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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 2021 | Vol. 99 No.05

Title:	MAPPING ACCEPTANCE OF INDONESIAN ORGANIC FOOD CONSUMPTION UNDER COVID-19 PANDEMIC USING SENTIMENT ANALYSIS OF TWITTER DATASET
Author:	BAGUS SETYA RINTYARNA
Abstract:	Computational intelligence based technique becomes popular lately for many application including revealing trend in healthy food consumption. Healthy alternative food that insures the basic physical needs of mankind becomes more popular among people worldwide nowadays. Organic food is believed as alternative food providing sustainable benefit for mankind especially under the pandemic situation that body urgently needs to maintain optimal immune system. Organic food helps to supply sufficient nutrients that is important for body to cope with virus infection. Previously, many studies have been conducted worldwide to exhibit organic foods consumption pattern. The approach can be categorized into two types. The first approach relies on pencil survey and focus group discussion involving a certain number of respondents. The analysis commonly applies statistical techniques. This approach has been considered time consuming and costly. A more sophisticated and time saving technique commonly make use social media platform as the primary tool for revealing the pattern. This study is an initial study to provide model of Indonesian organic food consumer considering that Indonesia is potential for both producer and consumer of organic food. The analysis is based on Twitter dataset and applying computational based technique using Lexicon Based Sentiment Analysis using VADER. Beforehand, we perform text analysis using Force Atlas2 to reveal spatial representation of both attraction force and repulsion force of words. To extent VADER, we employ Indonesian sentiment lexicon namely INSET. The sentiment analysis result confirms that 64% user accept positively organic food as healthy dietary food highlighting the importance of organic food for people to maintain optimal immune system in Covid-19 Pandemic Circumstances. Most of the user that positively post organic food, associate the food with kesehatan, praktis, and diet. Meanwhile, the rest post negatively and regard organic food as having expensive price compared with another kind of food.
Keywords:	Sentiment Analysis, Text Mining, Lexicon-Based, Organic Food, Covid-19 Pandemic, Twitter
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	BUSINESS INTELLIGENCE USAGE MODEL FOR HIGHER EDUCATION INSTITUTIONS
Author:	SALAMATU MUSA, NAZMONA MAT ALI, SURAYA MISKON, MUSTAPHA ABUBAKAR GIRO, RASHA ALJABALI
Abstract:	Higher education plays a substantial role in the political and socio-economic development of a country. Developing countries experience several challenges when it comes to higher education programs. Therefore, Information and Communications Technology (ICT) tools can bridge the gap of poor higher education services. Business Intelligence (BI) is a system that uses analytical techniques to obtain knowledge from large amount of data. Business Intelligence can assists higher education organizations to make eﬀective decisions, improve teaching and learning services, competitiveness, and to devise or produce new strategies and policies. However, BI implementation is often plagued with many complex processes, challenges, issues, risks, and drawbacks making it diﬃcult to achieve BI beneﬁts. Nevertheless, the number of successful BI implementation in Higher Education Institutions (HEIs) is still inadequate. Considering the challenge and need of implementing BI system, this research aims to contribute by determining the factors that inﬂuence BI usage for Malaysian Higher Education Institutions (HEIs). The study used literature review to ﬁnd the factors of BI usage. The literature revealed nine factors which promote the usage of BI system. These factors are organized into three dimensions: organizational, process and technological dimension. This study deploys a questionnaire survey method to collect data from BI users and descriptive statistics to validate the data. Based on the result obtained after the validation, it indicated that all the factors can be used by Malaysian HEIs as the factors that helps in realizing the success of BI usage. As a result, findings from this study can contribute in boosting the universities systems to obtain important patterns and predictions to formulate strategies, generate knowledge and decision-making processes that allow the achievement of institutional objectives.
