CD Skripsi

Implementasi Deep Learning Untuk Identifikasi Tanaman Rimpang Menggunakan Metode Convolutional Neural Network

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ABSTRACT
Rhizome plants are spices widely used by Indonesian people as cooking ingredients or traditional medicine. These plants have similar appearances, making them difficult to distinguish for some people. Errors in identifying rhizome plants can lead to poisoning, allergies, or unwanted side effects. To simplify identifying these plants, a system is needed to detect and differentiate types of rhizome plants, which can be achieved using Convolutional Neural Networks (CNN) with the YOLO algorithm. CNN is a Machine Learning technique capable of identifying objects based on their visual features, enabling efficient differentiation of rhizome plants. The image dataset used is divided into six classes, with a total of 700 images. Model testing produced results with a precision of 98%, recall of 99%, and mAP50-95 of 96%. Future research is expected to increase dataset variety to avoid overfitting.
Keywords: Machine Learning, Rhizome Plants, YOLO, Convolutional Neural Network

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状況

Detail Information

シリースタイトル

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請求記号

2007113813

公開者

Pekanbaru : Universitas Riau – F.TEKNIK – INFORMATIKA., 2025

校合

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言語

Indonesia

ISBN/ISSN

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分類

2007113813

Content Type

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Media Type

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Carrier Type

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刊行

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キーワード

TEKNIK INFORMATIKA

形態

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Statement of Responsibility

daus

Other version/related

添付ファイル

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