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[Classification Techniques in Finding Malignant Breast Cancer Detection ]]>
<![CDATA[Whardana, Adithya Kusuma]]>;
<![CDATA[Mufti, Abdul Latief]]>;
<![CDATA[Hermawan, Hendar]]>;
<![CDATA[Aziz, Umar Alfaruq Abdul]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): 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.8829
<![CDATA[The most fundamental aspect of cancer is that it is marked by abnormal and uncontrolled cell growth, allowing it to spread to the surrounding areas of existing tissues. One of the most common cancers experienced by people in Indonesia, according to the Indonesian Ministry of Health, is breast cancer. The diagnosis of diseases, especially cancer, also requires a visual form that is later used as an image to determine the condition within the patient's organs. The use of mammography images is one implementation of X-rays aimed at revealing the structure of human bones and tissues. The use of images is also recognized in information technology in the field of digital image processing, which is useful for analyzing, enhancing, compressing, and reconstructing images using a collection of computational techniques. One application of digital image processing techniques for breast mammography images is recognizing the possibility of breast cancer through computer automation using classification methods supported by googlepredict.net architectures. The results obtained in this study use a dataset sourced from King Abdul Aziz University, totaling 2378 images. The method used in this research is Convolutional Neural Network (CNN), with the addition of the GoogleNet architecture. The convolution extraction method runs with the GoogleNet architecture, enhancing deep learning for optimal breast cancer recognition. The overall results of this study found an average precision value of 90%, recall of 92%, F-1 Score of 91.49%, and accuracy of 91.49%.]]>
<![CDATA[Agile Implementation for Inventory (Case study: Business Unit of Private University) ]]>
<![CDATA[Swastyastu, Cempaka Ananggadipa]]>;
<![CDATA[Shanty, Ratna Nur Tiara]]>;
<![CDATA[Sari, Rika Puspita]]>;
<![CDATA[Wikanningrum, Anggit]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): 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.10060
<![CDATA[The Unitomo Employee and Lecturer Business unit in the city of Surabaya, Indonesia, has several problems arising from the implementation of a manual system in the form of a paper-based system, which has the potential risks, such as 1) Damage and Risk of data loss, 2) Ineffectiveness in making reports; 3) Increased risk of human error caused by transaction volume, lack of management, and non-integration data; 4) Limited user accesses to the ledger, Time-consuming processes, and no collaboration. This research aims to overcome this problem by developing an inventory recording system using an Agile approach with the Scrum framework, PHP with the Slim 3 framework, MySQL, black box testing, and adding revenue and sales features as a difference from previous research. The research results show that the system developed was successful in helping Unitomo Business unit employees and lecturers overcome their problems, especially in monitoring the amount of inventory stock. The proposed system has features for recording incoming and outgoing goods transactions and sales and income reports. The use of the Agile Scrum method in software development helps teams in project planning and monitoring progress throughout the design process.]]>
<![CDATA[Efficiency in Cloud Computing through Serverless and Green Computing based on Microarchitecture ]]>
<![CDATA[Fahira, Fahira]]>;
<![CDATA[Awangga, Rolly Maulana]]>;
<![CDATA[Gopikrishnan, Sundaram]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): 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.10479
<![CDATA[PT Pelindo Multi Terminal is a subholding of PT Pelabuhan Indonesia (Persero), a State-Owned Enter-prise (SOE). PT Pelindo Multi Terminal carries out Kesehatan dan Keselamatan Kerja (K3) or Occupa-tional Health and Safety (OHS) monitoring, which currently still uses manual methods with paper. This method causes problems, such as delays in decision making and the inability to monitor events in real-time. This research aims to overcome these problems by proposing an application called "Portsafe+". Portsafe+ is developed using microservices architecture and micro frontend, with Progressive Web Apps (PWA) as the interface and Google Cloud Function as the backend. Portsafe+ was tested by measuring the response speed of the backend that responds to each request. The test results show that this application improves the response speed with 99% execution time of 880.37 ms. Based on the test results, Portsafe+ successfully overcomes the existing problems. The application of PWA technol-ogy facilitates access and improves the efficiency of OHS management compared to the previously used paper-based manual system.]]>
<![CDATA[Supervised Learning Methods Comparison for Android Malware Detection Based on System Calls Referring to ARM (32-bit/EABI) Table ]]>
<![CDATA[Alhamri, Rinanza Zulmy]]>;
<![CDATA[Cinderatama, Toga Aldila]]>;
<![CDATA[Eliyen, Kunti]]>;
<![CDATA[Izzah, Abidatul]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): 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.10511
