performance of Adaboost CART. The results of this study indicate the accuracy of the classification on the Adaboost CART method can be increased compared to the CART method. This implies that Adaboost can add weights to classifiers which have small misclassifications and can reduce weights on the correctly classified objects. This research can be taken into consideration in choosing the right classification analysis in the case of data with class imbalance. Simulation results confirm that the classification accuracy of Adaboost CART is relatively large, 84.1%.
Keywords:	Adaboost, Classification and Regression Tree (CART), Class Imbalance, Credit, Bank
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	A KEY POINT ALGORITHM IMPROVING INDOOR POSITIONING ACCURACY BASED ON BEACONS
Author:	QIANFENG LIN, JOOYOUNG SON
Abstract:	The indoor positioning technology in this paper identifies real-time positions of the crew in ships. It can be used to get the regional personnel distribution and find timely locations of accidents. Therefore, the accuracy is very important when estimating positions in ships. This research considers the nearest reference point and beacon in ships as key points for improving the accuracy. We look for three key points to predict a user position. Firstly, KNN algorithm is used to get the first point P1. Secondly, the nearest reference point to the nearest beacon is the second point P2. Finally, the weighted centroid point of nearest beacons is the third point P3. The centroid position of these three key points is the predicted user position. Experimental results show the accuracy has been improved by at most 54%.
Keywords:	Indoor Positioning, Key Point, Reference Point, In-ship Position, Crew Safety
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	SMART-ETL-MR: NOVEL ETL FRAMEWORK FOR BUILDING DATA WAREHOUSE FROM BIG DATA SOURCE USING MAPREDUCE
Author:	ABDELJALIL BOUMLIK, NASSIMA SOUSSI, MOHAMED BAHAJ
Abstract:	The concept of Big Data created to face the massive explosion of data produced from web 2.0, smart devices, sensors, social networks platforms like Facebook, Twitter, Instagram, LinkedIn, has increased continuously. However, new challenges and opportunities appear due to the growth of data. Nevertheless, several prominent organizations and companies have realized that data is valuable and offers competitive advantages, great benefits, and relevant knowledge when it gets converted to actionable information they can use. However, collecting these massive data is not enough, as we should be able to integrate and analyze these data pulled from different heterogeneous sources after loading them to improve analysis goals. This research article's primary objective is to adapt the ETL (extraction transformation-loading) processes with the potential of Big Data technologies in order to deal with these new challenges from data warehousing perspective and knowledge discovery that directly impacts business decision-making systems. In this article, a new approach based called SMART-ETL-MR presented on the Map-Reduce paradigm to expedite data handling and to build a well-organized data warehouse. Experimental results prove that the ETL operation performs successfully with optimal algorithms.
Keywords:	Big Data, ETL, HBase, Map Reduce, Data Warehouse, Facebook
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	GENETIC ALGORITHM APPROACH FOR AUTOMATED GENERATION OF NEURAL NETWORKS ARCHITECTURE FOR ROBUST DIGIT RECOGNITION
Author:	RULLY SOELAIMAN, YOLANDA HERTITA PRATAMA, M.M. IRFAN SUBAKTI, YUDHI PURWANANTO
Abstract:	Digit recognition is a special part of research in Optical Character Recognition. It is a common technique to recognize the numeric characters from printed images. A solutionâ€™s design for the digit recognition problem on a case study of SPOJ Hard Image Recognition (HIR) has been proposed in this paper. The case studyâ€™s problem has challenging constraints such as runtime limit and source code limit and it has many noisy images. An artificial neural network has been implemented to solve this problem in consideration of its simplicity yet powerful enough algorithm. The selection of best ANN architecture is commonly achieved through trial and error process, which is a very time-consuming process. This paper also provides the use of a Genetic Algorithm to determine the architecture of ANN automatically. The creation of the dataset also has an important role to improve the accuracy. The proposed architecture development successfully passed the challenging constraints and achieved a high score of 108 at SPOJ HIR. The score obtained by using GA is higher than our predetermined ANN architecture.
