Journal of Information Systems and Informatics (Journal-ISI)
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Enterprise Architecture Model for Smart Government Implementation
Get PDFThis research aims to develop an enterprise architecture (EA) model to support the implementation of Smart Government that utilizes information and communication technology (ICT) to enhance efficiency, transparency, and public participation in governmental processes. The development of this EA model adopts a holistic approach, integrating various components of technology, organization, and business processes within the context of government to provide guidance for government agencies in planning, implementing, and managing the digital transformation required to achieve Smart Government. The findings of this study indicate that the proposed enterprise architecture model offers a clear and flexible structure to support the integration of government agencies, public service providers, and citizens. It is expected that by utilizing this EA model, governments can improve public services, operational efficiency, and create a more transparent and accountable environment. By leveraging this EA model, government agencies can streamline processes, enhance decision-making through data-driven insights, and foster greater inter-agency collaboration. It is expected that this model will improve public sector services by increasing accessibility, reducing administrative burdens, and optimizing resource utilization. Furthermore, the adoption of this model is anticipated to accelerate the digital transformation of the public sector, driving significant improvements in government service delivery, responsiveness, and citizen engagement
Exploring the Impact of IoT and Blockchain on Supply Chain Management in Developing Countries
Get PDFThe rapid development of the Fourth Industrial Revolution is having diverse effects on underdeveloped nations, influencing them in various ways. Developed countries have an advantage over underdeveloped countries since they embraced industrialization earlier, widening the gap between them. This comprehensive survey paper examines the multifaceted landscape of industry 4.0 in supply chain, shedding light on the potential challenges and key value drivers in the context of a developing country. Findings revealed that inadequate digital infrastructure, limited access to electricity, and a shortage of skilled workforce are the primary challenges faced by developing countries in the supply chain domain. The study systematically examines industry 4.0 technologies and indicates a 20-30% improvement in supply chain efficiency through the adoption of key technologies like IoT, AI, and blockchain. The study concludes by offering future research on industry 4.0 in supply chain management. The study results are assumed to offer insightful information to supply chain managers in developing countries, by enabling them with a deeper understanding of the major challenges and key drivers involved in integrating Industry 4.0 in their organizations and network
Optimization of Backpropagation (BP) Weight Values Using Particle Swarm Optimization (PSO) to Predict KIP Scholarship Recipients
Get PDFThe Indonesia Smart Card (KIP) Lecture program aims to improve the quality of human resources by providing educational assistance to students from underprivileged families. However, the distribution of KIP Lecture in Palembang still faces problems, such as inaccurate targeting and lack of public understanding of this program. The selection process for scholarship recipients is not optimal, causing students who should be prioritized to be overlooked. In addition, decision-making takes a long time due to the many variables that must be considered and the lack of transparency in data processing. This research discusses the Backpropagation (BP) method for predicting KIP College scholarship recipients, which has previously been applied to the classification of educational aid recipients with high accuracies results. However, BP has disadvantages such as minimum local risk and long training time. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights of the BP artificial neural network. PSO is a simple but effective optimization algorithm to find optimal weights more quickly and accurately. The results of previous studies show that the combination of BP with PSO can improve prediction accuracy compared to using BP alone. Therefore, this research aims to develop a more efficient and targeted prediction model for KIP College scholarship recipients through BP optimization using PSO, so that the selection process can be carried out more quickly and accurately
The Trajectory of Scaled Agile Research: A Bibliometric Analysis and Visualization Approach
Get PDFModern project management in organisations is moving towards Scaled Agile to achieve success. Scaled Agile refers to a set of organisational structures and processes for implementing agile practices that are applied on an enterprise scale. This study explores Scaled Agile growth and impact by analysing 238 publications obtained from the Scopus databases using bibliometric analysis. The results show that publications on Scaled Agile have steadily increased, with more contributions from developed nations than developing countries. In terms of the geographic distribution of publications, Germany is the leading followed by Sweden and the United States. The results also show that Scaled Agile is being applied across different fields, but is dominated by computer science, engineering, and business. We visualized the high-frequency terms using a word cloud and the keyword co-occurrence map, and a density map using VOSViewer. The h-index of 21 for the analysed articles indicates the significant scholarly impact of the publications. The study identified the following key themes: team dynamics, organisational structures, and practical applications of Scaled Agile. The study also identifies the major challenges associated with Scaled Agile, namely cultural issues and scalability issues, effective organisational design, and change management strategies. The findings of this study offer valuable insights into the current state of Scaled Agile that appeal to industry practitioners and academics interested in Scaled Agile research and implementation
Integrating Artificial Intelligence and Social Media for English as a Foreign Language (EFL) Learning: A Study on Meta-AI’s Influence on Reading Comprehension
Get PDFThis study explores the integration of Artificial Intelligence (AI) and social media, particularly Meta-AI-enhanced WhatsApp, in enhancing reading comprehension among English as a Foreign Language (EFL) learners. Despite the growing use of AI and social media in education, there is a notable lack of empirical research examining their combined effect on language learning. To address this gap, a systematic review was conducted following the PRISMA 2020 framework. A total of 850 studies were initially identified from databases such as PubMed, Scopus, Web of Science, and Google Scholar. After applying strict inclusion and exclusion criteria, 140 studies were included in the review, with 20 selected for in-depth analysis. The findings reveal that Meta-AI-supported platforms provide personalized learning paths, adaptive feedback, and enhanced engagement, contributing significantly to the improvement of reading skills. However, challenges such as ethical concerns, reduced human interaction, and technology accessibility were also noted. This study offers valuable insights for educators and policymakers on effectively integrating AI and social media tools into EFL instruction, suggesting that technology-enhanced environments can surpass traditional methods in promoting reading comprehension and learner motivation
