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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 : The Indonesian Journal of Computer Science

 1 
Evaluations of Large Language Models a Bibliometric analysis sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3767

Abstract

The development of artificial intelligence (AI) and the increased curiosity about how large language models (LLMs) may maximize an organization's opportunities and the ethical implications of LLMs, such as the ability to generate human-like text, give rise to concerns regarding disinformation and fake news. As a result, it is crucial to develop evaluation benchmarks that take into account the social and ethical implications involved. The great challenges of LLMs lack awareness of their own limitations, yet they persist in producing responses to the best of their capabilities. This often results in seemingly plausible but ultimately incorrect answers, posing challenges to the implementation of reliable generative AI in industry. This paper aims to delve into the evaluation metrics of machine-learning models' performance, specifically focusing on LLM. Therefore, bibliometric analysis utilized to explore and analyze various techniques and methods used in evaluating large language models. Additionally, it sheds light on the specific areas of focus when evaluating these models. The results show that natural language processing systems, classification of information, and computational linguistics are some of the techniques used to evaluate large language models. This work paves the way for future investigations employing extensive language models.
Data science for energy applications: A Bibliometric Analysis Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3781

Abstract

Global digitalization is altering the energy sector, demanding the adoption of data science applications to improve efficiency and innovation, despite the industry's existing data analytics. Data science is revolutionizing the energy and utilities industries, enabling efficient, sustainable, and innovative decision-making through data analysis and smart grid optimization. In the energy industry, organizations are turning to data science to reduce waste, optimize energy usage, and provide alternative energy sources. With the different parts of Africa facing energy crises, different applications are needed to provide a solution. Data science has the potential to provide good information and knowledge that could be used to contribute to energy solutions. To address these concerns, data science models enable utilities to accurately forecast energy demand, enabling efficient generation, distribution, waste reduction, and informed investment decisions by leveraging historical consumption data, weather patterns, and economic indicators. This article aims to explore data science for energy applications. The findings show tools and techniques that can be utilized to provide energy efficiency and energy sustainability through data science applications.
Performance of Cloud Computing Resources Allocations SGLA Model Compared to ARIMA Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4205

Abstract

Solutions for cloud computing are growing in popularity as a means for businesses to streamline operations, save expenses and increase productivity. The benefit of cloud services is that they let customers access on-demand apps and services from a shared pool of programmable computer resources and store data offsite. Cloud computing resource allocation requires sophisticated tools and methodologies for optimal utilization. These problems include load balancing, efficient resource management and compliance with legal and regulatory requirements. The majority of businesses are switching to cloud services and advising their customers to use internet services. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the performance ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA), and the individual method which is autoregressive integrated moving average (ARIMA). The Matlab tool is used for simulation and evaluation of the results. The results show SGLA prediction accuracy increased to an average of 98.9%, and ARIMA prediction showed an accuracy of 75.5%. In this regard, the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing.
A Review to Identify Adequate Data Analytic Frameworks for Managing Cloud Computing Resource Allocation Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4257

Abstract

Organizations need to understand the importance of resource allocation and traffic forecasting for cloud computing more than ever because of the increasing demand for online services, data storage and remote work, which makes it challenging to estimate traffic and distribute resources. In cloud computing, there is still opportunity for increasing the model's forecast accuracy. The more accurate the traffic flow, the better the resource allocation. Therefore, this study investigate the adequate data analytic frameworks for managing cloud computing resource allocation in the organisation, According to the findings, six technique were used just for prediction, while one was utilized solely for resource allocation. In this sense, cloud computing resource allocation and prediction may be achieved by combining several techniques.this paper contributes the review to identify adequate data analytic frameworks for managing cloud computing resource allocation.
Co-Authors Letsoalo, Refiloe Mabowa, Happy Malele, Vusi Malele, Vusumuzi Mkhohlakali, Andile Modipa, Thipe Ntsasa, Napo Ramalepe, Phetole Simon Toona, Priscilla Tshilongo, James