<![CDATA[Android malware detection research is a topic that is still being developed. From all the detection techniques developed, dynamic analysis methods have become interesting because they trace the suspect application system calls. Based on the system calls, by utilizing machine learning, the suspect application can be classified as malware or benign. Comparing the machine learning methods is im-portant to determine what method is best to support malware detection. This article aims to explain more clearly and simply the way to conduct Android malware detection based on system calls step by step using classification. Furthermore, it presents the system calls sequence conversion referring to the arm(32-bit/EABI) table, which has 398 system calls (0-397) as features. It will provide a compari-son of several supervised machine-learning methods for classifying Android applications. This initial research is part of the other research that has the purpose of developing a malware detection system based on an Android application. This research can be used to develop the best machine learning to classify malware applications using a Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes (NB). The result can be concluded that the KNN method has the lowest performance in detecting Android malware apps, with an accuracy of only 0.50. In comparison, the NB method has an accuracy of only 0,69. SVM and DT models have similar accuracy and recall results of 0.79 and 0.75, respectively, but DT obtained higher precision and scores of 0.83 and 0.76, respectively. Although in this study, the classification performance of DT is better than SVM, based on comparison with the results of previous research, SVM is a suitable method for Android malware de-tection based on system calls. It is proven by the results of research comparisons that the SVM method is always the method with the highest accuracy score among other methods. For the next research, the SVM method can be used to develop a malware detection system for Android applications.]]>
<![CDATA[Identifying Dominant Actors of Ferdy Sambo's Case Network on Social Media X/Twitter Using Social Network Analysis for Public Relations Strategy ]]>
<![CDATA[Prastiti, Novi]]>;
<![CDATA[Satoto, Budi Dwi]]>;
<![CDATA[Efendi, Moch Rizal]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): 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.10852
<![CDATA[The Indonesian National Police (Polri) has experienced ups and downs in building a positive image in interacting with the public. This decline in trust is caused by the emergence of various issues that show the low performance of the police. In the Ferdy Sambo case study, the performance and integrity of the police is at stake and the sensitivity of the police to meet public expectations. One solution to im-prove the image is through an effective public relations strategy. However, to develop it, a deep un-derstanding of the characteristics and interaction patterns between social media through social net-work analysis is required. This research aims to identify influential X/Twitter actors in the case study of Inspector General Ferdy Sambo by applying the centrality method in Social Network User Analysis. The results of centrality analysis on the network show a wide variety of centrality levels. The @Zaindamai account dominates with the highest Degree Centrality value of 0.426829, indicating the number of connections in the network. The main role in disseminating information is held by @Zaindamai with the highest Betweenness Centrality value of 0.325748, indicating its role in connect-ing various networks. @Rizkynu46127931 stands out in Closeness Centrality with a high value of 0.497791, signifying quick and efficient access to all parts of the social network. In addition, @Rizkynu46127931 has significant influence in the network based on the highest Eigenvector Cen-trality of 0.245625. This centrality value forms the basis for formulating a more focused public relations strategy, improving the efficiency of communication with stakeholders, and designing a more concrete public relations plan.]]>
<![CDATA[Enhancing Project Management and Monitoring with Precedence Diagram and Earned Value Method: A Case Study in Construction ]]>
<![CDATA[Rachmatullah, Sholeh]]>;
<![CDATA[Said, Badar]]>;
<![CDATA[Ramadhani, Nilam]]>;
<![CDATA[Alvionita, Vina]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): 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.11801
<![CDATA[An increasing number of projects has created challenges in effectively planning, organizing, and maintaining projects. Project planning becomes a crucial aspect of project management, particularly for monitoring progress and efficiently allocating resources. This study aims to implement the precedence diagram and earned value methods to enhance project management and monitoring. The predence diagram method is used to estimate project completion time, while the earned value method tracks time and budget according to project progress. In this case study, project management activities at CV. Firman Syah are observed. The development process starts with data collection and requirements analysis (functional and non-functional), followed by system architecture design using use case diagrams and Data Flow Diagram (DFD). The database is implemented using MySQL, and the application runs on an Apache web server. Vue.js is used for front-end development, while Lumen is employed for back-end development. The results of Black Box testing indicate that the application is capable of predicting project completion time and associated costs, as well as managing project data.]]>
<![CDATA[Utilizing Prolog for Automatic Transformation of English Words ]]>
<![CDATA[Barkah, Nida Muhliya]]>;
<![CDATA[Siregar, Maria Ulfah]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): 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.11936
<![CDATA[Artificial Intelligence is a branch of computer science that focuses on developing systems that emulate human intelligence to execute commands or tasks by applying the concept of automatic reasoning. Automatic reasoning is the ability of a system to draw logical conclusions, make decisions, and solve problems independently, without human intervention, based on provided information or rules. The complexity of grammatical rules in English often causes difficulties in understanding and remembering them, hindering mastery of correct English structures. To address these challenges, this research uses automatic reasoning as the primary foundation for developing automatic transformation systems for English words—specifically, transforming singular nouns into plural forms and adjectives into adverbs of manner. The objective of this research is to apply grammatical rules during word transformation in Prolog. Furthermore, this study tests the effectiveness and accuracy of the process by evaluating the success rate of correct transformations. The system's performance is assessed by comparing the transformed words to their correct plural forms and corresponding adverbs of manner, based on predefined grammatical rules. This research does not involve participants directly but evaluates the system's performance on a set of predefined words. The results demonstrate that the system successfully transformed all tested words with 100% accuracy, effectively altering their structure. This indicates that the system is both effective and reliable for use in English language learning. Additionally, the system's high accuracy in handling various morphological transformations makes it a valuable tool for improving English grammar and writing skills.]]>