Implementation of a Telegram-Based Child Consultation Chatbot Using IndoBERT
Get PDFChildren’s health and development are crucial aspects that require proper attention from parents. However, many parents lack easy access to immediate consultation regarding their child's health and well-being. To address this issue, this study develops a child consultation chatbot on Telegram using the IndoBERT model. The chatbot utilizes data from Halodoc and Alodokter, structured into an intent-based format with 227 tags, 5,428 patterns, and 278 responses. The dataset undergoes preprocessing, including lowercasing, text cleaning, normalization, stopword removal, and stemming. Four preprocessing scenarios are tested, including the use of term frequency-based stopwords without applying stemming, the use of NLTK stopwords without stemming, the use of term frequency-based stopwords combined with stemming, and the use of NLTK stopwords combined with stemming. The best model, trained with an 80:20 training-validation split using term frequency-based stopwords without stemming, achieves 98% accuracy, 98.5% F1-score, 98.9% precision, and 98.5% recall. The chatbot successfully classifies user intent and ensures structured interactions through a confidence-based response mechanism. This research demonstrates that an IndoBERT-based chatbot can effectively assist parents in obtaining quick and relevant information regarding their children's health and development
Predicting Respiratory Conditions Using Random Forest and XGBoost
Get PDFThis study examines the performance of Random Forest and XGBoost in predicting the diagnosis and severity of respiratory diseases using a simulated dataset of 2,000 patient records. The models were tested on two classification tasks: identifying disease types (e.g., pneumonia, influenza) and classifying severity levels (mild, moderate, severe). Both models achieved perfect accuracy in severity classification, with 1.0000 ± 0.0000 cross-validation scores, demonstrating strong stability under balanced class distributions. However, in the diagnosis task, Random Forest underperformed on minority classes, particularly pneumonia, with a recall of 0.18 and F1-score of 0.31. XGBoost, on the other hand, achieved superior results across all classes, including minority cases, with 0.9825 ± 0.0170 cross-validation accuracy and perfect test set performance. These findings highlight XGBoost’s robustness in handling imbalanced and multiclass medical data, making it a promising candidate for clinical decision support. Future work should address class imbalance and explore explainability techniques to improve trust and transparency in real-world applications
Blockchain and IoT for Sustainable Agriculture: Innovations and Impacts
Get PDFThe integration of Blockchain and the Internet of Things (IoT) is emerging as a transformative force in sustainable agriculture. This review explores the synergistic potential of these technologies to enhance transparency, traceability, resource efficiency, and resilience in agricultural systems. We conducted a systematic review of peer-reviewed articles and conference proceedings published between 2015 and 2024, sourced from databases such as IEEE Xplore, Scopus, ScienceDirect, and SpringerLink. Studies were selected based on relevance to agricultural sustainability, the implementation of IoT and blockchain, and empirical or conceptual insights. The findings reveal that IoT devices enable real-time data collection and monitoring, while blockchain ensures secure, immutable records for supply chain transparency and smart contracts. Despite their promise, challenges persist, including high implementation costs, scalability issues, and limited digital infrastructure in rural areas. The review underscores the need for collaborative frameworks and policy support to foster adoption and recommends future research to focus on hybrid models and localized applications
A Review of Internet Use and Access for BRICS Sustainable Futures: Opportunities, Benefits, and Challenges
Get PDFAccess to the internet and modern technologies have acted as a catalyst for increasing digital literacy skills in society over the last few decades. The use of modern technologies within society has accelerated disproportionately. Digital literacy and access to modern technology products have been described as beneficial to addressing the knowledge and skills deficit. Yet, insights concerning the comparisons of BRICS member countries in addressing literacy/digital skills and sustainable and affordable access to modern technology products, as well as internet delivery, remains nascent. The current literature lacks comparative studies on internet use and access. This study uses Technology Acceptance Model (TAM) constructs to better understand internet use and access among some of the BRICS member states. The purpose of this qualitative study is to carry out a review that compares internet use and access among the five member states. It is key to understanding trends in technology products, processes, people, and real-time data sharing among BRICS members. It draws on the available reports in English on internet access and use. Then, it analyses and provides a discussion on the relationships between different countries and possible opportunities. This study concludes that the affordability of internet access remains a challenge. The challenge is further exacerbated by the demand to have access before one is at liberty to effectively use it. On the other hand, the challenge for people with access to the internet is to understand and reap the full benefits of usage. These results are specifically discussed, with implications for research and practices within BRICS member states. Several limitations to the study are presented, which in turn opens up potential future research perspectives. In conclusion, BRICS members should continue forging strong links to navigate new opportunities and identified challenges
Utilize Extreme Programming Method for Developing Financial Report Standards Apps
Get PDFMicro, Small, and Medium Enterprises (MSMEs) play a crucial role in providing employment opportunities, especially when job competition in the formal sector is intense. Eliza Catering, an MSME located in Surakarta city, operates in the culinary sector and traditionally maintains simple financial records. This practice hampers the ability to accurately measure the company's performance and determine its profitability. This research aims to document the daily transactions of Eliza Catering using the BukuKas application and to generate financial reports in accordance with Financial Accounting Standards (FAS) EMKM. The data analysis process involved three stages: data reduction, data presentation, and conclusion drawing. The findings reveal that Eliza Catering previously only recorded income, lacking comprehensive financial documentation. By utilizing the BukuKas application, daily transactions were systematically recorded. The Extreme Programming method was employed to develop this research system, resulting in the preparation of financial reports based on FAS EMKM, which include profit and loss statements, financial position reports, and notes to the financial statements