Pengenalan Tanaman Obat Liar dengan Metode Convolutional Neural Network

Authors

  • Diah Puspito Wulandari Dept. Teknik Komputer Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
  • Muhammad Emirreza Pahlevi Dept. Teknik Komputer Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
  • Hany Boedinugroho Dept. Teknik Komputer Institut Teknologi Sepuluh Nopember Surabaya, Indonesia

DOI:

https://doi.org/10.59378/jcenim.v2i1.43

Keywords:

Tanaman Obat, CNN, Image Processing, Klasifikasi

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 dalam mengidentifikasi spesies atau jenis tanaman obat liar yang masih belum diketahui oleh banyak masyarakat sekitar, jenis tanaman ini juga untuk membantu meningkatkan industri obat menyeimbangkan ekosistem serta produktivitas dan keberlanjutan pertanian. Hasil dari percobaan menggunakan fitur CNN ini menunjukkan konsistensi dan keunggulan dibandingkan lainnya. Klasifikasi citra tanaman obat liar masih dianggap sebagai tantangan dan masalah yang belum terpecahkan, 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 kemudian dilakukan proses klasifikasi data citra tanaman obat liar tersebut sehingga bisa teridentifikasi lalu dikembangkan.

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Published

2025-03-15

How to Cite

Diah Puspito Wulandari, Muhammad Emirreza Pahlevi, & Hany Boedinugroho. (2025). Pengenalan Tanaman Obat Liar dengan Metode Convolutional Neural Network. Journal of Computer Engineering, Network, and Intelligent Multimedia, 2(1), 16–25. https://doi.org/10.59378/jcenim.v2i1.43

Issue

Vol. 2 No. 1 (2024)

Section

Articles

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Copyright (c) 2026 Diah Puspito Wulandari, Muhammad Emirreza Pahlevi, Hany Boedinugroho

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