Journal of Information Technology and Cyber Security
Journal of Information Technology and Cyber Security (JITCS) is a refereed international journal whose focus is on exchanging information relating to Information Technology and Cyber Security in industry, government, and universities worldwide. The thrust of the journal is to publish papers dealing with the the design, development, testing, implementation, and/or management of Information Technology and Cyber Security, and also to provide practical guidelines in the development and management of these systems. The journal will publish papers in Information Technology and Cyber Security in the areas of, but not limited to: 1. Enterprise Systems (ES): o Enterprise Resource Planning, o Business Process Management, o Customer Relationship Management, o System Dynamics, o E-business and e-Commerce, o Marketing Analytics, o Supply Chain Management and Logistics, o Business Analytics and Knowledge Discovery, o Production Management, o Task Analysis, o Process Mining, o Discrete Event Simulation, o Service Science and Innovation, and o Innovation in the Digital Economy. 2. Information Systems Management (ISM): o Software Engineering, o Software Design Pattern, o System Analysis and Design, o Software Quality Assurance, o Green Technology Strategies, o Strategic Information Systems, o IT Governance and Audits, o E-Government, o IT Service Management, o IT Project Management, o Information System Development, o Research Methods of Information Systems, o Adoption and Diffusion of Information Technology, o Health Information Systems and Technology, o Accounting Information Systems, o Human Behavior in Information System, o Social Technical Issues and Social Inclusion, o Domestication of Information Technology, o ICTs and Sustainable Development, o Information System in developing countries, o Software metric and cost estimation, o IT/IS audit, and o IT Risk and Management. 3. Data Acquisition and Information Dissemination (DAID): o Open Data, o Social Media, o Knowledge Management, o Social Networks, o Big Data, o Web Services, o Database Management Systems, o Semantics Web and Linked Data, o Visualization Information, o Social Information Systems, o Social Informatics, o Spatial Informatics Systems, and o Geographical Information Systems. 4. Data Engineering and Business Intelligence (DEBI): o Business Intelligence, o Data Mining, o Intelligent Systems, o Artificial Intelligence, o Autonomous Agents, o Intelligent Agents, o Multi-Agent Systems, o Expert Systems, o Pattern Recognition, o Machine Learning, o Soft Computing, o Optimization, o Forecasting, o Meta-Heuristics, o Computational Intelligence, and o Decision Support Systems. 5. IT Infrastructure and Security (ITIS): o Information Security and Privacy, o Digital Forensics, o Network Security, o Cryptography, o Cloud and Virtualization, o Emerging Technologies, o Computer Vision and Image, o Ethics in Information Systems, o Human Computer Interaction, o Wireless Sensor Networks, o Medical Image Analysis, o Internet of Things, o Mobile and Pervasive Computing, o Real-time Systems and Embedded Systems, o Parallel and Distributed Systems, o Cyber attacks, o Machine learning mechanisms for cyber security, o Modern tools for improving cyber security, o Emerging trends in cyber security, o Cyber security in Internet of Things (IoT), and o Cyber security in Cloud.
Articles
30 Documents
<![CDATA[Recommendation System Using the K-Nearest Neighbor Approach: A Case Study of Dual Camera Quality as a Smartphone Selection Criterion ]]>
<![CDATA[Pasha, Parcelliana Binar]]>;
<![CDATA[Muflihah, Yusrida]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.7559
<![CDATA[Many smartphones today need to be more precise about choosing one that suits the user's needs. In fact, smartphone sellers sometimes need help recommending smartphones that suit buyers' needs. Generally, buyers search for smartphone specifications with keywords they desire, but the results appear different from what they expected. Users need the main specifications, such as Random Access Memory (RAM) and Read Only Memory (ROM) capacity, battery, and high camera quality. This research aims to implement the K-Nearest Neighbor (KNN) algorithm for recommendation smartphone selection based on the criteria mentioned. The data test results show that the combination of KNN with four criteria has good performance, as indicated by the accuracy, precision, recall, and f-measure values of 95%, 94%, 97%, and 95%, respectively.]]>
<![CDATA[Sales Forecasting Analysis Using Trend Moment Method: A Study Case of a Fast Moving Consumer Goods Company in Indonesia ]]>
<![CDATA[Fauzan, Ammar]]>;
<![CDATA[Rahayu, Dania Gusmi]]>;
<![CDATA[Handayani, Annisa]]>;
<![CDATA[Tahyudin, Imam]]>;
<![CDATA[Saputra, Dhanar Intan Surya]]>;
<![CDATA[Purwadi, Purwadi]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.7572
<![CDATA[The market of Fast-Moving Consumer Goods (FMCG) companies in Indonesia is enormous. Unilever has 400 brands in more than 190 countries, making it a global business that is as influential in the consumer product market as it is in Indonesia. Sales forecasting at this company is very useful for planning expenses and the company's total costs on the business strategy. This study uses trend moment method to forecast the sales and earnings of Unilever Indonesia companies at the end of the year. This article aims to test the performance of the trend moment method calculation on the prediction of net sales and profits in FMCG companies. At the end of the analysis process, it can be concluded that forecasting using trend moment method is going very well. This indicator of success is shown by the error level of MAPE, which is below 10%.]]>
