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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 : Indonesian Journal of Data and Science

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Comparison of Three Resouces Allocation Technique in Cloud Computing Sekwatlakwatla, Sello Prince
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.118

Abstract

The shift to the cloud enables organizations of all sizes to swiftly, efficiently, and innovatively move their operations. The adoption of cloud computing has significantly transformed most organizations' work, communication, and collaboration methods, making it a crucial necessity for maintaining competitiveness in the digital age. Organizations are implementing cloud bursting to handle IT demand peaks by utilizing private cloud capacity and public cloud capacity, freeing up local resources for critical applications, and reverting data back to the private cloud. Organizations face challenges in allocating resources in cloud computing to automatically switch from private cloud to public cloud, leading to system issues, user frustration, operational failure, increased stress, and revenue loss. To address these concerns. This paper investigates traffic predictions by comparing three prediction tools, such as support vector machines, spatio-temporal, and edge-cloud collaborative schemes, and proposing conceptual solutions. Efficient cloud computing traffic management can prevent system bottlenecks, especially during peak periods, potentially leading to dissatisfied clients.
Bibliometric Analysis of Mixed Text Using Transformer-Based Architecture in Africa Sekwatlakwatla, Sello Prince; Malele, Vusumuzi; Ramalepe, Phetole Simon; Modipa, Thipe
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.131

Abstract

Deep learning techniques based on neural networks have been developed for text creation, a critical sub-task of natural language generation that aims to create human-readable content. Natural language processing (NLP) tasks are utilized to recognize speech in code-mixed comments on social media platforms like Facebook and Twitter, which enable users to interact and exchange ideas, views, status updates, pictures, and videos with people all over the world. Although NLP is widely investigated in the world and Africa is home to approximately 3,000 languages, many of which are derived from significant language families, in this regard, there are challenges that Africa faces in Natural Language Processing (NLP), especially mixed text using transformer-based architecture. The purpose of this study is to investigate the prevalence of mixed text using transformer-based architecture in Africa. Bibliometric analysis was used to assess natural language and mixed text in Africa, utilizing transformer-based architecture. show that sentiment analysis is the holistic tool that is used for mixed text using transformers, where social media, deep learning, codes, computational linguistics, and social networking are critical tools in generating human-like quality text. Therefore, this study proposes artificial intelligence, artificial neutral networks, and neural networks, as well as a prediction to estimate the technique or fluctuation as the method for mixed text using transformer-based architecture in Africa. This research sets the path for future studies that use mixed text using transformer-based architecture in Africa
A Literature Review to Investigate Data Analytics Tools for The Allocation of Resources in Cloud Computing Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.136

Abstract

To ensure efficient operations and cost-effectiveness, resource management in cloud computing entails managing cloud resources to satisfy application needs, financial restrictions, and security. In this regard, utilizing data analytics tools for the allocation of resources in cloud computing to efficiently predict, track, allocate, and monitor resources enables businesses to make informed decisions based on real-time data, which plays a crucial role in resource allocation. Organizations adopting cloud computing services face increased network traffic, limiting traffic routing flexibility and causing excess traffic to reach unprepared physical nodes due to an inability to adjust to real-time traffic changes. This paper uses a systematic literature review to investigate the data analytics techniques used for resource allocation in cloud computing. It uses data from 2019 to 2024, sourced from different research databases. The results show that the majority of data analytics tools, including ARIMA and SVM, are employed for resource allocation in cloud computing. This study offers guidance to organizations regarding data analytics tools for the allocation of resources in cloud computing, and the recommendations can be utilized for the enhancement of the results in cloud computing, as well as to scholars by suggesting techniques to further investigate resource allocation to address the current gaps in cloud computing
Co-Authors Letsoalo, Refiloe Mabowa, Happy Malele, Vusi Malele, Vusumuzi Mkhohlakali, Andile Modipa, Thipe Ntsasa, Napo Ramalepe, Phetole Simon Toona, Priscilla Tshilongo, James