CD Skripsi

Klasifikasi Tingkat Kematangan Buah Kelapa Sawit Menggunakan Hidung Elektronik Dengan Metodejaringan Saraf Tiruan

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ABSTRACT
Classification of the ripeness levels using an electronic nose system and machine
learning is a very important part of the automatic fruit sorting process. In this
study, the classification of the ripeness levels of oil palm fresh fruit bunches
(FFB) was carried out using an electronic nose system and artificial neural
network (ANN). The electronic nose system used consists of eight MQ series gas
sensors, arduino Atmega 2560 microcontroller and a power supply. The data
retrieval program and artificial neural network program were designed and created
using Python software. The samples of oil palm FFB used tenera varieties with 3
of the ripeness levels, namely unripe (F0-F1), ripe (F2-F3) and over ripe (F4-F5)
which were traditionally determined based on color and fraction. 9 bunches were
used for the testing process to produce 81 data and 3 FFB for the training process
which resulted in 27 data. The data by electronic nose system in the form of
sensor output voltage versus time is converted into trapezoid area for each sensor
and each sample. This trapezoid area data were used as a database for making
ANN.
Keywords : Electronic Nose, Artificial Neural Networks, oil palm FFB, Python

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

03 01. 121 (0023)

Penerbit

Pekanbaru : Universitas Riau – FMIPA – Fisika., 2021

Deskripsi Fisik

ix, 79 hlm.; ill.: 29 cm

Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

03 01. 121 (0023)

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

FISIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

ELFITRA

Versi lain/terkait

Lampiran Berkas

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