CD Skripsi

Penggunaan Yolo V5 Untuk Mengklasifikasi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit Berbasis Android Secara Real Time

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ABSTRACT
Identifying the level of maturity of Fresh Fruit Bunches (FFB) is one of the steps in
the oil palm harvesting process. The level of maturity of FFB is an important factor
that determines the quality of palm oil production. In general, farmers can
manually check the maturity of FFB by direct observation. Determining the
maturity of FFB manually requires time and skilled farmers to ensure proper
maturity. YOLO v5 is one of the machine learning methods that can be used to
quickly and accurately detect oil palm FFB objects. With YOLO v5, the model can
recognize the shape, color, and edges of each FFB training data. Using an 8002
dataset of FFB images consisting of 5600 training data, 1600 validation data, and
799 test data, the model can identify six levels of oil palm maturity consisting of
raw, ripe 1, ripe 2, ripe 3, overripe, and empty bunches. The trained model achieved
an accuracy of 82% with precision of 95.33%, recall of 84.5%, and an F1 score of
89.58%.
Keywords : Android, Degree of maturity, Object detection system, Oil palm,
YOLOv5.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903111120

Penerbit

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

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903111120

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

tethi

Versi lain/terkait

Lampiran Berkas

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