1 JUDUL SKRIPSI YANG DISETUJUI PROGRAM STUDI TEKNIK INFORMATIKA TA GENAP NO NIM NAMA JUDUL ACC CATATAN Yurdawati Pembuatan Sistem Pakar Untuk Menditek. The results showed that the CNN method obtained an accuracy rate of up to 89.2% for detecting plants and it was not accurate. JUDUL SKRIPSI YANG DISETUJUI PROGRAM STUDI TEKNIK INFORMATIKA TA GENAP. In this study, plant recognition was carried out and did not use the Tensorflow framework with a dataset of 1312 images. Deep learning used for object recognition and classification is Convolutional Neural Network because it was widely used in the past and produced significant results in image research. In Machine Learning (ML) there are problems, namely object detection and image classification. With parameters so that it can be applied in the field of machine learning (Machine learning). Thus, it can be useful to utilize leaf patterns, leaf blades, leaf bones and colors for parameter recognition. One of the differences that plants have is in the leaf pattern, leaf blade, leaf bone and leaf color. So it is necessary to be careful to identify plants that have the same characteristics.

Plants have thousands of millions of species, where between plants have similarities or are vague. The number of plants in the wild makes it difficult for climbers to find plants that can provide nutrients for the body.

Undergraduate thesis, Politeknik Negeri Jember.Īvailable under License Creative Commons Attribution Share Alike. Penerapan Deep Learning Menggunakan Convolutional Neural Network (Cnn) Untuk Klasifikasi Tumbuhan Beracun Menggunakan Model Mobilenet Skripsi.

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