Journal of Information Systems and Informatics (Journal-ISI)
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Model for Enhancing Cloud Computing Resource Allocation Management Using Data Analytics
Get PDFThe cloud computing environment requires an adequate and accurate traffic prediction tool to fulfill the needs of customers and support organizations effectively. In the absence of an effective tool for forecasting cloud computing traffic, many organizations might fail. It is difficult to predict the network resources that are suitable to meet the needs of all network clients at a given time in a cloud computing environment because of the inconsistent network traffic flow. There is still room for improving the predictive accuracy of the model in cloud computing. The higher the accuracy of the traffic flow, the better the allocation of resources. Therefore, this study proposes an ensemble method called SGLA (Stepwise Gaussian Linear Autoregressive) by combining linear regression, support vector machines, Gaussian process regression, and the autoregressive integrated moving average technique. SGLA performed better than all methods with a minimum MAPE of 1.03% of the ensemble approach by using the averaging strategy, SGLA shows a clear advantage in handling resource allocation better despite traffic fluctuations, with 91.7% traffic prediction accuracy. Overall experimental results indicate that this method performed better than single models in terms of prediction accuracy. The main contribution of this study is to propose a data analytics model for enhancing cloud computing resource management
Ethical Provision of Online Learning in South African High Schools
Get PDFDrawing from Kantianism, utilitarianism, information systems ethical models, and South Africa Department of Education policies, this study investigated how high schools can ethically provide online learning. The study was prompted by two unethical concerns highlighted in the literature: firstly, the potential discrimination to online learning against learners who do not have access to information technology resources, and secondly, the cyber risks faced by learners from prolonged exposure to Internet connected devices. To gather data for the study, interviews were conducted with 15 schoolteachers, who were conveniently sampled from five schools in Centurion, Pretoria city, South Africa. The data was thematically analysed, and the results of the study found constructs that inform ethical provision of online learning, which are: equal access to online learning, teacher competence, teacher empathy, and cyber security of learners. The findings of this study inform the policy on providing ethical online learning in South Africa and any other country
Leveraging COBIT 2019 Framework for Recommending ERP System Module Development at Cardboard Manufacturing Industry
Get PDFThe manufacturing industry is a cornerstone of Indonesia's economy, contributing significantly to the nation's GDP and exhibiting consistent growth. The sector's advancement is closely linked with technological innovations, particularly those associated with Industry 4.0, which integrates advanced technologies into production processes. Enterprise Resource Planning (ERP) systems play a pivotal role among these technologies. However, maximizing the potential of ERP systems necessitates robust IT governance, which can be assessed using the COBIT 2019 framework. This research targets the Cardboard Manufacturing Industry, which has not previously undergone an IT governance assessment, primarily due to the lack of a dedicated IT division. The study evaluates the current state of IT governance within the industry, focusing on specific objectives, including APO12 (Manage Risk), BAI09 (Manage Assets), APO14 (Manage Data), and EDM05 (Ensure Stakeholder Engagement). The methodology involves a comprehensive evaluation using the COBIT 2019 framework to identify gaps between the current and desired capability levels. The findings reveal significant discrepancies in IT governance maturity, highlighting improvement areas. Consequently, the study proposes recommendations to bridge these gaps, enhancing overall IT governance. Suggestions for developing customized ERP modules are further provided to support the industry's technological integration and efficiency
Utilizing IoT-Enhanced Multilayer Perceptron and Run Length Encoding for Classifying Plant Suitability Based on pH and Soil Humidity Parameters
Get PDFThis research proposes an IoT-based system for classifying plant suitability using pH data and soil humidity parameters. The system utilizes Run-Length Encoding (RLE) to compress sensor data, which is transmitted to a database server via the Esp8266 module. A Multilayer Perceptron (MLP) algorithm is employed to classify the data, achieving an accuracy of 0.82 with only two parameters. The classification results are displayed on a website, providing real-time recommendations for farmers. The system's performance is evaluated using a dataset from Kaggle. The Kaggle dataset contains 2200 instances for 22 different plants and the results show that the proposed system can effectively classify plant suitability based on environmental factors. This research contributes to the development of IoT-based recommendation systems for precision agriculture, and future studies can build upon this work to improve accuracy and quality
Enhancing Sales Performance through ARIMA-Based Predictive Modeling: Insights and Applications Model
Get PDFGociko Snack, a Micro, Small, and Medium Enterprise (MSME), often faces significant challenges in managing its inventory due to the unpredictable nature of market demand. Accurate sales forecasting is crucial for Gociko Snack to optimize stock levels, reduce storage costs, and avoid out-of-stock or overstock situations. Traditional methods of sales prediction are often unable to cope with the dynamic and complex market environment in which Gociko Snack operates. in solving the case This research uses the ARIMA (AutoRegressive Integrated Moving Average) model for forecasting and application modeling using the CodeIgniter framework in a structured Waterfall system development methodology. Through rigorous testing and evaluation, the Mean Absolute Percentage Error (MAPE) was set at 9.18, which shows the effectiveness of the application in predicting sales trends with a high success rate. This research contributes valuable knowledge and practical solutions to empower businesses to navigate and utilize data-driven decision making for long-term success and resilience
Leveraging NLP to Analyze Regulatory Document Interconnections: A Systematic Review
