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PENERAPAN ARSITEKTUR VISION TRANSFORMER DALAM PENENTUAN JENIS TANAH PADA CITRA DIGITAL

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Penilaian

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Soil is a thin layer of tiny particles formed by the natural process of weathering rocks on the earth's surface. Soil characteristics vary greatly from place to place. Soil classification currently plays an important role in agriculture to determine the right plants for certain soil types. So it is necessary to have a technology that is able to classify soil types in order to minimize planting errors and increase agricultural production. Therefore, this study was conducted to classify soil images in determining soil types. Broadly speaking, soil can be classified into 5 classes,namely Black Soil, Cinder Soil, Laterite Soil, Peat Soil and Yellow Soil. Deep learning is often used to perform various tasks in machine learning such as classification. One architecture that is very well classified is the Vision Transformer (ViT). The advantages of ViT is that it performs better on small datasets and has patching techniques in its architecture. In this study, soil classification was carried out to determine the type of soil using the ViT architecture. The research stages are data collection, data pre-processing, data augmentation, application of ViT, training, testing, and performance evaluation as well as analysis and interpretation.The study used soil image data sourced from Kaggle by obtaining performance evaluations, namely accuracy, sensitivity, specificity, F1-score, and Cohen's kappa respectively 97.17%, 93.20%, 97.62%, 92.99% , and 89.85%. Based on these results, it shows that the ViT Small Variant architecture is able to perform classification tasks to determine the type of soil from the image data used

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207004459	T81699	T816992022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T816992022

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2022

Collation

xi, 70 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.285 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Matematika
Pengolahan dan Analisa Data di Bidang Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

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