<![CDATA[Delivery Route Estimation on a Web-Based Restaurant Delivery System Using Greedy Algorithm ]]>
<![CDATA[Wando, Carmelita Margaretha Jawa]]>;
<![CDATA[Dzikria, Intan]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.7611
<![CDATA[Food delivery application services have been significantly developed in Indonesia. However, several areas have not received application services like this. Orders made by several restaurants still use social media such as Whatsapp, Facebook, and cell phones. Traditional ordering does not have sufficient means to calculate the cost of delivery of orders resulting in cost-efficiency problems. In addition, order delivery routes are a problem for couriers who have to deliver several orders at once. This research builds a web-based restaurant delivery system by applying a greedy algorithm to optimize routes and shipping costs. The results of this study indicate that the greedy algorithm can determine the best route for couriers to make deliveries so that shipping costs become lower. This research contributes as one proof of the application of the greedy algorithm to business problems and restaurants may use the resulting system to increase the effectiveness of order delivery.]]>
<![CDATA[A Child Growth and Development Evaluation Using Weighted Product Method ]]>
<![CDATA[Januantoro, Ardy]]>;
<![CDATA[Mandita, Fridy]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.7613
<![CDATA[Child development is one of the factors that must be considered in improving a country's education. The level of maturity of human resources is able to maximize starting from childhood. The guidebook of the Ministry of Education and Culture of the Republic of Indonesia (Kemendikbud RI) in 2018 contained six indicators to assess children's learning ability, namely: 1) Moral, 2) Social, 3) Language, 4) Cognitive, 5) Motor, and 6) Art. This study implements these indicators to evaluate children's growth and development. The evaluation method uses the Weighted Product Method (WPM). WPM provides a ranking of the result of the evaluation. In addition, WPM also has an assessment of Beneficial and non-beneficial as a more relevant assessment between indicators. Data were collected by questionnaire at kindergarten schools with the respondents' age average of 5-6 years. The results will be calculated with indicators criteria weights given. The test results recommended for students between 0.65 to 0.62 are as follows: Mahmud, Diko, Cindy, Denny, and Riko. The kindergarten manager can use these recommendations to increase the student's aptitude.]]>
<![CDATA[The Utilization of Information System for Crime Rate Modelling in Surabaya Using K-means ]]>
<![CDATA[Supangat, Supangat]]>;
<![CDATA[Sholiq, M. Mudhafiq]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.7676
<![CDATA[This study aims to model the crime rate in the city of Surabaya using the k-means clustering method. The data used is crime data that occurred in Surabaya in previous years, which includes the type of crime, location of crime, and crime rate. The k-means clustering method is used to classify crime data in the Surabaya area for 2020-2022 consisting of cluster 3, namely areas with moderate crime rates covering 6 sub-districts (1,260 cases), cluster 1 with areas with high crime rates, namely 12 sub-districts with 2,363 cases, and cluster 2 areas with low crime rates consisting of 13 districts with 2,178 cases based on data on the number of crimes. The geospatial visualization system is used to visually display modeling results, making it easier for interested parties to identify the location of a crime. The results of this study are expected to provide useful information for interested parties, such as the police and the community, in taking preventive action regarding crime rates in Surabaya.]]>
<![CDATA[The Role of Task-Technology Fit on the Design and Use of a Hotel Management System ]]>
<![CDATA[Dzikria, Intan]]>;
<![CDATA[Solihin, Muhammad Lutfi Syahindra]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.8712
<![CDATA[Hotels have many problems regarding how to improve their service performance by using technologies, especially when the hotels have a bit of understanding the importance of their employees’ tasks and the technology fitness. This study designed a hotel management system based on the tasks that the employees use to provide services. The purpose of this research is to investigate the influence of task-technology fit on the intention to use the designed hotel management system. In addition, statistical testing methods are also carried out on the system by measuring the impact of task-technology fit on the suitability between user task needs and technology, on user perceptions of technology and how these perceptions affect user intentions and behavior in accepting and using technology. The results of this study indicate that task-technology fit has a significant impact in facilitating its use. The results of this study contribute to the academic implementation of task-technology fit theory in the hotel reservation management system.]]>
<![CDATA[Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques ]]>
<![CDATA[Whardana, Adithya Kusuma]]>;
<![CDATA[Rentelinggi, Parma Hadi]]>;
<![CDATA[Timothy, Hezkiel Dokta]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.8716
