SEGMENTASI AKAR PADA CITRA TANAH DENGAN MENGIMPLEMENTASIKAN METODE ENSEMBLE LEARNING MENGGUNAKAN TEKNIK WEIGHTED AVERAGE PADA ARSITEKTUR U-NET DAN DENSENET

KUSMARENI, ANISA AULIA and Maiyanti, Sri Indra and Desiani, Anita (2022) SEGMENTASI AKAR PADA CITRA TANAH DENGAN MENGIMPLEMENTASIKAN METODE ENSEMBLE LEARNING MENGGUNAKAN TEKNIK WEIGHTED AVERAGE PADA ARSITEKTUR U-NET DAN DENSENET. Undergraduate thesis, Sriwijaya University.

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Abstract

The purpose of this study was to obtain an accurate segmentation model in predicting roots using a soil image dataset. This research implements ensemble learning with a weighted average technique on U-Net and DenseNet architectures for root segmentation in soil images. The research stages are data description, data pre-processing, training, testing, and performance evaluation as well as making conclusions. In this study, the results obtained from the performance evaluation of a single architecture, namely accuracy, sensitivity, specificity, precision, F1-Score, and IoU obtained from the U-Net architecture respectively, are 99.69% in this study, 75.75%, 99.76%, 66.63%, 70.87%, and 54.38%. While the results obtained from DenseNet architecture are 99.52%, 71.33%, 99.72%, 66.68%, 67.29%, and 50.71%. The results of the implementation of ensemble learning on U-Net and DenseNet architectures are 99.75%, 92.82%, 99.87%, 92.68%, 92.75%, and 86.49%, respectively. Based on the results obtained, it can be concluded that the implementation of ensemble learning on U-Net and DenseNet architectures can segment the roots of the soil image very well. The implementation of ensemble learning can also improve architectural performance and overcome the problem of overfitting in single architecture segmentation.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi, akar, ensemble learning, weighted average.
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5.A142 Computer science. Information society. Information technology.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Anisa Aulia Kusmareni
Date Deposited:	26 Dec 2022 08:09
Last Modified:	26 Dec 2022 08:09
URI:	http://repository.unsri.ac.id/id/eprint/84725

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