Journal of Information Systems and Informatics (Journal-ISI)
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Enhancing IT Change Management through Communities of Practice and Social Learning: A Case Study at a University
Get PDFAs information technology (IT) is taking over numerous aspects of our lives, handling IT changes becomes more and more urgent for the higher education institutions. The study that aims to explore the influence of social learning within a Community of Practice (CoP) in IT change management at the University X in Jakarta was conducted. Through a case study approach that involves document analyses, semi-structured interviews, and participant observations, it was shown the pivotal role of CoPs in facilitating the IT changes by promoting social interaction and collaboration. The CoP can help to find the problem early and build a risk-reduction plan around it. This research finds that despite challenges maintaining consistent procedures, inter-departmental coordination, and the necessity of broadened training and communication, integrating CoPs with COBIT 2019 principles can offer a unified approach to IT transformation for the universities. The integration will allow universities to have better plan around changes in technology and offers practical examples for other higer education institutions
Determinants of E-participation in Government Initiatives based on Theory of Planned Behaviour: Insights from Guyana
Get PDFE-participation is growing increasingly relevant as a tool that facilitates citizens’ participation in policymaking and decision-making activities while studies surrounding the intention of citizens to engage with e-participation in developing countries remain limited. Thus, it is essential to understand the factors that may or may not influence a citizen’s intention to engage with e-participation initiatives in order to build successful initiatives. This study proposes a conceptual model that extends Theory of Planned Behaviour to incorporate the construct, Trust in Technology. Using data collected from an online survey of 344 Guyanese citizens, the model was tested and validated using Partial Least Square - Structural Equational Modelling (PLS-SEM). The quantitative results proved that citizens with stronger perceived behavioural control and subjective norms positively affects the intention to engage with e-participation. Additionally, the study found that attitude and trust in technology have no significant effect on citizen intention. The findings presented in this document present a vivid idea of the factors that impact citizens' intentions to participate in e-participation programmes in Guyana. These findings can help practitioners in designing effective and efficient e-participation programs
Transformation of Consumer Behavior Through Smart City Technology: A Literature Review
Get PDFOnce city implements a smart city, the transformation that occurs not only impacts the city's infrastructure and operations, but also significantly influences individual interactions with public facilities and their consumption patterns. This literature review aims to identify changes in consumer behavior and daily activities after the implementation of smart city technology. The methodology used is PRISMA, with references published over the last decade. The research results show changes in various aspects, including mobility, energy efficiency, citizen engagement, environmental awareness, shopping experience, quality of life, education and information, business prospects, and response to the crisis. These findings show that smart city technology brings positive changes in the daily lives of city residents, which are influenced by the use of technology and the way it is implemented by the community. This research provides insight for policy makers and city managers to understand the broad impact of smart cities on community behavior
Application of Content-Based Filtering Method Using Cosine Similarity in Restaurant Selection Recommendation System
Get PDFThis research focuses on developing a restaurant recommender system designed to assist users in selecting restaurants based on preferences such as cuisine type and proximity, thereby enhancing the dining experience. The system employs a content-based filtering approach combined with the Cosine Similarity algorithm to calculate similarity values between restaurant addresses and categories, ensuring personalized and accurate recommendations. Data for the system was collected from TripAdvisor and Google Maps using a web scraping method, resulting in a comprehensive dataset that reflects a wide variety of dining options. An experiment involving 30 respondents was conducted to evaluate the system's performance under real-world conditions. The results demonstrated an accuracy rate of 88%, indicating that the recommender system effectively delivers highly relevant restaurant suggestions to users. These findings suggest that the system can serve as a valuable tool for culinary tourists and local residents, simplifying the process of discovering new dining experiences and aligning them with individual preferences
Creating Realistic Human Avatars for Social Virtual Environments Using Photographic Inputs
Get PDFThis paper presents the development and evaluation of realistic virtual reality avatars created with a Blender add-on called Facebuilder. In this process, a person's head is photographed from different angles. These photographs are used in subsequent steps to generate a realistic avatar face. To investigate the user experience of interacting with these avatars, a study was conducted in VR using the MyScore application. The study involved 22 participants who met in a virtual environment to discuss a topic of their choice. Statistical analyses including descriptive statistics, Wilcoxon Signed-Rank Test, and Friedman Test showed significant differences supporting all three hypotheses: users preferred communicating with realistic avatars, were more focused and engaged when interacting with them. The results indicate a significant preference for realistic avatars in educational use cases, primarily due to the perceived seriousness of the interactions and the resulting higher level of participant engagement. The suitability of realistic versus non-realistic avatars was found to be use-case dependent. Participants suggested that realistic avatars would be more appropriate for educational scenarios and non-realistic avatars for entertainment
Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis
Get PDFIn today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments
Comparing CNN Models for Rice Disease Detection: ResNet50, VGG16, and MobileNetV3-Small
Get PDFThe Oryza sativa (rice) plant is an important staple food source, especially in the Asian region. Rice production is often disrupted by diseases such as Brown Spot, Leaf Scald, Rice Blast, Rice Tungro, and Sheath Blight, which can reduce yield and crop quality. This research aims to classify rice plant diseases using a deep learning approach with Convolutional Neural Networks (CNN) architecture, namely ResNet50, VGG16, and MobileNetV3-Small. The dataset used is Rice Leaf Disease Classification which consists of 1305 images with five disease labels. The data is divided into training, validation, and testing sets with proportions of 70%, 15%, and 15%. The results showed that the MobileNetV3-Small model provided the best accuracy on the test data of 79%, while VGG16 achieved the validation accuracy of 78.84%. Based on these results, MobileNetV3-Small is considered the most superior model for rice disease classification. This research shows the great potential of applying deep learning in automatic rice disease detection
A Balancing Energy Efficiency and Security in CR-LoRaWAN Ecosystems
No full textCognitive Radio-enabled Long Range Wide Area Networks (CR-LoRaWAN) plays an important role in IoT applications. However, due to the limitations of devices and dynamic scheduling mechanisms of the channels, there is still a challenge to balance energy efficiency against security. This paper proposes two developed algorithms that address these challenges: Algo A and Algo B. Algo A ensures key security by mitigating nonce generation vulnerabilities through the replacement of insecure random numbers with prime numbers. Algo B develops this basis by further improving energy efficiency through optimization in session key generation and device management, adding security to it. Both the algorithms incorporate prime numbers in their session key generation that are verified by the Rabin-Miller test and the Sieve of Eratosthenes, with incorporated solar energy harvesting to give a longer life to such devices. Cognitive radio technology is integrated into it for dynamic and intelligent channel selection. Extensive simulations demonstrate that Algo A is much better at handling data with key security, while Algo B outperforms Algo A on energy consumption reduction by 20% and enhancement of overall network security by 15%. These results reveal that Algo B has a better trade-off between security and energy efficiency; hence, Algo B is more suitable for practical deployment. The work further enhances the sustainability and reliability of CR-LoRaWAN networks, especially in resource-constrained environments
Selecting Achievement-Based Students Using Blockchain and AHP: Semarang University Case Study
Get PDFThe current learning process requires students to be active, creative, and innovative both in activities and lectures. Students are one of the main indicators in the accreditation of a university. Therefore, a tertiary institution is also required to be able to receive and provide assistance to students and graduates in the form of scholarships, such as achievement scholarships. At present, it is quite difficult to determine students who have excelled at tertiary institutions in considering and determining students who are entitled and appropriate to receive them. The Decision Support System is a solution to be able to assist in managing student achievement data and determining the right scholarship recipients, and is supported by blockchain technology so that security is guaranteed and protected so that it is not easily hacked and information can be tracked by parties who are given access, so that they can share information transparently. This study uses the waterfall system development method with the Analytcal Hierarchy Process (AHP) problem solving method for the analysis model of the decision support system , blockchain algorithms, and open source software used solidity ethreum. The actual results will be that a decision support system with the AHP model combined with Blockchain can help provide more precise decisions to determine more appropriate scholarship recipient students, as well as increase trust in parties in tracking every transaction information on student achievement activities transparently and safely
Improving IT Service Management (ITSM) Capability in Small Application Development Firms Using FitSM: A Case Study Integrated with Socio-Technical Systems Theory
Get PDFThis paper looks at how the FitSM framework, together with Socio-Technical Systems (STS) theory, can help improve IT Service Management (ITSM) in a small software development firm. Limited resources often cause small businesses to manage services inefficiently, which leads to inconsistent delivery and dissatisfaction among clients. The study focuses on a single software company in Jakarta, where ITSM processes like incident management, change management, and configuration management were assessed. The research involved interviews with key staff, an evaluation of current practices, and a gap analysis to identify areas needing improvement. The results show that most processes are at an early stage, with some progress in Configuration Management. By using FitSM’s structured approach and addressing teamwork and communication issues through STS theory, the company can improve service reliability and efficiency. The study concludes that combining FitSM with socio-technical principles provides a practical solution for small companies to enhance their ITSM practices and overall service quality