Keywords:	Artificial Neural Network, Digit Recognition, Genetic Algorithm, Optimization, Pattern Classification
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	MODALITY CONFLICT DETECTION MODEL WITH AUTHORIZATION PROPAGATION IN POLICY EVALUATION
Author:	TEO POH KUANG , HAMIDAH IBRAHIM , FATIMAH SIDI , AND NUR IZURA UDZIR
Abstract:	Modality conflict is one of the main issues in policy evaluation. Existing modality conflict detection approaches do not consider complex condition attributes such as spatial and temporal constraints. In this paper, a modality conflict detection model is proposed to identify the applicable policies during policy evaluation, which supports an authorization propagation rule to investigate the class-subclass relationships of a subject, resource, action, and location of a request and a policy. We have evaluated the effectiveness of our proposed modality conflict detection model on real XACML policies for university, conference management, and health-care domain. Overall, our solution achieved higher percentage of P, R, and F in retrieving the applicable policies and in detecting modality conflict as compared to the previous work.
Keywords:	Modality Conflict, Authorization Propagation, Policy Evaluation, Spatial and Temporal Constraints, Distributed Environment
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	INNOVATIVE ARCHITECTURE BASED ON BIG DATA AND GENETIC ALGORITHM FOR TRANSPORT LOGISTICS OPTIMIZATION
Author:	Z. MOUAMMINE, A. AMMOUMOU , B. NSIRI , S. BOUREKKADI
Abstract:	Many studies were carried out in the smart city field to improve the integration or to optimize the intelligent transport systems (ITS) in business. Yet, there were very few researches studying how ITS can still work outside of the context of a smart city. Taking this issue into consideration, this study was carried out to suggest a new system allowing shipping companies to keep working with their ITS even outside of the context of Smart city based on Genetic algorithm, Big Data, and multi-agent architecture. This is possible through implementing a transport information system, which will provide and exchange data about the optimum path to follow with vehicles (traveling vehicle problem) on the road. Theoretically speaking, this proposed system accelerates the processing time and enhances the quality of the obtained result with the Genetic algorithm to solve Salesman problem.
Keywords:	Distribution Logistics, Intelligent Transport Systems, Traveling Salesman Problem, Genetic Algorithm, Big Data, Smart City.
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	IMPROVING RECOMMENDATION ACCURACY AND DIVERSITY USING NETWORK EMBEDDING METHOD
Author:	NADIA BUFARDI, HAYTAM HMAMI, ABDELHADI FENNAN, ABDELOUAHID LYHYAOUI
Abstract:	In recent years, diversity in recommender systems have become increasingly an essential dimension for evaluating the effectiveness of recommendations. However, many existing recommendation techniques are challenged by information overload with the widespread use of recommender systems in many real-world applications. In this paper, we propose a new diversified recommendation approach, namely DRN2V, based on rich constructed graphs and Network Embedding technology. Specifically, we construct a knowledge graph of two sub-graphs, the User-Item subgraph that represents the interactions between users and items and the Item-Category subgraph which uses the item categorization to enrich the network structure. Afterwards, we use Node2vec algorithm to capture the complex latent relationships between users and items from the constructed knowledge graph. Moreover, to propose personalized and relevant predictions for each user, a new formula was proposed based on category coverage and users' preferences for categories. The experimental results demonstrate the significant outperforms of our approach over several embedding-based methods and recommendation algorithms including both traditional and diversity-oriented algorithms) regarding accuracy and diversity.
Keywords:	Recommender system, Accuracy, Diversity, Network embedding, Node2vec
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	ADAPTIVE LEARNING SCHEME FOR THE VIRTUALIZATION OF A ROTARY SERVO BASE UNIT
Author:	HOLMAN MONTIEL A., FREDY H. MARTÃNEZ S., EDWAR JACINTO G.
Abstract:	A system is a structure composed of mechanical, electrical or electromechanical parts that interact with each other to fulfill an objective. At an industrial level they are known as manufacturing processes and at an academic level these processes are emulated by implementing mechanisms such as: designing scale prototypes, building test and trial laboratories or developing specialized simulators. However, the efforts made by the authors to build scale or simulated prototypes that are an ideal copy of the real process are not perfect. On the one hand, some real physical implementations reduce the margin of error by improving the quality of the prototype materials, but as the quality of the materials increases, so does their cost, which reduces accessibility to the population. On the other hand, the simulators do not perfectly emulate characteristics of the environment, such as humidity, temperature, vibrations, among others, which reduces its reliability in the presentation of the results obtained. Therefore, this article proposes a strategy to virtualize a QUANSER SRV-02 rotary servo base unit, which from experimental data reconstructs a mathematical model using a Genetic Algorithm (GA), which minimizes the margin of error between experimental and practical data. This tool will allow virtual practices (simulation) with results very close to the behavior of the real plant.
