Journal of Information Systems and Informatics (Journal-ISI)
Not a member yet
580 research outputs found
Sort by
A A Comprehensive Review of Energy Optimization Techniques in the Internet of Things
No full textThe advancement of energy efficiency in the Internet of Things (IoT) and wireless sensor networks (WSNs) is an important research effort, given their rapid application expansion across smart cities and homes, healthcare, agriculture, and industrial automation. This paper conducted a comprehensive survey of existing innovative solutions to challenges focusing on hardware-based, software-driven, and network optimization approaches, alongside artificial intelligence-driven and demand-side energy management, and security-enhanced frameworks. 82 peer-reviewed journal articles and conference papers published between 2021 and 2025 were reviewed, using sources such as IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Google Scholar. It identifies significant developments in energy-efficient techniques, including ultra-low-power hardware, adaptive scheduling, bio-inspired clustering, and energy harvesting. Others include intelligent optimization methods(e.g. machine, quantum-inspired heuristics), and blockchain-enhanced security. A structured evaluation process is implemented, following PRISMA guidelines, categorizing studies, and synthesizing findings to highlight technological progress, challenges, and future research directions. The findings show a growing trend towards integrated, multi-objective routing and cross-layer energy optimizations, with significant progress in minimizing energy use, network lifetime and improving security mechanisms. However, challenges like scalability, computational overhead and real-world deployment issues persist. Our study offers valuable insights for sustainable energy management in IoT and WSNs and helps guide future development toward more resilient, adaptable and sustainable energy-aware systems
Optimizer Evaluation for Maize Leaf Disease Using Transfer Learning with MobileNetV3-Small
Full text linkManual identification of maize leaf disease presents significant challenges, including time- consuming processes, dependence on expert availability, and a high risk of misdiagnosis due to similar symptoms among different diseases. These limitations often lead to delays in disease management, unstable crop yields, and economic losses for farmers. This study aims to address these issues by evaluating the performance of different optimizers in classifying maize leaf disease using transfer learning with the MobileNetV3-Small architecture. A total of 2,850 images of maize leaf disease were used and divided into training, validation, and testing sets. Model evaluation involved systematically comparing the Adam, RMSprop, and SGD optimizers by training each configuration under identical conditions and assessing the resulting model performance. The results show that the RMSprop optimizer provides the best performance with 92.98% accuracy, 93.08% precision, 92.98% recall, and 92.98% F1-score. Based on the evaluation, selecting an appropriate optimizer is essential to improve accuracy and reliability of transfer learning models in maize leaf disease classification. These findings highlight the potential to advance smart agricultural systems by enabling more accurate disease detection, which can reduce crop failure risks and enhance disease management in maize production
The Impact of Artificial Intelligence (AI) for Transforming Tourism Marketing on the USA Industry Practices
Full text linkThis study explores the transformative role of Artificial Intelligence (AI) in tourism marketing, highlighting its ability to enhance personalization, operational efficiency, and consumer engagement. The objective is to bridge the gap between theoretical capabilities and practical applications of AI in the USA tourism marketing. The methodology employs a PRISMA-based approach, focusing on recent studies from 2020 onward to analyze AI’s impact on marketing practices. A thorough examination of 389 publications obtained from databases like Scopus, Google Scholar, and Scimago for detailed qualitative analysis. The key contribution of this paper lies in its structural approach, which discuss the potential of various AI tools such as tailored recommendations and AI chatbots etc., offering fresh insights on their influence on the American Tourism Marketing sector. The report presents a framework for assessing the impact of AI on customer satisfaction and productivity, providing pragmatic solutions for tourism enterprises. In near future AI will develop enhancing human critical thinking and converting human cognition capabilities
Implementing IT-Based Succession Planning in University IT Units: Enhancing Operational Continuity
Full text linkOne of the accredited universities in Indonesia is committed to quality education through the use of information technology. However, the university's IT unit often experiences vacancies in key positions due to high employee turnover, which impacts workload and business processes, especially in handling Request for Change (RFC). While application X supports performance appraisals, it has not been optimized for succession planning. This study explores the potential of application X as a tool for succession planning by integrating the Rothwell and Integrated Talent Management models. The design includes identifying key positions, assessing candidate competencies, preparing development plans, and establishing a structured knowledge transfer system to sustain organizational leadership. Additionally, integrating Large Language Models (LLMs) like ChatGPT is expected to enhance assessment objectivity, provide individual development recommendations, and ensure a more effective leadership transition. The system's role in improving assessment objectivity is vital for unbiased, data-driven decisions, while its contribution to leadership transitions ensures a smoother, more systematic process for maintaining leadership continuity. With features such as candidate search and staff assessment, the system is expected to help organizations select the right replacement and maintain university operations
Developing an IT-Based Knowledge Sharing System for University IT Units: Integrating Large Model Language
Full text linkInformation technology companies in Indonesia face the challenge of high employee turnover, which leads to the loss of important knowledge and has an impact on productivity and innovation. This research aims to develop conceptual knowledge sharing and knowledge sharing systems in university IT units, which do not yet have an integrated system for documenting knowledge. Observations show that the ticketing system used can be optimized as a long-term knowledge sharing platform. The designed model includes strengthening the culture of sharing, utilizing social networks within the organization, applying information technology, reward systems, and the SECI model approach. In addition to knowledge repository features, role systems, documentation automation, search, and collaboration modules, the integration of Large Language Models (LLM) such as ChatGPT is expected to improve information search, documentation automation. LLMs play a crucial role in enhancing user interactions by enabling natural language queries, improving search accuracy, and automating knowledge classification. Moreover, they facilitate knowledge extraction from unstructured data, assist in summarizing key insights, and provide adaptive learning capabilities. By leveraging LLMs, the system can increase efficiency, reduce the time required to find relevant information, and ensure knowledge continuity within the organization