Keywords:	Business Intelligence, Usage, Case study, Factors, Higher Education Institutions.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	A COMPARISON OF ML APPROACHES ON SENTIMENT ANALYSIS, BASED ON ONTOLOGIES, SARCASM AND SUBJECTIVITY DETECTIONS IN THE CASE STUDY OF US ELECTIONS
Author:	IHAB MOUDHICH, ABDELHADI FENNAN
Abstract:	Nowadays, Twitter has become one of the most excellent tools that give people the power to express their emotions. And also, to interact with other ordinary or political people. According to The Verge, as known as the American technology news website, more than 166 million users are using Twitter every day; this thing made Twitter one of the largest news sources and one of the places where most of the politicians publish their opinions or their thoughts. Sentiment analysis or opinion mining is a method that is used to understand the user's behavior based on their feelings in a given text, which can help to get a global idea of the expected outcomes of USA elections. Our research is based on extracting the data and analyzing tweets' sentiment to predict the USA elections results. As we know, most Americans use Twitter to interact with each other to explain their opinions and thoughts about the subjects related to their country. Also, the hashtag system on Twitter makes it easy to help people to interact and go viral. We also included other variables to make a significant comparison of our results, such as detecting sarcasm and subjectivity in a tweet. Also, we used two machine learning approaches: First known as Long Short-Term Memory (LSTM). The second is Bidirectional Encoder Representations from Transformers (BERT). In this work, we used more than 500,000 tweets to get a significant result. Moreover, our developed Framework consists of 5 steps: First, collecting data based on ontologies that we defined. Second, text pre-processing to clean data. Third, predicting subjectivity and sarcasm in a tweet. Fourth applying the two cited approaches to get the sentiment. And the last one is visualizing and analyzing the results.
Keywords:	Sentiment Analysis, Machine Learning, Ontology, LSTM, BERT, Sarcasm Detection, Subjectivity Detection.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	A MACHINE LEARNING APPROACH FOR BREAST CANCER EARLY DETECTION
Author:	MUAWIA A. ELSADIG
Abstract:	The rapid increase in incidence of breast cancer is clearly noticeable. The cause of the disease is not clear and the reasons behind the increase of incidence are not well identified. In addition, a method for preventing its occurrence has yet to be discovered. Therefore, its early detection plays a major role in the treatment process and assists in achieving an acceptable survival rate. As a matter of fact, there are many methods, based on machine learning or statistical approaches, for distinguishing between benign and malignant images. However, most of them do not achieve the desired accuracy, due to the use of inaccurate features, the absence of proper classifiers, or inefficient datasets. Therefore, this study introduced an effective classifier approach based on a support vector machine (SVM) with an adequate features selection method that considers only the features with high influence and neglects the others. This scenario potentially enhances the accuracy of classification and reduces the computational overheads. In addition, the use of a trusted dataset and the application of proper validation methods were reflected in reliable and trusted results. Selecting of SVM is done after conducting real experimental scenarios for seven reputable classifiers in the field of breast cancer diagnosis. The experimental results reflect that the classifier approach, based on SVM, outperformed the other classifiers by obtaining the highest accuracy, reaching 97.4%. The contribution of this paper includes introducing an efficient SVM approach to predict breast cancer and presenting a comparison study for seven popular classifiers in this field. Our results have been thoroughly validated to nominate SVM as the best classifier for breast cancer detection.
Keywords:	Data mining, Machine Learning, Deep learning, Medical Images Classification, Breast Cancer Detection, Breast Cancer Diagnosis.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	A NOVEL SOIL MOISTURE, TEMPERATURE AND HUMIDITY MEASURING SYSTEM- AN IOT APPROACH
Author:	LARRY ELIKPLIM KODJO AKPALU, ROSE-MARY OWUSUAA MENSAH GYENING, OSMAN YAKUBU, ISAAC NKRUMAH JR
Abstract:	The world is currently experiencing a rapid population growth resulting in increased food demand. To avoid the risk of famine in especially low-income countries, effective and efficient agricultural practices that will enhance food production at a lower cost is desired. For effective food production, soil quality is very important. There should be improved techniques to determine precise soil moisture, humidity, and temperature measurement to guide farmers to be efficient. In Sub-Saharan African countries such as Ghana, farmers use conventional methods to check how good the soil is for cultivating crops; they examine the soil with their bare hands. This approach has proven to be ineffective as it requires significant amount of human effort. To address this challenge, an IoT based soil moisture, humidity, and temperature measurement system is proposed. It consists of a moisture probe to determine the moisture percentage of the soil, a temperature and humidity sensor for measuring the temperature and humidity of the soil, and a Wi-Fi module for transmitting the data to a data repository for analysis. A prototype is developed based on a conceptual framework which is tested under live conditions. Based on ideal parameters for the cultivation of certain crops which are stored in the data repository, and the live data captured by the sensors, the farmers are alerted electronically on the most suitable crops to plant. The system was tested in five different locations and based on the data gathered, it recommended the products that are suitable for cultivation in a particular field. The proposed system outperformed other referenced models in identifying soil suitability for the cultivation of crops. The system was able to recommend suitable crops for planting based on data on soil parameters, a feature that made it novel.