Keywords:	Genetic Algorithm; ARIMA Models; Servomechanisms; Virtualization; Prototypes.
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	AN EFFICIENT SELF-MAPPING ALGORITHM FOR HEXAGON-BASED GRIDDING IN AD HOC NETWORKS
Author:	MOHAMMAD M. QABAJEH, LIANA K. QABAJEH
Abstract:	Mobile Ad hoc NETwork (MANET) is a multi-hop autonomous network formed exclusively among a collection of mobile nodes without any centralized infrastructure. MANETs are generally unstable; the alternative for this non-infrastructure topology is to construct a virtual infrastructure. Using virtual clustering helps in creating an infrastructure for MANET to simplify routing and network management. Network terrain may be portioned into numerous shapes to support scalable routing. Hexagon-based gridding outperforms other gridding shapes; due to its geometric features. However, cell coordinate assignment and mapping node physical location into grid map is an important aspect. In this paper, we introduce a self-mapping algorithm to enable each mobile node to be aware of the precise cell it belongs to during the network lifetime without the need to communicate with other nodes. This algorithm is a core part of the position-based unicast and multicast routing protocols that rely on virtual hexagon infrastructure. Our algorithm has been developed to simplify routing discovery in large-scale MANETs and to ensure that the overhead is as low as possible.
Keywords:	MANETs, Position-based, Routing, GPS, Self-mapping, Multicast Routing, Unicast Routing
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	BLOCKCHAIN-ENABLED SMART UNIVERSITY: A FRAMEWORK
Author:	RANIA A. ABOUGALALA, MOHAMED A. AMASHA, MARWA F. AREED, SALEM ALKHALAF, DALIA KHAIRY
Abstract:	Digitizing has become a necessity for universities to develop and improve their services to facilitate access for beneficiaries. Utilizing new technologies helps universities be more effective, efficient, flexible, and comfortable. Smart university, a concept that represents higher education in the digital age, has increased these considerations greatly, and blockchain is one of the fastest emerging technologies in this era. This paper discusses the definitions of blockchain and smart university and it also presents a conceptual framework to illustrate how smart universities can use blockchain technology to support and ensure a better understanding of the involvement their students with the university. Furthermore, it presents two case studies using the formative assessment to show the impact on the quality of education and the assessment of academic supervision on scientific theses. A seven-tier framework of formative assessment in smart universities will be used to improve the quality of education.
Keywords:	Smart University, Blockchain, Distributed Database, Smart University , Digitizing
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	DEVELOPMENT OF ADAPTIVE LOAD REGULATOR FOR SHEARER ELECTRIC DRIVE, PROVIDING MAXIMUM RESPONSE TIME OF CONTROL SYSTEM
Author:	SHPREKHER D.M., BABOKIN G.I., ZELENKOV A.V.
Abstract:	Using the Matlab/Simulink software package a mathematical model of the electromechanical system of a shearer with an built-in moving system has developed. It takes into account all the main factors that determine its operating modes: electro-magnetic transients in drive motors for cutting and moving; dynamic forces in transmissions; the distribution of the cutting force between the cutting drive and drive for moving, the nature of the change in the moment of resistance forces during operation by changing the strength of the coal. The shearer control system contains a load regulator with a PI-controller. However, it was found that, despite the simplicity of the configuration and physical implementation, as well as the relatively high reliability, this class of control devices may not provide optimal operation of the control system in all modes due to the non-linearity of the control object and the random nature of the coal force changes as the shearer moves in the coal face. To overcome these shortcomings, the possibility of a neural-network implementation of correction of the PI-controller coefficients is considered. The possibility of the correction of the PI-controller coefficients controlling the speed of the shearer movement, with a random nature of the coal strength changing, is proposed and experimentally proved. It is shown that the use of the PI-controller with the corrector in the form of the neural network in the control system will increase performance of the load regulator by an average of 1.5â€“3 times in comparison with the classical regulator. All this will allow to avoid critical overloads, and hence the possible breakdown of the mechanical parts in the transmission of the shearer in case of a sudden collision of the executive body of the shearer with a solid inclusion.
