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.
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Penetration Testing and Vulnerability Analysis of SINTA Platform to Strengthen Privacy and Data Protection
Supangat, Supangat;
Amna, Anis Rahmawati;
Rochman, Mochamad Yovi Fatchur
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.12216
The increasing reliance on digital platforms for academic and governmental purposes necessitates robust cybersecurity measures. Consequently, identifying vulnerability is critical to ensuring data security and providing actionable recommendations for cybersecurity officers. Platforms like Sinta (Science and Technology Index), which focus on collecting peer-reviewed papers and maintaining researcher’s research records, represents significant governmental contributions in academia. Cybersecurity awareness is demonstrated through events organized to evaluate the vulnerability of the platform, enabling researchers to access its security and report potential issues. This study addresses these concerns by conducting system penetration testing using the OWASP and Burp Suite Framework, focusing on identifying five critical vulnerabilities. The evaluation examines issues, such as sensitive data exposure in API responses, error log disclosures, email enumeration, and improper access to system files. The results reveal that the platform suffers from multiple levels of security vulnerabilities, prompting recommendations for authorities to take actions to mitigate potential risks effectively.
A Comparison of Polynomial Regression and Support Vector Regression for Predicting the Consumer Price Index Based on Food Commodity Prices in East Java, Indonesia
Suyono, Ayu Adelina
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.12353
Food price fluctuations occur almost daily and directly affect purchasing power as well as the stability of regional and national economies. As one of the largest provinces in Indonesia, East Java, which significantly contributes to national GDP, has diverse economic structures and highly sensitive to price changes. Given this situation, government needs more accurate prediction methods to monitor Consumer Price Index (CPI) movement as a basis for establishing more appropriate economic strategy and policy. This study aims to compare the performance of Polynomial Regression (PR) and Support Vector Regression (SVR) in predicting CPI using food price data from SISKAPERBAPO for the 2014 - 2020 period, covering regencies and cities in East Java. To ensure the quality of the analysis, missing values were removed. A Pearson’s r correlation analysis was then conducted to assess the relationships between food prices and CPI. The model obtained was then evaluated using mean squared error (MSE), root mean square error (RMSE), Mean absolute percentage error (MAPE), and computation time. The results shows that third order PR achieved higher accuracy with MAPE of 0.3% (training) and 3.4% (testing), while SVR performed lower with MAPE of 5.9% (training) and 6.0% (testing). In addition, PR was more computationally efficient than SVR. These findings underscore PR as a more reliable method for predicting CPI using complex regional food data.
Payroll Decision Support System for Production Employees Based on Key Performance Indicators Using the Simple Additive Weighting Method
Suganda, Tegar Bangun;
Dzikria, Intan
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.12523
CV. XYZ is a Micro, Small, and Medium Enterprise (MSME) engaged in plastic sack sewing services and implements a piece rate pay system. The company faces obstacles in managing performance-based payroll, which impacts operational efficiency and payment accuracy. This study aims to develop a Payroll Decision Support System (DSS) to record and evaluate the work results of production employees using Key Performance Indicators (KPIs). KPI evaluation is applied as the main criteria in weighting using the Simple Additive Weighting (SAW) method, to produce optimal performance scores. The implementation of KPI-based incentives is expected to improve employee performance while encouraging a productive and transparent work culture. Blackbox testing shows that 97% of the test data meets user requirements, indicating that the system can run according to specifications. The results of this study contribute to the integration of KPIs and SAW in assessing production employee performance for fair and effective payroll decision-making, as well as helping companies implement efficient management practices.
Development of a Web-Based Spare Parts Inventory System Using the First-In, First-Out Method and Acceptance Analysis Based on the Unified Theory of Acceptance and Use of Technology
Firdaus, Adam;
Swastyastu, Cempaka Ananggadipa;
Shanty, Ratna Nur Tiara
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.13042
As a service-oriented company, Jaya Sentosa faces significant challenges in managing its spare parts inventory, leading to resource inefficiencies. Key issues include documentation discrepancies, inventory inconsistencies, and inadequate monitoring mechanisms. To address these problems, this study designs and implements a web-based information system, referred to as SIWEB-JS, aimed at enhancing inventory accuracy and operational efficiency. The system was developed using the Rapid Application Development (RAD) approach, adopting the First-In, First-Out (FIFO) principle, and built with PHP using the CodeIgniter v4.4.4 framework, supported by a MySQL database. System functionality was evaluated through Black-Box Testing, while user acceptance was assessed using the Unified Theory of Acceptance and Use of Technology (UTAUT). The results indicate that SIWEB-JS effectively records real-time transactions, maintains accurate inventory levels, and improves data accessibility. The implementation of FIFO contributed to better inventory governance. Furthermore, the UTAUT-based evaluation revealed that Performance Expectancy (PE), Effort Expectancy (EE), and Social Influence (SI) significantly influenced Behavioral Intention (BI), suggesting that the system is perceived as useful, user-friendly, and well-supported by users.
Early Breast Cancer Detection Using Gabor Filter and Convolutional Neural Network for Microcalcification Identification
Mufti, Abdul Latief;
Whardana, Adithya Kusuma
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya
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DOI: 10.30996/jitcs.132037
Breast cancer poses a considerable challenge in Indonesia, resulting in numerous fatalities. This study aims to improve the accuracy and efficiency of early breast cancer diagnosis by leveraging modern image processing and artificial intelligence. The dataset used is the Mini-DDSM (Mini Digital Database for Screening Mammography), taken from Kaggle and vetted by radiologists into a Region of Interest (ROI) consisting of three categories: Benign, Cancer, and Normal. The methodology encompasses comprehensive image preprocessing, which includes resizing, cropping, RGB-to-grayscale conversion, Laplacian of Gaussian (LoG) filtering, Gabor filtering, global threshold segmentation, and image enhancement. A Convolutional Neural Network (CNN) is employed for classification purposes. Ninety percent of the images are allocated for training, while 10% are designated for testing, with critical parameters such as learning rate, batch size, and epochs being tuned throughout the training process. The CNN architecture was assessed based on recognition rate, error rate, epoch count, and training duration. The results provide a flawless validation accuracy of 100% over 32 trials. The findings demonstrate that the suggested method markedly enhances early breast cancer identification using microcalcification analysis in mammography images, assisting medical professionals in early diagnosis and potentially elevating patient recovery rates through prompt detection and treatment.