Digital Mapping of Fermented Foods for the Advancement of Gastronomy Tourism in Indonesia
Full text linkThis research introduces a pioneering digital mapping framework for Indonesian fermented foods that integrates geospatial technologies with traditional gastronomic knowledge systems. Employing Rapid Application Development methodology on the Oracle APEX platform, the study establishes a comprehensive documentation infrastructure capturing the geographical distribution, production methodologies, and cultural significance of diverse fermentation practices across Indonesia's archipelagic landscape. The resulting prototype offers multifunctional capabilities through an intuitive interface design that serves preservation imperatives and tourism development objectives. Findings demonstrate that systematic digital documentation of fermented food traditions creates measurable economic opportunities through enhanced destination competitiveness, specialized culinary tourism routes, and improved market visibility for artisanal producers. The community-driven documentation protocols position local knowledge-holders as primary content contributors, while the system architecture establishes essential connections between geographical contexts and traditional fermentation techniques. This research addresses critical documentation gaps while establishing standardized protocols applicable beyond Indonesia to other regions with significant fermentation heritage. The digital mapping system ultimately functions as both a cultural preservation mechanism and a strategic asset for sustainable gastronomy tourism development, offering a replicable model for transforming endangered culinary knowledge into economically viable digital assets that benefit traditional food-producing communities
Contextual ITSM Adoption Across Educational Levels: A University and a Secondary School in Jakarta
Full text linkThis research investigates contextual ITSM adaptation across educational levels through a comparative case study of a university and secondary school in Jakarta. Using qualitative methodology with interviews, observations, and document analysis, we examined implementation patterns at University X (5,000 students, 12 IT staff) and SMA Y (450 students, 2 IT staff). Results show universities achieved semi-formal ITSM maturity levels 2-3 while secondary schools operated at pragmatic levels 1-2, reflecting resource disparities where universities allocated 4% versus 1.5% of operational budgets to IT services. Key findings reveal persistent perception gaps where academic staff predominantly view IT as "repair function" rather than strategic service, with most service requests still submitted through informal channels instead of standardized procedures. Three primary implementation challenges emerged: resource limitations, structural complexity, and cultural resistance. Based on these findings, we propose a phased transformation model (Stabilization, Standardization, Development) accommodating "layered maturity" - allowing institutions to operate at different maturity levels across ITSM domains rather than uniform advancement. This research contributes a contextual ITSM implementation framework bridging industry standards with educational realities, providing practical guidance for institutions balancing digital transformation aspirations with resource constraints, particularly relevant for developing countries facing similar educational technology challenges
Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality
Full text linkThis research applies sentiment analysis to understand user perceptions of the Access by KAI application, especially specific aspects such as speed, payment process, and user interface (UI/UX). User reviews are collected and processed through preprocessing stages, balancing using the SMOTE method, and classified using three machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, and Logistic Regression. The SVM model achieved the highest accuracy of 89.33%, followed by Logistic Regression at 88%, and Decision Tree at 86.67%. Precision, recall, and F1-scores for each model were also evaluated, showing strong performance in detecting negative sentiments but lower performance for neutral and positive sentiments. In addition, keyword-based analysis revealed that negative sentiment was most commonly found in the aspects of the payment process and speed. WordCloud visualization also strengthens the results by showing the dominance of negative words in user reviews. The results of this study provide important suggestions and input for application developers to improve aspects of the service that are considered less satisfactory by users. Thus, this study can be used as a practical guide in making strategic decisions to improve the quality of service and user satisfaction of the Access by KAI application
COBIT 2019 for Enhanced ICT Governance: A Case Study at a Higher Education Institution
Full text linkEffective ICT governance is essential for aligning technological resources with institutional objectives, especially in higher education institutions with limited resources. This study evaluates the ICT governance framework at PUSTIK STMIK Lombok using COBIT 2019, focusing on six domains: APO04, APO10, BAI02, DSS01, DSS05, and EDM01. A structured survey was conducted with 189 respondents, including faculty, students, and administrative staff, to assess capability maturity levels and identify governance gaps. The results indicate that all domains achieved Level 3 (Established), reflecting standardized processes but highlighting deficiencies in security resilience, vendor management, and operational change management. A SWOT analysis identified weaknesses such as limited proactive security measures, insufficient stakeholder engagement, and inadequate staff training, while opportunities include government funding and emerging technologies. To address these challenges, a tailored governance framework was developed, incorporating policies, standards, and procedures to enhance security, innovation management, and vendor accountability. The findings underscore the applicability of COBIT 2019 in resource-constrained educational settings and provide practical recommendations to bridge governance gaps. Future research should examine the long-term effects of governance improvements and explore the framework’s scalability across similar institutions
Enhancing Web Performance for E-learning Platform using Content Delivery Network (CDN) and Varnish Cache
Full text linkAlong with the development of technology, the web has become a very popular platform for providing information services and digital content, especially in education sector. The popularity of web services such as e-learning is directly proportional to the increasing number of users. The increase in the number of users is often a problem because it can lead to decreased web performance and potential downtime. To overcome this problem, this study proposes Content Delivery Network (CDN) and Varnish Cache as solutions. Web performance evaluation was carried out in a campus internal network using Apache JMeter with a load of 1,000 users. Based on the evaluation, there was a 175.5% increase in throughput, from 51.9 to 142.9 requests per second. In terms of response time, it improved by 54.3%, decreasing from 16,476 ms to 7,526 ms. Additionally, latency was reduced by 82.4%, from 3,555.8 ms to 624.8 ms. The error rate also decreased from 31.4% to 17.2%. These results indicate that CDN implementation can effectively improve web server performance and provide an optimal user experience, especially under high load conditions