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) Journal of Business Studies and Management Review
Hamid, Abu Bakar Abdul
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Social Sciences

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

Found 2 Documents
Search

 1 
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.
EXPLORING THE COMBINED EFFECT OF PRODUCT AVAILABILITY, PRICING, AND PROMOTION ON FEMALE CONSUMER RETENTION Shikha, Farzana Arifin; Hoque, Ashikul; Hamid, Abu Bakar Abdul
Journal of Business Studies and Management Review Vol. 9 No. 1 (2025): JBSMR, Vol 9 No.1 December 2025
Publisher : Management Department, Faculty of Economics and Business, Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jbsmr.v9i1.47955

Abstract

This article investigates how promotional activities, strategic pricing, and product availability influence the retention of female consumers, a relatively underexplored domain in customer loyalty research. Recognizing that women represent a substantial share of global purchasing power, the study underscores the importance of understanding the factors that shape long-term female buyer engagement. A systematic review of existing literature, supported by industry-based analyses such as reports and case studies, was conducted to evaluate how these strategies affect consumer behavior. The findings reveal that transparent pricing, consistent product availability, and personalized promotional initiatives are key determinants of customer satisfaction and retention. The study is distinctive in highlighting the scarcity of women-cantered retention strategies within existing scholarship. It emphasizes the necessity for retailers to deliver seamless and tailored shopping experiences, as failure to do so may result in financial and reputational losses. From a practical standpoint, the research suggests that organizations should adopt data-driven insights and flexible promotional strategies to strengthen the loyalty of female consumers. Keywords: Female Consumers, Customer Retention, Product Availability, Psychological Pricing, Promotions, Loyalty Strategies
Co-Authors Ather, Danish Hoque, Ashikul Jain, Vishal Mun, NG Khai Nyamasvisva, Tadiwa Elisha Sandeep Gupta Shikha, Farzana Arifin Tyagi, Nitin