CD Skripsi

Penerapan Metode Elastic Net Regression Dalam Memprediksi Berat Biji Gandum

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The demand for food is increasing, one of which is for wheat crops. However, wheat yields are becoming more vulnerable to threats posed by climate change, rising temperatures, reduced rainfall, and adverse agroclimatic events. Therefore, this study aims to build a model to predict wheat seed weight based on genetic data using the Elastic Net Regression method, by ranking genetic markers according to their Minor Allele Frequency (MAF) values to understand the genetic variation that influences wheat weight. The model development is done by first selecting the minimal genetic markers and the lambda parameter. Then, the Elastic Net Regression model is run and predictions are made. Model evaluation is conducted by calculating the Root Mean Square Error (RMSE) and Pearson correlation to obtain the results. The best model evaluation recommendation, based on the smallest RMSE value of 0.43, was found in Stage 2 FI FP and Stage 3 DS FP. Based on the highest correlation value of 0.79, it was found in Stage 3 LSHS BP. In this study, stages 1, 2, and 3 achieved average correlation values of 0.67, 0.59, and 0.72, respectively. In a previous study, the average correlation values were 0.56, 0.50, and 0.42. This indicates that the results of this study are better than the previous one.

Keyword: Correlation Pearson, Elastic Net Regression, MAF, RMSE, Wheat.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2003125722

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2003125722

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

SISTEM INFORMASI

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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