Development of Prediction System for Crude Palm Oil (CPO) Production with Time Series Data Mining Approach

Solichin, Achmad; Hasanah, Uswatun; Jayanta,

doi:10.1109/icimcis51567.2020.9354321

Cited by 2 publications

(1 citation statement)

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“…A study proposed LR to predict the vegetative components of oil palms [96]. Similarly, Solichin [97] suggested a prediction method for LR, SVR, and MLP combined with a time series model and intelligent nonlinear model. Notably, this approach improves the error caused by time series analysis.…”

Section: Types Of Regressionmentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

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Section: Types Of Regressionmentioning

confidence: 99%