CD Skripsi

Klasifikasi Jenis Tanaman Herbal Menggunakan Convolutional Neural Network (Cnn)

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Beauty is an important aspect of many people's lives, driving increased interest in natural care products, especially those based on herbal plants. Herbal plants have long been known for their active compounds that are beneficial for skin and hair care. However, many people find it difficult to recognize herbal plants directly, which poses a challenge in selecting safe and effective beauty products. This study aims to develop a herbal plant classification model using the Convolutional Neural Network (CNN) architecture with the DenseNet121 model, which is useful for identifying types of herbal plants. The Dataset used consists of 2,066 images divided into three sets: Training, validation, and Testing, and categorized into four classes of herbal plants. Using a transfer learning approach with a pre-trained Imagenet model, this research achieved excellent evaluation results, with average Accuracy, Recall, Precision, and F1 Score ranging around 90% and approaching the optimal value. These results demonstrate that the CNN model with DenseNet121 is capable of delivering very high performance and effectively classifying types of herbal plants.



Keywords: Convolutional Neural Network, CNN Model, Herbal Plants, DenseNet121

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2003111131

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2003111131

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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