Article Per Year (5 Year)
p-Index From 2020 - 2025
0.92
P-Index
This Author published in this journals
All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Journal of Current Research in Blockchain International Journal Research on Metaverse
Khosa, Joe
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
How Effective are Different Machine Learning Algorithms in Predicting Legal Outcomes in South Africa? Khosa, Joe; Mashao, Daniel; Olanipekun, Ayorinde; Harley, Charis
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.215

Abstract

This study examines the effectiveness of different machine learning algorithms in predicting legal outcomes in South Africa's Judiciary system. Considering the advancement of artificial intelligence in the legal sector, this research aims to assess the effectiveness of various machine learning algorithms within the legal domain. Text classification is done using machine learning algorithms, including Logistic Regression, Random Forest, and K-Nearest Neighbours, with datasets obtained from a state legal firm in South Africa. The datasets undergo diligent data cleansing and pre-processing methods, encompassing tokenization and lemmatization techniques. This study evaluates these models' applications through accuracy metrics. The findings demonstrate that the Logistic Regression model attained an accuracy rate of 75.05%, whereas the Random Forest algorithm achieved an accuracy rate of 75.08%. On the other hand, the K-Nearest Neighbours algorithm exhibited no optimal performance, as evidenced by its accuracy rate of 62.76%. This study provides valuable insights for legal professionals by addressing a specific research question about the successful application of machine learning in South Africa's legal sector. The results indicate the possibility of using machine learning to predict the outcomes of criminal legal cases. Additionally, this study highlights the significance of responsibly and ethically implementing machine learning within the legal field. The results of this study enhance our comprehension of the prediction of legal outcomes, establishing a foundation for future investigations in this dynamic area of study. A limitation of this study is that the data was obtained from a single law firm in South Africa.
Sentimental Analysis of Legal Aid Services: A Machine Learning Approach Khosa, Joe; Mashao, Daniel; Olanipekun, Ayorinde
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.521

Abstract

Legal Aid services in South Africa, administered by Legal Aid South Africa (SA), aim to provide essential legal representation to vulnerable individuals lacking financial resources. Despite its significant role, there is a pervasive perception among the public that the quality of these state-funded services is substandard, often leading to negative attitudes towards the organization. This research employs sentiment analysis to evaluate client perceptions of Legal Aid SA's services, using a dataset of 5,246 entries from Twitter and the Internal client feedback system between 2019 and 2024. The study utilizes various machine learning algorithms, including Naive Bayes, Stochastic Gradient Descent (SGD), Random Forest, Support Vector Classification (SVC), Logistic Regression, and Extreme Gradient Boosting (XGBoost), to analyze sentiment polarity and classify feedback into positive, neutral, and negative sentiments. The accuracy, precision, recall, and F1 scores assessed model performance. The SVC and XGBoost models demonstrated superior performance, achieving testing accuracies of 90.10% and 90.00%, respectively. In contrast, Naive Bayes and Logistic Regression lagged, with test accuracies of 82.00% and 85.00%, respectively. The findings reveal that most responses are either neutral or positive, suggesting a predominantly favourable impression of Legal Aid services. This research not only aims to enhance Legal Aid SA's service offerings but may also provide valuable insights for similar organizations globally.
Co-Authors Harley, Charis Mashao, Daniel Olanipekun, Ayorinde SUBEKTI, FAJAR