Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JOIN (Jurnal Online Informatika) Assyfa Learning Journal
Sandeep Gupta
Pt.B.D. Sharma, PGIMS, Rohtak-12400I Haryana
Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences Other

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

Found 2 Documents
Search

 1 
Media Representations and Ageing: The Influence of Bollywood on the Upper Economic Class in Urban Mumbai and Implications for Media Literacy Madhukullya, Samikshya; Gupta, Sandeep
Assyfa Learning Journal Vol. 3 No. 2 (2025): Assyfa Learning Journal
Publisher : CV. Bimbingan Belajar Assyfa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61650/alj.v3i2.694

Abstract

Mumbai's urban upper economic class faces unique pressures to maintain youthfulness, shaped by pervasive societal norms and the influential portrayals of ageing in Bollywood media. This study aims to evaluate how Bollywood's representations impact perceptions of ageing among Mumbai's affluent, and to explore the implications for media literacy and educational strategies. Employing a quantitative survey approach, data were collected from 32 upper-class residents of Mumbai using Google Forms and analyzed with SPSS to assess attitudes toward ageing, the influence of media, and gendered expectations. The findings reveal that Bollywood's youth-centric narratives significantly contribute to negative perceptions of ageing, reinforcing ageist stereotypes and intensifying the desire to appear youthful, with notable differences between male and female respondents. These results underscore the urgent need for inclusive and diverse media representations, as well as the integration of media literacy interventions in educational and community settings to foster critical engagement with age-related stereotypes. The study concludes that promoting media literacy and age-inclusive content can play a pivotal role in challenging societal biases, supporting intergenerational understanding, and informing policy and curriculum development in urban India.
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 Madhukullya, Samikshya Mun, NG Khai Nyamasvisva, Tadiwa Elisha Partap S. Ghalaut Tyagi, Nitin Veena S. Ghalaut