Keywords:	Shearer, cutting drive, coal strength, neural network, PI-controller
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	SOME APPROACHES TO ASSESSING THE QUALITY OF MASKING NOISE INTERFERENCE OF SPATIAL NOISE GENERATORS
Author:	NURZHIGIT SMAILOV, ASKHAT BATYRGALIYEV, NURGUL SEILOVA, AINUR KUTTYBAEVA, AIDOS IBRAYEV
Abstract:	The article discusses the characteristics of spatial electromagnetic noise generators and the formation of a broadband noise signal. It also describes a number of known methods and methods for assessing the quality of masking noise interference and their differences. Different approaches to measuring masking noise when evaluating its quality are proposed. The first method is based on the measurement of the instantaneous values of the amplitudes of the noise signal and the calculation of the entropy coefficient based on this method. The second method involves searching for correlation of masking noise signals of noise generators in different frequency subbands. The third approach is to use statistical and (or) graphical methods (tests) for randomness. The completeness and objectivity of assessing the quality of masking noise interference from spatial noise generators will be achieved by combining all the methods.
Keywords:	noise generators, masking noise, TEMPEST, noise quality rating, electromagnetic radiation
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	AUTHORSHIP AUTHENTICATION OF POLITICAL ARABIC ARTICLES BASED ON MODIFIED TF-IGF ALGORITHM
Author:	HEBA M . KHALIL, AHMED TAHA, TAREK . EL-SHISTAWY
Abstract:	Recently, authorship forensic analysis for political articles has become very important. It is the process in which a linguist attempts to identify the author of an anonymous text based on the vocabulary used and the linguistic style of the writer. The most existing studies of authorship forensic analysis focus on the English language, while researches concerning the Arabic language is rare. In this research, we present a new methodology that enhances authorship forensic analysis focusing on the Arabic language. The basic idea is to extract the unique vocabulary terms identifying the author (or a political group) and used for recognition of unknown authors. In the current work, a Term Frequency- Inverse Group Frequency (TF-IGF) is proposed, which is a modification of the traditional TF-IDF method. Our approach is tested with large political dataset and determine the performance of Authorship forensic analysis method based on vocabulary words.The experimental results show that the average accuracy for recognizing groups has increased from 89.33 % when using TF-IDF, to 92% with the proposed TF-IGF. Further improvement is achieved when representing the vocabulary terms in its Arabic lemma form, rather than its root form. The results show that the accuracy is improved from 89.33 % to 92%.
Keywords:	Authorship Forensic Analysis, TFIDF, Term Weighting, Linguistic Style.
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	THE INFLUENCE OF TRUST, PERCEIVED USEFULNESS, AND PERCEIVED EASE UPON CUSTOMERSâ€™ ATTITUDE AND INTENTION TOWARD THE USE OF MOBILE BANKING IN JAKARTA
Author:	MOHAMAD SAPARUDIN , AGUS RAHAYU , RATIH HURRIYATI , MOKH. ADIB SULTAN
Abstract:	The rapid development of information technology has encouraged the banking industry to innovate in serving its customers. One of the most phenomenal technology service facilities is m-banking. This could be seen from the use of mobile banking is steeply increasing in recent years since almost every bank offers mobile banking services. In response to this fact, this study aims at examining the influence of trust, perceived benefits and perceived ease upon attitudes and intention of customers to use mobile banking. This study used technological acceptance model (TAM) with trust. The collection of data was conducted through a survey-based empirical study of 150 of respondents using convenience sampling. The result of study shows that attitude highly influences the intention to use mobile banking and attitude, as mediation, is influenced by perceived benefits, ease and trust.
Keywords:	Trust, Perceived Usefulness, Perceived Ease, Attitude, Intention.