Keywords:	Automatic, Cloud, Internet Of Things, Sensors, Soil Moisture
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	AN EFFICIENT FULLY HOMOMORPHIC ENCRYPTION SCHEME OVER NTRU_ROBUST_PKE
Author:	EL HASSANE LAAJI, ABDELMALEK AZIZI, TAOUFIK SERRAJ
Abstract:	Fully Homomorphic Encryption(FHE) is an important field of research. This modern technology enables computation over encrypted data by a cryptosystem able to perform correctly those calculations. In this work, we create an improved release of NTRU called NTRUrobus_PKE (Public Key Encryption), and we create over it an efficient Fully Homomorphic Encryption (FHE) scheme. Our NTRUrobust_PKE is implemented by using the Number Theoretic Transform (NTT) algorithm combined with our own Fast Modular Multiplication algorithm (FMMA). Using these algorithms allows the complete cryptographic process to be faster by a factor up to 69 times compared to using just the convolution multiplication instead. And that allows our FHE protocol over NTRUrobust_PKE to perform iteratively the computation over the encrypted data with perfect correctness and for unlimited depth. In terms of security, we target the high level by using the parameters set that meets the category 5 security level defined by NIST (National Institute for Standard and Technology).
Keywords:	Post Quantum cryptography, Modular Multiplication, NTRU, NTT, Fully Homomorphic Encryption.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	COMPARISON OF BOLDI VIGNA (Ζ2) ALGORITHM AND ELIAS DELTA CODE ALGORITHM IN AUDIO FILE COMPRESSION
Author:	HANDRIZAL, FAUZAN NURAHMADI, MUHAMMAD ABRAR
Abstract:	Information technology nowadays develops quickly and brings a lot of positive impacts on human life. Humans are now able to exchange data and information easily. But the problem is the size of the data tends to be so large and requires a lot of storage and makes the transmission cost so high. To overcome this problem, data processing techniques are required, one of them is through data compression so that the result size can be reduced. WAV is an uncompressed audio saving format and it makes mainly files in (*.wav) extension tend to have a large size and need a lot of space in storage. In this research, the compression test will be done on 8-bit wav audio files using the Boldi-Vigna algorithm and the Elias Delta Code algorithm. Both of these algorithms are included in the lossless compression algorithm which means that it can restore the compressed data to original data through the decompression process. The performance of Boldi-Vigna and Elias Delta Code algorithms will be calculated based on the predefined comparison parameters. Based on the test results, it is obtained that the Boldi-Vigna algorithm is better at compression with an average Ratio of Compression of 69.562%, Compression Ratio of 1.458, and Space Saving of 30.428%. The compression results obtained are influenced by the number of the same digital values contained in the wav audio file that needs to be compressed. Both algorithms can restore the whole audio file like the original audio file through the decompression process.
Keywords:	Compression, Elias Delta Code, Boldi-Vigna
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	PARTIAL LEAST SQUARES ANALYSIS: THE INTERACTION EFFECT AMONG CYBERSECURITY, CYBERCRIME AND ONLINE SHOPPING INTENTION
Author:	JASSIM AHMAD AL-GASAWNEH, GHADA AL-RAWASHDEH, Ali ZAKARIYA Al-QURAN, AHMAD MTAIR AL HAWAMLEH, NAWRAS M. NUSAIRAT, ABDUL HAFAZ NGAH
Abstract:	This study examined the moderating role of cybersecurity in the relationship between cybercrime and online shopping intention among Jordanian customers. A total of 270 online users were the study sample. Online survey was used to gather data which were then analyzed using partial least squares structural equation modelling run using Smart PLS 3.2.9 software. This study found that the cybersecurity moderated the negative relationship between cybercrime and online shopping intention among Jordanian customers. This study contributes theoretically by filling the gaps in the literature, by proposing the combined use of Perceived Risk Theory and Protection Motivation Theory in using the moderating role of cybersecurity in the relationship between cybercrime and online shopping intention. In practice, this study facilitates E-marketers in improving their company’s online markets by altering the perspectives and intention of customers towards online shopping, particularly through the improvement of cybersecurity policies to assure safe transactions.
