Prediction of Rubber Crop Production in Malaysia Using Machine Learning

M. N. Shah Zainudin, P. H. Jing, N. A. Sulaiman, M. R. Kamarudin, N. Z. Nizam and Sufry Muhammad
Additional contact information
M. N. Shah Zainudin: Faculty of Artificial Intelligence and Cyber Security (FAIX), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
P. H. Jing: Faculty of Electronics and Computer Technology and Engineering (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
N. A. Sulaiman: Faculty of Electronics and Computer Technology and Engineering (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
M. R. Kamarudin: Faculty of Electronics and Computer Technology and Engineering (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
N. Z. Nizam: Faculty of Technology Management and Technopreneurship (FPTT), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
Sufry Muhammad: Faculty of Science and Information Technology (FSIT), Universiti Putra Malaysia (UPM), 43400 UPM Serdang, Selangor, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 9, 3680-3688

Abstract: Agriculture is a cornerstone of economic growth in many countries, especially in developing nations where it provides a significant source of employment and raw materials for industries like sugar, palm oil, and rubber. In Malaysia, agriculture is particularly vital, with rubber products being a key export. To mitigate the economic impact of potential crop shortages, a reliable prediction system for rubber production is crucial. Traditional, human-based methods for predicting crop yields are often inefficient, time-consuming, and prone to errors. For example, the crop-cut method can lead to inaccurate measurements and overestimation of production. To address these limitations, a new study introduces a machine learning-based prediction system. This system, which was developed to forecast rubber crop production in the Malaysian states of Melaka, Perak, Pahang, and Johor, utilizes several machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, and Neural Network. The performance of these models was evaluated using Mean Square Error (MSE) and Mean Absolute Error (MAE). The findings of the study demonstrate that Linear Regression was the most effective algorithm, consistently providing the most accurate predictions with the lowest MAE values. This new system offers a more efficient and precise alternative to traditional methods, enabling better financial planning and decision-making for the agricultural sector.

Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... ssue-9/3680-3688.pdf (application/pdf)
https://rsisinternational.org/journals/ijriss/arti ... ng-machine-learning/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:9:y:2025:issue-9:p:3680-3688

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().