CD Skripsi

Sistem Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit Menggunakan Metode Faster R-Cnn Berbasis Web

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Identifying the level of maturity of Fresh Fruit Bunches is one of the steps in the palm oil harvesting process. The level of maturity of fresh fruit bunches is an important factor that determines the quality of palm oil production. In general, farmers can check FFB maturity manually by observing directly. Determining FFB maturity manually requires time and the expertise of a skilled farmer to ensure proper maturity. Faster R-CNN is a machine learning method that can be used to detect palm oil FFB objects quickly and accurately. By creating a detection system designed with UML and then using the Tensorflow 2.4 library for the Faster R-CNN Model and using flask to use model on web, the model can recognize the shape, color and edges of each TBS training data. Using 8590 TBS image datasets consisting of 6872 training data and 1718 test data trained in 50,000 steps with 64 batch sizes. The model can identify six levels of maturity of oil palm consisting of unripe, mature 1, mature 2, mature 3, late ripe, and empty bunches. The model training results have an accuracy of 72% with an IoU of 0.75 then an accuracy of 59% with an IoU of 0.50:0.95 and an accuracy of 86% with an IoU of 0.50

Keywords : Degree of maturity, Faster R-CNN, Flask, Oil palm, Object system, Tensorflow, Web

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903112272

Penerbit

Pekanbaru : Universitas Riau – FMIPA – Sistem Informasi., 2023

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903112272

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

SISTEM INFORMASI

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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