Get PDFA sustainable digital village requires an effective policy management mechanism to deliver relevant regulatory information to the community. Management information systems for regulations play a crucial role in achieving this. However, communities still face challenges in understanding and navigating the relationships between various regulations. To address this issue, this study conducts a systematic review of the components found in regulatory documents and the methods used to analyze them. The review identifies eight key components in regulatory documents: topic, structure, category, initiator, level, considerations, related regulations, and content. Natural Language Processing (NLP) techniques can be employed for data preprocessing, including tokenization, lowercasing, stop word removal, stemming, filtering, part-of-speech tagging, lemmatization, and chunking. For feature extraction, methods such as TF-IDF, bag-of-words, WordCount, N-grams, and word embeddings can be applied. To measure the interconnection between regulations, techniques like cosine similarity and K-Means clustering can be utilized. Experimental results demonstrate that combining different methods significantly influences the accuracy of identifying regulatory interconnections. The choice of methods whether simple or complex depends on the context, and confirmation through manual analysis is often required to ensure accuracy
Understanding IoT Adoption in Botswana’s SMEs: A Research Onion Approach
Get PDFThe advent of the Internet of Things (IoT) presents a transformative opportunity for Small and Medium-sized Enterprises (SMEs), unlocking their potential for enhanced operational efficiency, productivity, and data-driven decision-making. However, harnessing these benefits necessitates a rigorous and structured methodological approach. On the next hand, selecting an appropriate research methodology can be problematic, as it demands consideration of context-specific factors. This study addresses a significant gap by theoretically evaluating and proposing a suitable "research onion" methodological approach, which can be employed to explain IoT adoption in Botswana's SMEs. This structured approach provides a comprehensive analytical lens comprising the research philosophies, research strategy, approaches, choices, time horizons, techniques and procedures. By carefully applying and justifying each element within the research onion’s distinct layers, the study empowers Information Systems (IS) researchers to effectively explain their methodological decisions. Hence, findings will inform policymakers and decision-makers in Botswana, enabling them to design targeted interventions that promote widespread IoT adoption in SMEs. Future research will empirically test this framework in Botswana's SME sector using surveys, thus furthering our understanding of the IoT adoption factors in SMEs
Framework for Intelligent-Electricity Billing and Consumption Information System (IEBCIS)
Get PDFThe increasing demand for energy, coupled with the depletion of natural resources and environmental concerns, necessitates the adoption of sustainable energy practices. The use of electricity sustainability principles and smart meters data integration into energy systems plays a crucial role aiding electricity users make informed decisions about their energy consumption, driving sustainable energy practices and improving environmental stewardship. Despite efforts at electricity innovations, challenges persist in improving existing electricity frameworks, particularly in enhancing security and privacy measures, optimizing energy efficiency, improving user engagement and awareness, and addressing sustainability, scalability, interoperability, reliability and mounting environmental concerns and global warming issues. The integration of the principles of electricity sustainability and smart meter data into the development of a framework for Intelligent-Electricity Billing and Consumption Information System (IEBCIS) is crucial and an optimized approach to tackle these issues moving forward. Therefore, this paper presents IEBCIS framework that incorporated key aspects of electricity sustainability, interoperability, scalability, usability, reliability, security and privacy in the design of its framework. The Action Design Research (ADR) methodology using the pragmatism research philosophy was used to develop software prototypes to elucidate requirements for testing the framework. The result from the prototype showed significant potential to transform electricity billing and consumption practices by empowering users to make informed decisions about their energy usage, driving sustainable energy practices and improving environmental stewardship. Using the prototype, electricity consumers were able to access their smart data, query their electricity bills, simulate various electricity reduction best practices and view their total energy proposed savings in rands (South Africa currency). Ultimately, the IEBCIS framework achieved its aim of contributing to a more efficient and sustainable energy ecosystem, aligning with the global imperative for sustainable energy practices
Enhancing Credit Risk Classification Using LightGBM with Deep Feature Synthesis
Get PDFIn the digital financial services era, Peer-to-Peer (P2P) lending has emerged as a significant innovation in fintech. However, credit risk remains a major concern due to the potential for payment failures, which can cause losses for platforms and investors. This research explores the impact of Deep Feature Synthesis (DFS) on credit risk classification and evaluates the performance of the Light Gradient Boosting Machine (LightGBM) algorithm with and without DFS. The data used in this study was sourced from Kaggle, a peer-to-peer lending company based in San Francisco, California, United States. The dataset contains 74 attributes, with a total of 887,379 rows. DFS automatically generates new attributes, while LightGBM is used for selecting the most important features, aiming to optimize credit risk predictions and simplify the model's complexity. The results of credit risk classification models using DFS and without it. Findings reveal that DFS enhances the accuracy of the credit risk classification, achieving a 0.99 accuracy rate compared to 0.97 without DFS, achieving a recall and F1-score of 0.94 and 0.96 with DFS and 0.68 and 0.81 without DFS. These results suggest that DFS is an effective feature engineering technique for boosting credit risk model performance. This research contributes significantly to the P2P lending industry by demonstrating that combining DFS with LightGBM can improve credit risk management, making it a valuable approach for financial platforms
Optimizing Aspect-Based Sentiment Analysis for Kyai Langgeng Park Using PSO and SVM
Get PDFThis study aims to analyze aspect-based sentiment on Taman Kyai Langgeng tourism reviews, focusing on three main aspects: price, service, and facilities. This study combines Particle Swarm Optimization (PSO) method for feature selection and Synthetic Minority Over-sampling Technique (SMOTE) to handle data imbalance, which is a novel approach in aspect-based sentiment analysis. A total of 827 review data were retrieved from the Google Maps platform and manually labeled. This method resulted in significantly improved sentiment classification accuracy over the model without optimization. After the application of PSO and SMOTE, the model accuracy for the price aspect increased from 91.56% to 94.28%, the service aspect from 89.75% to 92.85%, and the facility aspect from 79.51% to 88.88%. The results of this study show that the combined PSO and SMOTE approach not only improves the accuracy, but also the consistency of sentiment classification on various aspects. These findings provide deep insights for tourism managers in identifying strengths and weaknesses based on visitor reviews