Machine Learning-Based Predictive Modeling of Outcomes in EMS-Induced Plant Mutagenesis: An Innovative Approach for Genetic Improvement

Maulana, Faruk (2024) Machine Learning-Based Predictive Modeling of Outcomes in EMS-Induced Plant Mutagenesis: An Innovative Approach for Genetic Improvement. Undergraduate thesis, Politeknik Negeri Jember.

	Text (Abstract) Abstract.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (80kB)
	Text (Bab 1 Pendahuluan) Bab 1 Pendahuluan.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (130kB)
	Text (Daftar Pustaka) Daftar Pustaka.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (115kB)
	Text (Laporan Lengkap) Laporan Lengkap.pdf - Submitted Version Restricted to Registered users only Download (2MB) \| Request a copy

Abstract

Chemically mutating DNA by alkylating guanine bases, EMS is a mutagen that causes mutations that cause notable changes in the genotype and phenotype of plants that are treated. In order to predict the results of mutagenesis based on phenotypic and genotypic data gathered, this research focuses on building a predictive model using logistic regression. In training, the logistic regression model's accuracy was 65%, and in testing, it was 56%. These results show how plant breeding operations can be made more productive and efficient by combining conventional mutagenesis methods with cutting-edge machine learning techniques.

Item Type:

Thesis (Undergraduate)

Contributors: