@inproceedings{7e7dd7d2d55b415881b88fd62835feab,
title = "Improving the Classification Result of Rice Varieties Using Gradient Boosting Methods",
abstract = "An accurate identification of rice grain is crucial for classifying rice varieties. This study classifies five distinct rice types that share morphological characteristics using four different machine learning methods. A total of seventy-five thousand records, consisting of fifteen thousand for each variety of rice grains, are collected from previous research. Machine learning methods that are used in this study are the Gradient Boosting method and its variances. The experimental results show that Light Gradient Boosting Machine was the algorithm with the most significant classification success rate compared to other methods, with an accuracy of 98,14%.",
keywords = "Gradient Boosting method, classification, crops, machine learning, rice",
author = "Amalia Utamima and Alexander Alangghya and Hakim, {Tarisa A.} and Aryageraldi Pajung",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023 ; Conference date: 04-05-2023 Through 06-05-2023",
year = "2023",
doi = "10.1109/SIST58284.2023.10223511",
language = "English",
series = "SIST 2023 - 2023 IEEE International Conference on Smart Information Systems and Technologies, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "164--167",
booktitle = "SIST 2023 - 2023 IEEE International Conference on Smart Information Systems and Technologies, Proceedings",
address = "United States",
}