Keywords:	Cybersecurity, Cybercrime, Online Shopping Intention, Jordan
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	THE INFLUENCE OF BLACK HOLE ATTACK IN ROUTING EFFICIENCY IN MANET
Author:	MAHA ABDELHAQ, RAED ALSAQOUR, HANEEN AL-ABDULLATIF, WALAA AL-TAMIMI, REHAM AL-ANAZI, MAISA AL-SIBAIE, AMAL AL-ZAHAMI, SHEUIHINAH AL-OTAIBI
Abstract:	Mobile Adhoc Network (MANET) is a combination of mobile nodes in a non-infrastructure network in which nodes communicate continuously without a centralized network manager. MANET nodes act as routers and are dependent on one another to keep the network connected. Due to the nature of the MANET network, it is vulnerable to different types of attacks including Blackhole, Denial of Service, and Rushing. Blackhole attack is a harmful active attack on MANET (also called Selfish node attack). In this paper, we present a comparative study on blackhole attack resistance utilizing three types of routing protocols. The three protocols are Ad hoc On-Demand Distance Vector (AODV), Zone Routing Protocol (ZRP) and Optimized Link State Routing Protocol (OLSR). This paper's main contribution is to introduce a new model for blackhole simulation of attacks in Network Simulator Version 2 (NS2). In terms of throughput and end-to-end delay, the efficiency of the three routing protocols will be tested under the direct influence of blackhole attacks.
Keywords:	MANET, Routing protocol, Blackhole attack, AODV, ZRP, OLSR
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	MODIFIED CUCKOO SEARCH ALGORITHM FOR SOLVING OPTIMAL POWER FLOW PROBLEM
Author:	PURWOHARJONO
Abstract:	Optimal power flow (OPF) is known as one of the most important planning and scheduling tools in electrical power systems. The OPF problem is a non-convex optimization problem, therefore, the applications of meta-heuristic algorithms in the OPF problem have been gained more attentions in recent years. In this paper, a modified cuckoo search algorithm (MCSA) is proposed to solve OPF problem. The proposed method has been developed on the original cuckoo search algorithm to improve the quality of the optimal solutions. Modifications include additional information exchanges between the top eggs, or the best solutions. The new algorithm is implemented to the OPF problem so as to minimize the total generation cost when considering the equality and inequality constraints. In order to validate of the proposed algorithm, it is applied to the standard IEEE 30-bus and IEEE 57-bus test systems. The results show that the proposed technique provides better solutions than other heuristic techniques reported in literature.
Keywords:	Modified cuckoo search algorithm, Optimization, Optimal power flow, Power system
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION
Author:	ADIL H. KHAN1, D.N.F. AWANG ISKANDAR, JAWAD F. AL-ASAD, SAMIR EL-NAKLA, SADIQ A ALHUWAIDI
Abstract:	There are multiple types of skin cancer but melanoma is the deadliest skin cancer or lesion type. Early recognition of melanoma in dermoscopy images essentially increase the endurance rate. However, the precise acknowledgment of melanoma is very challenging because of the numerous reasons: low difference among lesion and skin, visual comparability among melanoma and non-melanoma lesions, and so forth. Consequently, the dependable programmed diagnosis of skin cancer is exceptionally helpful to dermatologist. In this paper, we proposed profound learning strategy to address three primary assignments developing in the zone of skin lesion picture preparation, i.e., dermoscopic highlight, extraction and detection. A profound algorithm comprising of preprocessing in CIELAB color space and Delaunay triangulation based clustering along with Particle Swarm Optimization (PSO) is proposed for the segmentation. Moreover, skin lesion images are clustered based on fused color, pattern and shape based features. A boost ensemble learning algorithm using Support Vector Machines (SVM) as initial classifiers and Artificial Neural Networks (ANN) as a final classifier is employed to learn the patterns of different skin lesion class features. The proposed automated system is assessed on the ISIC and PH2 datasets. Test results show the promising efficiency of our proposed study, i.e., 96.8% and 92.1% segmentation accuracy for ISIC and PH2 datasets respectively. Classification accuracy of 97.9% also accomplished on ISIC dataset. It can be concluded from this research that proposed system employed the power of simple methods that are less resource-hungry yet provide better results.
