Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOIN (Jurnal Online Informatika) Journal of Computer Science and Information Technology
Nyamasvisva, Tadiwa Elisha
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A REVIEW OF THE INFLUENCE OF ARTIFICIAL INTELLIGENCE IN ACADEMIC WRITING Subedi, Rameshor; Nyamasvisva, Tadiwa Elisha
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2024): Desember
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v2i1.1134

Abstract

have revolutionized many aspects of the writing process, offering both opportunities and challenges for researchers, academicians, students and educators. AI in academic writing presents substantial possibilities by acting as an intelligent writing assistant, language translator, supporting automated summarization, enhancing writing styles and grammar, and enabling data analysis and visualization. To ascertain the influence of AI in academic writing, a comprehensive review of literature related to artificial intelligence, machine learning, and academic writing were conducted. This study aims to address three distinct challenges, including the widespread usage of AI-enabled tools for academic writing, problems with authorship, copyright, and plagiarism in AI-generated content, and how these problems might be fixed. The primary aim of this article is to recognize and highlight the implication of AI in the context of academic writing. To improve their writing abilities, particularly in academic writing, learners, academic researchers, authors, and educators would benefit more from this study. However, the authorship, copyright and plagiarism should be taken into consideration. In the machine generated text, the authorship and copyright  go to the user who gives the input in his. When AI-generated text is combined with original content and thoroughly reviewed using plagiarism detection software, it helps reduce the risk of plagiarism. Keywords: Artificial Intelligence, Machine Learning, Academic Writing, Natural Language Processing  
Enhanced Agricultural Decision-Making: Machine Learning Approaches for Crop Prediction and Analysis in India Gupta, Sandeep; Hamid, Abu Bakar Abdul; Nyamasvisva, Tadiwa Elisha; Tyagi, Nitin; Jain, Vishal; Mun, Ng Khai; Ather, Danish
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1610

Abstract

This paper addresses the critical aspects of agriculture in the Indian economy and the challenges faced by this sector, including soil quality decline, unpredictable weather, and the need for efficient decision-making. It presents machine learning as a transformative approach for improved agricultural decision-making, enabling enhanced crop prediction and productivity. Machine learning (ML) algorithms are shown to effectively analyze vast datasets to generate predictive models that aid in crop selection optimization, disease outbreak prediction, and market fluctuation anticipation, thus leading to increased yields and profitability. Focusing on crop prediction, the paper discusses models leveraging historical data and advanced algorithms to forecast crop yields. Additionally, the application of machine learning in precision farming, such as optimizing fertilizer application, is explored. The paper uses a mixed-method approach on a dataset encompassing various crops and environmental parameters. In this paper the various techniques such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) algorithms have been employed to demonstrate the utility of ML in the agricultural fields. The KNN at the value of K=4 and SVM with polynomial kernel resulted the accuracy of 0.982 and 0.989 respectively. Whereas DT and RT gave the results in terms of accuracy of 0.987 and 0.970 respectively. Overall, it can be said that all these techniques used in the present work showed the better accuracy for agricultural sustainability.
Co-Authors Ather, Danish Hamid, Abu Bakar Abdul Jain, Vishal Mun, NG Khai Sandeep Gupta Subedi, Rameshor Tyagi, Nitin