KOMPUTEK
Vol 6, No 1 (2022): April

IMPLEMENTASI TENSOR FLOW LITE PADA TEACHABLE UNTUK IDENTIFIKASI TANAMAN AGLONEMA BERBASIS ANDROID

Muhammad Bagus Baihaqi (Universitas Muhammadiyah Ponorogo)
Yovi Litanianda (Universitas Muhammadiyah Ponorogo)
Andy Triyanto (Universitas Muhammadiyah Ponorogo)

Article Info

Publish Date
16 Apr 2022

Abstract

Aglonema or sri fortune has various types with various shapes, patterns and colors. Various types and more and more due to the many crossing processes carried out by owners and lovers of aglonema plants. For ordinary people who do not have insight into aglonema, it will be difficult to distinguish aglonema plants because the shapes, patterns and colors have similarities. It takes a Teachable Machine system with a complex but more sophisticated method that is able to recognize plants with a higher level of accuracy. The machine learning process is carried out on a computer to identify image data into classification results in the form of predictions. Tensorflow lite is a machine learning library specially designed for object recognition. Therefore, researchers are encouraged to create an Android-based mobile application that is able to recognize aglonema plants quickly, easily and accurately. 

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Journal Info
KOMPUTEK
Website

Abbrev

komputek

Publisher

Universitas Muhammadiyah Ponorogo

Subject

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

Description

Jurnal Mahasiswa Teknik (Mesin, Elektro dan Informatika) Universitas Muhammadiyah Ponorogo ISSN : 2614-0985 (media cetak) ISSN : 2614-0977 (media ...