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PREDIKSI JUMLAH TANDAN BUAH SEGAR (TBS) KELAPA SAWIT PADA PT PERKEBUNAN MITRA OGAN

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Penilaian

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One of the companies engaged in managing palm oil into crude palm oil is PT Perkebunan Mitra Ogan. Fresh fruit bunches play an important role in making crude palm oil, if the crude palm oil production does not meet the target, the company will suffer losses, therefore to meet the demand for the crude palm oil market, the company must carry out FFB forecasting which will be used as a reference so that the production output in crude palm oil remains stable or increases. Forecasting results can later be used as information to increase FFB production yields. This study used FFB production data for 2010-2019, using the artificial neural network method – backpropagation. From the results of the trial and error, training and testing have been carried out as many as 96 architectural parameters with a combination of A and combination B. The most optimal JST architecture is 12-3-1, the logsig – purelin activation function and the traingdm training function.The mse value obtained on the training architecture is 0.041087, the mse value on the test architecture is 0.034534 and has a test accuracy rate of 75%. It is known that the production of FFB in the following year or 2022 in January – March is (22595.23436.23854) tons. Keywords: Prediction, Fresh Fruit Bunches, Artificial Neural Networks, Backpropagation.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003079	T77094	T770942022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T770942022

Publisher

Inderalaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xvi, 78 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Prodi Sistem Informasi

Specific Detail Info

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Statement of Responsibility

SEPTA

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