CD Skripsi

KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK

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Oil palm fruit is one of the most important commodities in the plantation sector, as
the largest plantations in Indonesia are oil palm plantations. The significance of oil
palm as an economic commodity makes the optimal harvesting process crucial,
especially in determining the ripeness of the fruit to ensure the quality and quantity
of the oil produced. This sorting process still relies on human labor with visual
observation, which can lead to manual sorting limitations. Humans have limitations
in sorting large quantities of oil palm fruit, resulting in inconsistency due to fatigue,
lengthy sorting times, and subjective color assessment. These human limitations
drive research to develop a classification model for oil palm fruit ripeness using
Deep Learning technology, specifically the Convolutional Neural Network (CNN)
algorithm. This research aims to produce a model that can classify ripeness into
ripe and unripe categories. Several training sessions with variations in parameters
such as epochs and batch size were conducted to achieve the best accuracy results.
The findings of this research yielded an accuracy of 99% for training, 100% for
validation, and 100% for testing.
Keywords: Oil Palm Fruit, Classification, Convolutional Neural Network

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2007110353

Penerbit

Pekanbaru : Universitas Riau - F. Teknik - Teknik Elektro., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2007110353

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK ELEKTRO

Info Detail Spesifik

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Pernyataan Tanggungjawab

GUNTUR

Versi lain/terkait

Lampiran Berkas

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