Nature and Enlightenment

Warman, Caroline

doi:10.1017/cco9781139565837.010

Cited by 1 publication

(1 citation statement)

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“…Otomasi di implementasikan menggunakan program perintah yang dikendalikan oleh sistem kontrol yang kemudian akan dieksekusi oleh sistem kontrol [1]. Teknologi otomasi dalam bidang pertanian telah banyak dikembangkan salah satunya adalah sistem pemeriksaan kualitas otomatis non-destruktif untuk buah kelapa sawit [2], identifikasi kematangan buah jeruk dengan teknik jaringan syaraf tiruan [3], penentuan tingkat kematangan buah tomat menggunakan analisis citra [4], dan percobaan pengembangan peralatan grading otomatis untuk buah kiwi [5].…”

Section: Pendahuluanunclassified

IMPLEMENTASI JARINGAN SYARAF TIRUAN (JST) DAN PENGOLAHAN CITRA UNTUK KlASIFIKASI KEMATANGAN TBS KELAPA SAWIT

Minarni

Salumbae

Hasbi

2018

JKFI

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The clasification of ripeness stages of oil palm fresh fruit bunches (FFBs) can be done using color parameters. These parameters are often evaluated by human vision, whose degree of accuracy is subjective which can cause doubt in judgement. Automatic clasifications offreshfruit bunches (FFBs) based on color parameters can be done using computer vision. This method is known as a nondestructive, fast and cost effective method. In this research, a MATLAB computer program has been developed which consists of RGB and HSV GUI which is used to record, display, and process FFB image data. The backpropagation artificial neural network (ANN) program is also developed which is used to classify the oil palm fruit fresh bunches (FFBs). Samples are fresh fruit bunches (FFB) of oil palm varieties of Tenera which comprise of Topaz, Marihat, and Lonsum clones. Each clone composed of three levels of ripeness represented by five fractions. The measurements were started by capturing images of oil palm, extracting RGB and HSV values, calculating weight values from the image database to make anANN program, preparing grid programs for oil palm FFBs, and comparing grading levels of oil palm FFBs using program and by harvester. This program successfully classified oil palm (FFBs) into three categories of ripeness which are unripe (F0 and F1), ripe (F1 and F1) and over ripe (F4 and F5). The RGB and HSV programs successfully classified 79 out of 216 FFBs or 36.57% and 106 out of 216 TBS or 49.07%. Respectively the HSV program is better than RGB program because the representation of HSV color space are more understood by human perception hence can be used in calibration and color comparison.

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Section: Pendahuluanunclassified