PENERAPAN METODE ENSEMBLE LEARNING PADA HASIL U-NET DAN RESNET MENGGUNAKAN TEKNIK WEIGHTED VOTING UNTUK SEGMENTASI AKAR PADA TANAH

ILAHI, MUHAMMAD WAHYU and Andriani, Yuli and Desiani, Anita (2022) PENERAPAN METODE ENSEMBLE LEARNING PADA HASIL U-NET DAN RESNET MENGGUNAKAN TEKNIK WEIGHTED VOTING UNTUK SEGMENTASI AKAR PADA TANAH. Undergraduate thesis, Sriwijaya University.

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Abstract

The feature learning capability using Convolutional Neural Network (CNN) has good performance in image segmentation. The architecture of CNN which is often used for image segmentation is U-Net and ResNet architecture. Several studies have proposed the Ensemble Learning (EL) method to combine the results of architectural performance in each image segmentation. Weighted Voting is one of the most frequently used EL methods, Weighted Voting works by choosing the largest weight from the final prediction generated by each segmentation model. The dataset used is the root image of Cichorium intybus L (Chicory) taken from the Zenodo dataset with performance evaluation measures namely accuracy, precision, sensitivity, specificity, F1-Score and Intersection over Union (IoU). The results of this study obtained an accuracy of 99.93%, precision 94.86%, sensitivity 85.19%, specificity 99.98%, F1-Score 89.77%, and IoU 81.44%. Based on these result, it shows that the proposed architecture is able to perform root segmentation in the soil well based on the category of model performance evaluation.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	U-Net, ResNet, EL, Weighted Voting, Akar Chicory
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Mr. Muhammad Wahyu Ilahi -
Date Deposited:	05 Sep 2022 05:57
Last Modified:	05 Sep 2022 05:57
URI:	http://repository.unsri.ac.id/id/eprint/78319

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