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All Journal Journal of Information Systems and Informatics Indonesian Journal of Data and Science The Indonesian Journal of Computer Science
Sekwatlakwatla, Sello Prince
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics

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Journal : Journal of Information Systems and Informatics

 1 
Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.640

Abstract

The increase in the use of digital technology led to an increase in online activities. In this regard, many organizations adopted cloud computing systems to manage this online traffic. It is plan of every cloud computing resource provider to manage their system effectively and efficiently. This paper uses bibliometric analysis technique to look at the prevalence of utilization of data analytics techniques in addressing cloud computing resource allocation challenges. In this regard, the following research databases the Association for Computing Machinery, the Institute of Electrical and Electronics Engineering, Web of Science and Scopus databases, were consulted. The research articles published before the beginning of 2017 to 2023 were considered as part of the analysis. The results showed that the prevalent data analytics techniques used to address the cloud computing resources allocation challenge are Support Vector Machine, Spatio-temporal and edge-cloud collaborative scheme. Failure to effectively and efficiently provide cloud computing management resource allocation will lead to system bottlenecks especially during peak periods. In this regard, such a failure could lead to dissatisfied clients.
Model for Enhancing Cloud Computing Resource Allocation Management Using Data Analytics Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.679

Abstract

The 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.
Bibliometric Analysis of Data Analytics Techniques in Cloud Computing Resources Allocation Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.782

Abstract

Cloud computing provides on-demand computing services over the Internet, allowing for quicker innovation, more flexible resources, and economies of scale while reducing the need for physical data centers and servers. With this benefit, most organizations are adopting this technology, and some organizations are also operating fully on cloud computing. This causes traffic to increase, and most of these organizations are struggling with resource allocation, resulting in complaints from users regarding inactive system performance, timeouts in applications, and higher bandwidth use during peak hours. In this regard, this study investigates data analytics techniques and tools for the allocation of resources in cloud computing. The study indexed journal articles from the Scopus Database and Web of Science (WOS) between 2010 and 2024. This article brings new insights into the analysis of data analytics techniques in Africa and collaborations with other developing countries. The findings present tools and approaches that may be used to allocate cloud computing resources and give recommendations.
Mapping Trends in Air Quality Research in South Africa: A Bibliometric Analysis, 1998-2024 Sekwatlakwatla, Sello Prince; Malele, Vusi; Toona, Priscilla; Tshilongo, James; Mkhohlakali, Andile; Letsoalo, Refiloe; Mabowa, Happy; Ntsasa, Napo
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1025

Abstract

The foundation of South Africa is the Constitution, which guarantees every citizen access to a safe and healthy environment. Despite a wealth of research on lower-income households, the effects of burning wood for cooking, heating, and comfort in South African homes are also affecting the air quality; even if the government is working very hard to put measures in place to improve air quality, it will be very difficult to accommodate every household in South Africa. South Africa's low-income urban settlements focus on air quality monitoring for policy formulation and strategy building and Lack of garbage removal services and systems is another characteristic of low-income communities that exacerbates ambient air pollution levels. Based on the quantity of South African publications and citations in air quality that are listed in the Scopus and Web of Science databases, the study used bibliometric analysis to look at the country's air quality and the factors that affect it. Data was collected from 1998 to 2024; the results show that air pollution, nitrogen dioxide and emissions are causing a risk to children, and also having a high impact in causing diseases like asthma, respiratory health and climate change is playing a critical role in increasing the risk. Moreover, the word cloud reflects a growing emphasis on certain air pollutants, including NO₂, PM2.5, black carbon, and SO₂. NO₂ has been linked to substantial health implications, including respiratory disorders, asthma aggravation, and cardiovascular issues.
Co-Authors Letsoalo, Refiloe Mabowa, Happy Malele, Vusi Malele, Vusumuzi Mkhohlakali, Andile Modipa, Thipe Ntsasa, Napo Ramalepe, Phetole Simon Toona, Priscilla Tshilongo, James