Keywords:	Skin Lesion, Artifacts Removal, Delaunay Triangulation, Features Fusion, Ensemble Classification
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	RECOGNITION OF SPEAKER’S EMOTION BY SQUEEZENET CONVOLUTIONAL NEURAL NETWORK
Author:	LIUDMYLA TEREIKOVSKA, IHOR TEREIKOVSKYI, AIMAN BEKETOVA, GABIT KARAMAN, NADIIA MAKOVETSKA
Abstract:	The article deals with the development of neural network means for analyzing a voice signal to recognize the speaker’s emotions. We have established the possibility of improving these means through the use of a convolutional neural network of the SqueezeNet type, which determines the necessity to assess the effectiveness of such use. We have also determined that it is possible to assess the efficiency of using the neural network model experimentally by means of indicators of recognition accuracy and duration of training. A software implementation of SqueezeNet has been developed, with a training sample formed, using the publicly available TESS database, consisting of samples of voice signals with 7 emotions for 2 users. Mel-frequency cepstral coefficients are used as the parameters characterizing a voice signal. Using computer experiments, we have found that after 80 periods of training on a fairly limited training sample, SqueezeNet enables using validation examples to achieve speaker recognition accuracy of about 0.95, which is proportionate to the results of the best modern systems of the similar purpose and confirms the possibility of effective use of this type of network for analyzing a voice signal. We have shown the necessity for further research related to the adjustment of neural network solutions to the recognition of the speaker’s emotions under a variety of noise interference. We have also determined the feasibility of developing a method for adjusting SqueezeNet architectural parameters to the conditions of the task to analyze a voice signal for simultaneous recognition of the speaker’s personality and emotions.
Keywords:	Voice Signal, Emotion Recognition, Neural Network Model, Convolutional Neural Network, SqueezeNet
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	THE INFLUENCE OF DIGITAL TECHNOLOGY, CUSTOMER EXPERIENCE, AND CUSTOMER ENGAGEMENT ON E-COMMERCE CUSTOMER LOYALTY
Author:	RUDY, HARJANTO PRABOWO, ASNAN FURINTO, MOHAMMAD HAMSAL
Abstract:	E-commerce players need appropriate strategies to reach firm performance and sustainability. Earlier research has investigated this question by examining the relationships between customer experience and customer engagement and customer experience and customer loyalty. This research offers novelty in digital technology’s role as a variable that affects customer experience and customer engagement and the effects of customer experience and customer engagement on customer loyalty in Indonesian e-commerce. This research used descriptive survey and explanatory survey as research methods. Serving as the analysis units in this research were individuals and serving as observation units were e-commerce retail customers. Respondents’ data were analyzed with SEM-AMOS. The research results showed that digital technology could be applied to form customer experience and customer engagement, and eventually, it could also be used to build customer loyalty.
Keywords:	Digital Technology, Customer Experience, Customer Engagement, Customer Loyalty, E-Commerce Retail
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	DETECTING DEPRESSION IN ALZHEIMERS DISEASE AND MCI BY SPEECH ANALYSIS
Author:	BASHAR ABDALLAH-QASAIMEH, SYLVIE RATTÉ
Abstract:	It is estimated that 30% of Alzheimers patients suffer from depression. Since this condition can lead to further cognitive decline and suffering, its detection is essential to alleviate MCI (mild cognitive impairment) or AD symptoms in patients. This paper presents a machine learning method aimed at identifying MCI and Alzheimers disease (AD) patients suffering from depression, using different features extracted from their speech. 276 participants (mean age 70.9 years) are selected from DementiaBanks Pitt Corpus for this research. The interviewer’s voice and the silences are removed from the audio records as a preprocessing task. Several audio features are extracted from the patients speech to achieve this task. For instance, MFCCs, Spectral Centroid, Spectral Roll-Off Point, and others. We trained and compared three families of classifiers (SVM, Random Tree, and Random Forest) through two experiments, one using Spectral feature variants and MFCC, and the other using only MFCC features. A third experiment is conducted for comparison with the literature review. In all cases, we used a bootstrapping method to solve the sampling bias, i.e., 70% of the patients not suffering from depression. From the results, the MFCC feature set was more appropriate for tree-based classifiers than SVM, in which the Random Tree classifier reported the highest classification performance (91.3%). Meanwhile, the other feature sets were more appropriate for SVM than the tree-based classifiers, where SVM reported an 89.1 classification accuracy, with 91.1% recall.