<![CDATA[Risk Management Analysis of Information Security in an Academic Information System at a Public University in Indonesia: Implementation of ISO/IEC 27005:2018 and ISO/IEC 27001:2013 Security Controls ]]>
<![CDATA[Meitarice, Sonya]]>;
<![CDATA[Febyana, Lidya]]>;
<![CDATA[Fitriansyah, Aidil]]>;
<![CDATA[Kurniawan, Rahmad]]>;
<![CDATA[Nugroho, Riki Ario]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): 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.12099
<![CDATA[An online academic information system is potentially exposed to various threats from internal and external sources, which may compromise the institution's objectives if not managed effectively and appropriately. Academic portals often experience issues such as server downtime and unauthorised access attempts. However, there is no specific documentation dedicated to managing these issues. This study aims to analyze risk management in information security for the academic portal of Universitas Riau, Indonesia. The study employs the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 27005:2018 standard and ISO/IEC 27001:2013 security controls, following four key stages: context establishment, risk assessment, risk treatment, and recommendations. The findings identify eight categories of information system assets, 30 identified threats, and 43 vulnerabilities, including two high-risk categories, 19 medium-risk categories, and 22 low-risk categories. Of the 43 vulnerabilities, 21 risks required risk modification, four required risk avoidance, and four required risk sharing. Fourteen risks, which can be managed through risk retention (acceptance of risk), fall under the category of risk acceptance. Furthermore, ISO/IEC 27001 suggests that implementing control recommendations can minimize and effectively address these risks. Nevertheless, this study focuses primarily on information security risks and does not extensively cover related areas such as data privacy, regulatory compliance, or operational risks. Future research can explore the effectiveness of training programs and awareness campaigns in reducing human-related risks, such as phishing and social engineering attacks.]]>
<![CDATA[Comparison of Dimensionality Reduction Techniques to Improve Performance and Efficiency of Logistic Regression in Network Anomaly Detection ]]>
<![CDATA[Ahfa, Mokhamad Isna Marzuki]]>;
<![CDATA[Hakim, Lukman]]>;
<![CDATA[Rosadi, Muhammad Imron]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 3 No. 1 (2025): 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.12212
<![CDATA[Network anomaly detection is a crucial process to identify abnormal network traffic, which may pose a security threat. This research aims to improve the performance and efficiency of Logistic Regression (LR) in network anomaly detection by applying dimension reduction techniques, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (TSVD), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). The performance of each dimension reduction method is evaluated based on accuracy, precision, recall, F1-score, and computation time. The results show that TSVD provides the best performance with 95.86% accuracy, 0.96 precision, 0.96 recall, 0.95 F1-score, and 13.83 seconds computation time. In contrast, ICA showed the worst performance, especially in precision, recall, and F1-score, with values of 0.73, 0.83, and 0.78, respectively. Meanwhile, although t-SNE produces competitive accuracy, it has a high computational cost with an execution time of 1698.54 seconds. These findings show that choosing the right dimension reduction algorithm not only improves detection performance but also supports data processing efficiency, making it highly relevant for large-scale network security scenarios. Keywords: dimensionality reduction, Logistic Regression, network anamoly detection, performance evaluation, Truncated Singular Value Decomposition.]]>
<![CDATA[The Waterfall Model in the Implementation of a Room Reservation Information System: A Case Study at a University ]]>
<![CDATA[Setiawan, Akas Bagus]]>;
<![CDATA[Rizaldi, Taufiq]]>
<![CDATA[ Journal of Information Technology and Cyber Security Vol. 2 No. 2 (2024): 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.12248
<![CDATA[Politeknik Negeri Jember (National State of Jember Polytechnic), Indonesia, fully understand that infrastructure is one among various key factors to enhance the quality of the university. However, in the department of Information Technology, infrastructure management is a prominent issue. The primary cause of the issue appears to be a manual arrangement of room reservations, as room usage often crashes due to the scheduling mismatch. To tackle this issue, this study focuses on the development of a room reservation information system, known as “Sistem Informasi Peminjaman Ruangan dan Sarana Pembelajaran (SIPRu)”. The system is developed as an additional feature in the department website. It is developed using the Waterfall method, beginning with the requirements analysis (i.e., an identification of functional and non-functional requirements) and architectural processes design processes (i.e., the development of use case diagram, flowchart, data flow diagram, and database design). In this study, Laravel is used for the implementation, and black box testing is used to verify compliance with the functional requirements. During the testing process, 42 department stakeholders participated to validate the requirements and conduct user experience testing. The results indicated that the stakeholders have found the website to be sufficiently responsive to their needs. Furthermore, it is revealed that SIPRu is effective at assisting users in reserving rooms at the department of Information Technology, National State of Jember Polytechnic.]]>
