Pahlevi, Muhammad Emirreza (2021) Pengenalan Tanaman Obat Liar dengan Metode Convolutional Neural Network. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Tanaman adalah dasar dari semua sumber kehidupan di bumi yang memberikan kita semua makan dan juga oksigen. Pemahaman yang baik tentang tanaman obat sangat penting untuk membantu da- lam mengidentifikasi spesies atau jenis tanaman obat liar yang ma- sih belum diketahui oleh banyak masyarakat sekitar, juga untuk membantu meningkatkan industri obat menyeimbangkan ekosistem serta produktivitas dan keberlanjutan pertanian. Hasil dari per- cobaan menggunakan fitur CNN ini menunjukkan konsistensi dan keunggulan dibandingkan lainnya. Identifikasi tanaman obat liar masih dianggap sebagai tantangan dan masalah yang belum ter- pecahkan, hal ini dikarenakan tumbuhan di alam memiliki macam bentuk dan representasi warna jadi berdasarkan citra model bentuk tanaman berdasarkan kelengkungan dengan memanfaatkan ukuran integral agar bisa mengetahui fungsi kelengkungan tersebut kemudi- an dilakukan proses klasifikasi data citra tanaman obat liar tersebut sehingga bisa teridentifikasi lalu dikembangkan.
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Plants are the basis of all life on earth that provide us all with food
and oxygen. Good knowledge about medicinal plants is very impor-
tant to help identify species or types of fake drugs that are still unk-
nown to many local people, as well as to help improve the medicinal
industry balance the ecosystem and productivity The results of the
experiment using the CNN feature show consistency and advantages
over others. Identification of liar medicinal plants is still considered
a challenge and an unsolved problem, this is because plants in nature
have various shapes and color representations so based on the ima-
ge of the plant shape model based on curvature by utilizing integral
sizes in order to know the function of the curvature then the image
data classification process is carried out. The liar medicinal plant
can then be developed
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Tanaman obat liar, CNN, Klasifikasi , Identifikasi , Image Processing, Herbal plants, Classification, Identify, ResNet50. |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis |
| Depositing User: | Muhammad Emirreza Pahlevi |
| Date Deposited: | 03 Sep 2021 10:03 |
| Last Modified: | 03 Sep 2021 10:03 |
| URI: | http://repository.its.ac.id/id/eprint/91360 |
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