CD Skripsi

Prediksi Kandungan Kimia Tandan Buah Segar Kelapa Sawit Menggunakan Partial Least Square Dan Artificial Neural Network Pada Pencitraan Multispectral

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ABSTRACT
Oil palm fresh fruit bunches (FFB) are fruits that produce vegetable oil with the chemical content of oil content (OC) and free fatty acid (FFA). Prediction of these two parameters can use machine learning. This study aims to predict the content of oil palm FFB against the OC and FFA parameters. Multispectral imaging is the input data for machine learning to predict the two parameters in palm oil FFB. The method used in this study is Partial Least Square (PLS) as a dimensionality reduction on the unused spectrum and Artificial Neural Network (ANN), which is used to predict the content of oil palm FFB. This study was conducted 12 times for all models and produced ANN models for OC and FFA with an MSE score of 16.41% for OC and 3.75% for FFA, and obtained a MAPE score of 26.02% for OC and 24.54% for FFA. The classification for the MAPE score in OC is acceptable, and the MAPE score in FFA is acceptable.
Keywords: Oil Palm, Multispectral Imaging, Artificial Neural Network, Partial Least Square, Dimensionality Reduction

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903111219

Penerbit

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

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903111219

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

Deti

Versi lain/terkait

Lampiran Berkas

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