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	VIRTUAL LEARNING ENVIRONMENT AND LEARNING STYLES
Author:	HHANI HOSNI, MOHAMED EL-DOSUKY , MOHAMED EISA
Abstract:	Virtual Learning Environments are computer based Learning environment that offer online learning opportunities to learn. The paper overviews VLEs before proposing a new VLE, that is capable of determining the learning styles of students (visual, tactile, auditory). This helps the system in recommending study materials to each student.
Keywords:	Virtual learning environment (VLE), Learning styles (LS), Learning management systems (LMS)
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	THE EFFECT OF COGNITIVE FACTORS IN DETERMINING STUDENTâ€™S SUCCESS IN COMPUTER PROGRAMMING
Author:	MURIMO BETHEL MUTANGA
Abstract:	There is a growing reliance on technology as the core driver of the 4th industrial revolution. This trend not only delineates Information Technology (IT) as a key topic of global discussion but also makes programming the most rapidly growing skills required by employers. Also, on the academic front, it challenges the capability of current curricula to produce competent IT graduates armed with the right skill-set to meet the surging demand for IT professionals. Moreover, addressing this challenge goes beyond designing a university curriculum for fields that offer IT courses with a computer programming component because unlike other subjects, students often have little to no experience with computer programming before arriving at the university. Consequently, teaching and learning computer programming becomes more challenging than other subjects, and aside from the direct result in poor studentsâ€™ academic performance, fewer students also master the skill. Generally, the debate on improving studentâ€™s academic performance has inspired a myriad of investigations into factors with correlative impact. However, while literature significantly links studentâ€™s academic performance to the impact of cognatic factors, there is still a need to investigate the impact of cognition on subjects. Such investigation has the potential to contribute toward enhancing curriculum development and inform approaches to teaching and learning. Therefore, in this paper, we investigated the effect of cognitive factors on studentsâ€™ performance in introductory programming. Using a case study of undergraduate students at a South African University of Technology, our findings show that enhancing cognitive abilities leads to greater performance in introductory programming. More so, personal motivation was found to be the core driving force behind developing and enhancing cognitive ability.
Keywords:	Cognitive factors, Cognitive performance, Programming, Curriculum, Learning
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	A SYSTEMATIC MAPPING STUDY ON SOFTWARE EFFORT ESTIMATION
Author:	AHLAM ALHADDAD , IBRAHIm Al-BALTAH , ABEDALLAH ABUALKISHIK , MAJDI ABDELLATIEF , ABDULMONEM ALI AL KHARUSI
Abstract:	Context: software effort estimation has been considered as one of the key drivers in software development success. A comprehensive understanding of state-of-the-art of software effort estimation techniques is very important. Objective: the aim of this study is to identify and characterize the existing software effort estimation techniques and to points insights of this research field. Method: a systematic mapping study on 136 primary studies was conducted to answer six research questions. Results: the study revealed that most of the existing work have used MMRE, MRE, and PRED for measuring the accuracy of effort estimation, where NASA93 and COCOM81 were the most used dataset. Furthermore, most of the reviewed studies attempted to use machine learning methods, whereas other studies proposed hybrid models. With respect to size metrics, most of the reviewed studies used line of code ( KLOC/ LOC/SLOC). Conclusion: new research should be carried out and oriented towards studying the relationship between the various factors that increase or decrease software effort such as, project type, team memberâ€™s expertise, required software reliability, and software complexity, which can be very useful to enhance effort estimation techniques.
Keywords:	Software Effort Estimation, cost estimation, Systematic mapping study.
