Deep Learning Identifikasi Tanaman Obat Menggunakan Konsep Siamese Neural Network

  • Kartarina Kartarina Universitas Bumigora Mataram
  • Lalu Zazuli Azhar Mardedi Universitas Bumigora Mataram
  • Miftahul Madani Universitas Bumigora Mataram
  • Miftahul Jihad Universitas Bumigora Mataram
  • Regina Aprilia Riberu Universitas Bumigora Mataram
Keywords: Deep Learning, Siamese, Android, Medicinal Plant Family

Abstract

One of the roles of humans in community empowerment activities is in the health sector. Community empowerment in this field can be carried out through efforts to improve community health by utilizing family medicinal plants. The purpose of using family medicinal plants is so that the community can preserve these plants, reduce hospital costs, and serve as access to first aid for health if health services are difficult to reach. Based on this explanation, to increase public knowledge and understanding of the types of Toga and its benefits as a medicinal ingredient, it can be done several ways. One of them is through an in-depth learning approach. The deep learning approach with Siamese Neural Network is by comparing two patterns and producing an appropriate output based on the similarity of the two patterns. The application of Siamese Neural Network with an Android Smartphone is the right choice because it can facilitate the community in terms of usage. Based on these problems, the writer wants to build an application that can identify plants based on Android-based leaf images by applying the Siamese Neural Network approach. In this study, the results achieved were that the Siamese Neural Network could be implemented on Android-based smartphones. The application was able to identify family medicinal plants based on leaves.

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References

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Published
2021-02-10
How to Cite
Kartarina, K., Mardedi, L. Z. A., Madani, M., Jihad, M., & Riberu, R. A. (2021). Deep Learning Identifikasi Tanaman Obat Menggunakan Konsep Siamese Neural Network. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 2(4), 223-228. https://doi.org/10.35746/jtim.v2i4.114
Section
Articles