<![CDATA[This research aims to detect diabetic retinopathy using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The main objective is to compare these two methods in detecting the condition. Based on the study’s result after training 10 times on each method, the accuracy results were 92% for the CNN method and 50% for the RNN method. These results show, this study with the dataset used, the CNN method is much more effective in detecting diabetic retinopathy than the RNN method. The CNN method is better due to its ability to extract spatial features from images, which is important in image classification tasks.]]>
<![CDATA[Navigating the Cyber Threat Landscape: A Comprehensive Analysis of Attacks and Security in the Digital Age ]]>
<![CDATA[Jony, Akinul Islam]]>;
<![CDATA[Hamim, Sultanul Arifeen]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.9715
<![CDATA[In this contemporary digital age, cybersecurity stands as a crucial linchpin amid the expanding role of technology in our lives, encountering numerous challenges. This review addresses the imperative need for robust cybersecurity measures as malicious actors continually innovate methods to exploit vulnerabilities in computer systems, networks, and data. The exploration delves into the multifaceted realm of cybersecurity attacks, unveiling the evolving threat landscape and their profound implications. From cybercriminals utilizing phishing attacks to the covert tactics of malware and the disruptive potential of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, including Phishing, Zero-Day Exploits, Man-in-the-Middle, and SQL Injection Attacks, the cybersecurity battleground is ever-expanding. The study systematically categorizes cyber threats, scrutinizes their distinctive characteristics, and elucidates the modus operandi of each attack type. Through a meticulous dissection of cybercriminal methods and motivations and a comprehensive evaluation of countermeasure efficacy, this review offers indispensable insights for securing our digital future in an era marked by escalating interconnectivity and technological dependence.]]>
<![CDATA[Prediction of Women's Potential Type 2 Diabetes with Similarity Classifier Based on P-Probabilistic Extension ]]>
<![CDATA[Dewi, Ratih Kartika]]>;
<![CDATA[Wardhani, Shinta Kusuma]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.9945
<![CDATA[Diabetes is a chronic disease that occurs when the pancreas can’t produce enough insulin or when the insulin hormone can’t be used effectively by the body. The condition of the increased blood sugar, known as hyperglycemia, is a short-term impact that often occurs in uncontrolled diabetes. Meanwhile, the long-term impact of uncontrolled diabetes can cause damage to various body systems, especially blood vessels and nerves. Early detection of diabetes in individuals who are susceptible to diabetes is the main key to control diabetes issues. Artificial intelligence can help this issue. Early diabetes detection with artificial intelligence can predict whether a person in the next 5 years has the potential to suffer from diabetes type 2 or not, based on six variables including 2-hour plasma glucose concentration in the oral glucose tolerance test, diastolic blood pressure, fold thickness triceps, body mass index, diabetes pedigree function, and age. The prediction was built by using similarity classifier based on p-probabilistic extension, trained with the Pima Indian Diabetes dataset with women as research subjects. The contribution of this research is to select representative features in the Pima Indian diabetes dataset then implement them with similarity classifier based on P-Probabilistic Extension. The aim of this study is to compare similarity classifier algorithm with K-nearest neighbor as classifier that widely used in Pima Indian diabetes dataset. The test scenario is carried out by dividing 70% of the training data and 30% of the testing data, then the accuracy for the Pima Indian diabetes data will be compared with K-nearest neighbor and the similarity classifier. Accuracy shows a success value of 75.38%, so the similarity classifier that is built can be used to predict potential diabetes with better performance than K-nearest neighbor.]]>
<![CDATA[Decision Support System for Single Tuition Scholarship Awardees in Higher Education Using Mamdani Fuzzy Inference ]]>
<![CDATA[Kholifaturrahman, M.]]>;
<![CDATA[Rachmatullah, Sholeh]]>;
<![CDATA[Said, Badar]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July ]]>
Publisher : <![CDATA[Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30996/jitcs.10009
<![CDATA[Universitas Madura implements the Single Tuition Fee (UKT) scholarship program in the Informatics department. The current UKT scholarship selection system uses a traditional model that is still not effective, causing obstacles such as inflexibility in registration time. The print-out documents are vulnerable to damage or loss and difficulty searching when it required. The criteria in the current system consisted of a minimum Grade Point Average (GPA) of 3, a letter of family condition, and also student status in semesters 3 to 7. The three criteria are not enough to determine a scholarship candidate. The recruitment process involves only the Head of the Study Program (Kaprodi). The Informatics study program still has many candidates applying for scholarships reaching around 280%. This research proposes a Decision Support System (DSS) using Fuzzy Mamdani with six criteria, including GPA, Achievement, Parents' Income, Parents' Dependents, Semester, and History of not receiving scholarships with the aim of overcoming these problems. The results show that the performance of the proposed SPK is very good, it is shown by the MAPE value of less than 10% and more efficient time than the current system. This system has also been in accordance with the required functions through the black test.]]>