Keywords:	Alzheimers; MCI; depression; automatic detection; speech analysis.
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	EXPLORATION OF VARIOUS VIEWPOINTS IN CLOUD COMPUTING SECURITY THREATS
Author:	T.A MOHANAPRAKASH, DR.V.NIRMALRANI
Abstract:	The cloud technology has demonstrated an outstanding performance in last few decades. It has been able to reduce the upfront investment requirements of enterprises in order to provide a seamless service with the introduction of Infrastructure as a Service, Software as a Service and Platform as a Service. This provides a wide variety of options and instant deployment of selected services. The prime component of the cloud computing is the virtualization of the shared computing resources among multiple clients. Even though the cloud computing has outperformed the traditional computing methodologies it has a few backlogs in terms of security. The security threats associated with cloud are but not limited to data integrity, network breach, insider attacks, virtual machine side channel attacks etc. Now a days 70% of the industries using cloud, because of this threat becomes a serious concern to the cloud service provider and to the clients. In this paper analysis about the recent security threats of cloud in regards to data security, network security, environmental issues and virtualization issues. Also this paper discuss and analysis roles of various algorithms used in cloud computing for security.
Keywords:	Cloud computing, Data security, Virtualization threats
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	APPLICATION OF CUSTOM-MADE SIMULATOR FOR TRAINING IN ROBOTIC NAVIGATION STRATEGIES
Author:	JORGE DUARTE, YEISSON TRIVINO, FREDY MARTINEZ
Abstract:	The social needs that require the support of robotics are increasingly evident. Tasks such as caring for children and the elderly in isolated, unsupervised homes have become evident following the demands of social isolation caused by the COVID-19 pandemic. Service robotics is presented as a current and readily available solution for the remote monitoring of people, particularly those with medical restrictions or in education processes. However, the development of these applications requires a critical mass of professional personnel trained in the design of these robots and their navigation and manipulation schemes. Current robotic systems tend to be expensive, so their availability at the university level is limited. Its use aims to be dedicated to research, leaving aside the processes of training at the undergraduate level. A very efficient way to develop a specialized training in robotics for young researchers is through the use of software tools that simulate existing robotic models in the laboratory. This article presents the formulation and development of a software tool designed to train young researchers within the research group. The tool is the first of a knowledge management system that the research group has proposed to encourage and accelerate research in service robotics. This first tool focuses on the problem of autonomous planning of movement in globally observable environments, similar to those normally foreseen for service robots. Specifically, it implements the algorithm of visibility graphs to define the navigation route of a robot from a point of origin to a point of destination. As result, it is presented the final operation of the tool, its advantages in terms of configuration and manipulation, and its capacity to evaluate the performance in front of different variables of the problem. This article is developed as part of the group's research in mobile robotics and image processing, specifically related to the training of junior researchers.
Keywords:	Research Process, Robotics, Self-Directed Learning, Simulation Model, Specialized Training
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	INDOOR POSITIONING SYSTEM USING COMBINED METHOD OF FINGERPRINTING AND DEAD RECKONING
Author:	ROLLY JUNIUS LONTAAN, GEDE PUTRA KUSUMA
Abstract:	The development of technology to determine the position of an object is very helpful and widely used, such as the technology of the Global Positioning System (GPS). However, GPS technology can only be used to see the position of an object outdoors at an accurate level that is still far from the indoor positioning technology known as the Indoor Positioning System (IPS). This research will discuss IPS technology which is very useful for several things. One of the benefits that can be applied to student attendance systems using smartphones, which generally already have Bluetooth technology. IPS technology currently has several methods and hardware used, one of which is Bluetooth Low Energy (BLE). BLE issues a signal that is lower than Wi-fi so that in a room several BLEs can be placed which will later be taken by the Received Signal Strength Indicator (RSSI) from the specified Reference Point (RP). One RSSI from each RP is the offline data retrieval phase from the Fingerprinting method. The next step is online data collection and will use the Weighted Sum approach to calculate the second fingerprint data and combine it with the Dead Reckoning method. Where the Dead Reckoning method takes advantage of the accelerometer and gyroscope features found on smartphone devices. To produce a stable Final Location from both methods will use the Kalman Filter formula. When these two methods are combined, the results show that the Fingerprinting method has higher accuracy than the Dead Reckoning method.