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	A COMPARISON STUDY OF DOCUMENT CLUSTERING USING DOC2VEC VERSUS TFIDF COMBINED WITH LSA FOR SMALL CORPORA
Author:	AMALIA AMALIA, OPIM SALIM SITOMPUL, ERNA BUDHIARTI NABABAN, TEDDY MANTORO
Abstract:	The selection of a suitable word vector representation is one of the essential parameters in document clustering because it affects the performance of clustering. The excellent word vector representation will generate a good clustering result, even only using the simple clustering algorithm like K-Means. Doc2Vec, as one of word vector representations, has been extensively studied in large text datasets and proven outperforms the performance of traditional word vector representation in document categorization. However, only a few studies analyze word vector representations of small corpora. As appropriate, learning observation in a small corpus is also crucial because, in some cases, a large corpus was not always available, particularly in some low-resources languages like Bahasa Indonesia. Moreover, the clustering of the small datasets also plays essential roles in pattern recognition and can be an initial step to implement the analysis result in a more significant corpus. This study is an experimental study that aims to explore more in-depth exploration to compare document clustering using Doc2Vec versus TFIDF-LSA for small corpora in Bahasa Indonesia. In this study, the quality of word vector representation is measure by the cluster performance using intrinsic and extrinsic measurements. The study also considers measuring word representation based on time and memory consumption. This study also concerns with getting an optimal word vector representation by tuned appropriate hyper-parameter. The word vector representations were tested to various sizes of the small corpora using the K-Means algorithm. The result of this study, a TFIDF-LSA gets a better cluster performance; meanwhile, the Doc2Vec model gets a better time and memory usage efficiency.
Keywords:	Clustering, Word Vector Representation, Word Embedding, Clustering Comparison, Small Corpora
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	COMPARATIVE ANALYSIS OF DEPTH-FIRST SEARCH ALGORITHM AND GREEDY ALGORITHM AT NEAREST ATM SEARCH IN PADANG SIDEMPUAN CITY
Author:	DIAN RACHMAWATI , SYAHRIL EFENDI , ADI SYAHPUTRA SITUMORANG
Abstract:	The Automated Teller Machine (ATM) is needed when users need immediate banking transactions. But sometimes ATM has some problems such as ATMs are being broken, money deposited in ATM depleted, the number of people who queued at ATM, etc. At that time, people needed an alternative ATM located nearby. To make the process of ATM search easier, than it takes a system that serves to find the nearest ATM from the ATM location that the user is visiting. Padang Sidempuan City has many ATMs in various places. This research will make the nearest ATM search system in Padang Sidempuan city. In this system, there is a menu to search by selecting the starting point and ATM of the bank that you want to go and will produce the nearest ATM and the line from the starting point to the ATM. To support the closest ATM search in this system, the deep first search algorithm and the greedy algorithm applied to this system. Then, the performance of both algorithms will be compared based on process time and distance. After implementation and comparison, it is known that the complexity of the depth-first search algorithm is the same as the complexity of the algorithm greedy, (N2). Attesting with a sample of 10 starting points and 1 ATM destination, the depth-first search algorithm has an average running time of 239.9675 milliseconds, and the average distance is 3033.555 meters, while a greedy algorithm has an average running time of 274.8501 milliseconds and the average distance is 2035.2568 meters. So it concludes that the depth-first search algorithm is more efficient in running time than the greedy algorithm. But in generating shorter distances, the greedy algorithm is better than the depth-first search algorithm.
Keywords:	Depth First Search, Greedy, ATM, Shortest Path, Algorithm
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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Title:	DIMENSIONS OF PROTECTION BEHAVIORS: A SYSTEMATIC LITERATURE REVIEW
Author:	IBRAHIM MOHAMMED AL-HARTHY , FIZA ABDUL RAHIM , NOR'ASHIKIN ALI , AMANDO P. SINGUN JR
Abstract:	The term Bring Your Own Device (BYOD) has generated many hopes and fears among many users in this field related to behaviors of information protection. Threats of BYOD include illegal access to policy changes and information, disclosure of confidential details to the public, leakage of organization data and privacy, access control, abuse, and lost of devices. This study examines the existing studies on various dimensions in conceptualizing the behaviors of information protection. Using a systematic method, we analyzed four major databases, including IEEE, Science Direct, SpringerLink, and Taylor & Francis, from which 57 articles were selected from the year 2010 to 2019. In this study, ten (10) dimensions are discussed: protection behaviors and its Perceived Severity, Perceived Vulnerability, Self-Efficacy, Response Efficacy, Response Cost, Subjective Norm, Attitude, Security Self-Efficacy, Information Security Awareness and Perceived Behavioral Control.
Keywords:	Bring Your Own Device, BYOD, Protection Behaviors, Systematic Literature Review
Source:	Journal of Theoretical and Applied Information Technology 15^th September 2020 -- Vol. 98. No. 17 -- 2020
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