Keywords:	Indoor Positioning System, Bluetooth Low Energy, Fingerprinting, Dead Reckoning
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	5G SPECTRUM VALUATION OF MILIMETER WAVE TECHNOLOGY: A CASE STUDY OF INDONESIA INDUSTRIAL AREA FOR ACCELERATION OF BROADBAND DEVELOPMENT
Author:	ALFIN HIKMATUROKHMAN, R. DEINY MARDIAN, KALAMULLAH RAMLI, MUHAMMAD SURYANEGARA, IBRAHIM KHOLILUL ROHMAN
Abstract:	Spectrum valuation is significant in helping policymakers prepare new technology regulations to address unique circumstances, such as location-specific services in industrial areas with larger spectrum bands. This paper presents the results of a study on the economic valuation of 5G spectrum at millimeter wave (mmWave) for accelerating broadband development. It uses a case study of industrial areas in Indonesia, focusing on the effects of frequency bands of 26 GHz and 28 GHz on three factors of an engineering-economic model: maximal cellular coverage, cost per square kilometer (in terms of capital expenditures (CAPEX) and Operational expenditures (OPEX)), and spectrum value per MHz population. The results showed that the mmWave utilization for 5G services requires higher infrastructure expenses. Population density also has a significant influence on spectrum valuation in both lower and higher frequency bands.
Keywords:	5G, Spectrum Valuation, Engineering-economic model, 5G mmWave, 5G Capex and Opex
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	CYBER CRIME RISK CONTROL IN NON-BANKING ORGANIZATIONS
Author:	FORD LUMBAN GAOL, ANANDA DESSI BUDIANSA, YOHANES PAUL WENIKO , TOKURO MATSUO
Abstract:	Cybercrime has become the main concern of an agency or business nowadays. It says that 71 percent of organizational threats occur because of the failure of the company to monitor the software, according to the Global Risk Management Report. Though 74% is disputed because there is no updating of the IT system. This research focuses on Indonesia and, according to the Patroli Siber website, 815 cases of offensive material have been disseminated, followed by web fraud in as many as 530 cases, unauthorized access in 104 cases, data theft in 36 cases, device reduction in 17 cases and data manipulation in as many as 54 cases. For 1361 instances, Whatsapp Social Media was ranked first in social media, followed by Intsagram 1288 instances, Facebook 596 instances, and email 108 instances. This research is also carried out in order to provide input to non-bank organizations to take future preventive steps against the risk of cybercrime. The outcome found that for non-bank organisations, there are certain steps that need to be taken in risk reduction.
Keywords:	Risk Mitigation, Cyber Crime, Cyber Threats, Non-Banking Organization
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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Title:	A NOVEL AFRICAN BUFFALO BASED GREEDY ROUTING TECHNIQUE FOR INFRASTRUCTURE AND CLUSTER BASED COMMUNICATION IN VEHICULAR AD-HOC NETWORK
Author:	N SREE DIVYA , Dr. VEERAMALLU BOBBA , Dr. RAMESH.VATAMBETI
Abstract:	In this modern era, the wire free replica is utilized Vehicular Ad hoc Networks (VANETs) to converse each other. Also, the VANET paradigm not required any specific fixed infrastructure. Furthermore, the vehicle in VANET framework is movable like as mobile nodes. Also, the wireless connectivity between the vehicular nodes is not stable in all cases, it often changes their structure. Research have recommended various responses to control these issues and furthermore to lessen blockage in VANET environment. Therefore, the infrastructure of a network changes frequently which results in communication overheads, energy consumption and lifetime of the nodes. Consequently, in this paper a novel African Buffalo based Greedy Routing (ABGR) technique is to improve the performance of infrastructure and cluster based communication of the node. Moreover, the routing overhead and infrastructure communication can be enhanced by this proposed protocol. Consequently, the energy consumption solution is enhanced based on the CH. Sequentially, the proposed routing protocol is compared with existing protocols in terms of end-to-end delay, throughput, Data transmission Ratio (DTR), and energy consumption and so on. Therefore, it shows that the energy utilization and lifetime of the nodes in the proposed network has been enhanced.
Keywords:	Vehicular Ad-Hoc Network (VANET), Energy Consumption, Data Transmission Ratio (DTR), End-to-End Delay, Throughput
Source:	Journal of Theoretical and Applied Information Technology 15^th March 2021 -- Vol. 99. No. 05 -- 2021
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