Identifikasi Non-Destructive Kandungan Total Karoten Pada Buah Kelapa Sawit (Elaeis Guineensis Jacq) dengan Pemodelan Artificial Neural Network (ANN).

Arifin, Ahmad Diyanal (2016) Identifikasi Non-Destructive Kandungan Total Karoten Pada Buah Kelapa Sawit (Elaeis Guineensis Jacq) dengan Pemodelan Artificial Neural Network (ANN). Sarjana thesis, Universitas Brawijaya.

Abstract

Kandungan total karotenoid yang sebagian besar terdiri dari total karoten pada Crude Palm Oil (CPO) merupakan salah satu syarat mutu yang harus dipenuhi oleh perusahaan kelapa sawit dalam mengekspor produknya ke luar negeri. Selama ini prediksi kandungan total karoten pada buah sawit yang diterima dari para petani dilakukan secara manual oleh mandor sortasi berdasarkan pada pengalaman dan perkiraan yang bersifat subjektif sehingga berdampak pada rendahnya harga buah sawit ditingkat petani sawit. Di sisi lain pengujian secara kimiawi dinilai mahal dan membutuhkan waktu yang relatif lama. Pemanfaatan analisis Citra Digital (Image Analysis) dan Pemodelan Jaringan Syaraf Tiruan/ Artificial Neural Network (ANN) dapat menjadi solusi alternatif dalam mengidentifikasi kandungan total karoten pada buah sawit secara objektif, cepat dan bersifat non-destructrive terhadap buah sawit uji yang digunakan. Penelitian ini bertujuan untuk mengetahui hubungan antara indeks warna RGB, HSV dan L*a*b terhadap kandungan total karoten pada buah sawit. Dari 9 parameter warna kemudian dipilih 4 parameter dengan trend hubungan terbaik untuk dibuat pemodelan prediksi total karoten menggunakan ANN. Dari hasil penelitian diketahui bahwa terdapat pengaruh proses penundaan waktu (inap) terhadap kandungan total karoten pada buah sawit, dimana semakin lama penundaan waktu maka kandungan total karoten juga semakin tinggi dengan nilai R2 = 0,9167. Pola hubungan antara indeks warna RGB, HSV dan L*a*b terhadap kandungan total karoten menunjukkan trend hubungan terbaik adalah naik secara eksponensial, dimana 4 parameter yang memiliki trend hubungan terbaik adalah Saturation, value, a* dan b*. Pada tahap pemodelan dengan ANN didapatkan nilai R-square training dan validasi pada taraf 15% data vaidasi berturut – turut adalah 0,8693 dan 0,8329. Sedangkan pada taraf 20% data validasi didapatkan nilai R-Square training sebesar 0,8944 dan nilai R-Square validasi sebesar 0,8768.

English Abstract

The content of total carotenoids consisting mostly of β- carotene on Crude Palm Oil (CPO) is one of the quality requirements that must be met by palm oil companies in order to export their products abroad. During this prediction total carotene content in palm fruit received from farmers is done manually by the foreman sorting based on experience and estimates. This would be detrimental to the farmers because of the subjective nature of the ratings impact on the low price of oil palm fruit farmer level. On the other hand chemical testing is expensive and requires a relatively long time. The utilization of Digital image analysis (Image Analysis) can be an alternative solution in identifying the content of total carotenoids in the palm fruits in an objective, rapid and non-destructrive against palm fruit samples used. This digital image analysis techniques coupled with Neural Network Modeling / Artificial Neural Network (ANN) to provide the level of accuracy and precision. This study aims to determine the relationship between the index color RGB, HSV and L * a * b to the total content of carotenoids in the palm fruits. From 9 index parameters then selected four parameters with the trend for the next best relationship modeling predictions made total carotenoids using ANN. From this research, the result of the influence of the delay time to the total content of carotenoids in the palm fruits, where the longer the delay, the total content of carotene also higher with R2 = 0.9167. The pattern of the relationship between the index of color RGB, HSV and L * a * b to the total content of carotenoids obtained the best relationship trend is rising exponentially, with four parameters that have the best relationship trend is Saturation, value, a * and b *. At this stage of modeling with ANN R-square values obtained training and validation at the level of 15% of the data vaidasi respectively - are respectively 0.8693 and 0.8329. While the extent of 20% of validation data obtained value of R-Square training at 0.8944 and the value of R-Square validation of 0.8768

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTP/2016/106/051602987
Uncontrolled Keywords: ANN, Buah Kelapa Sawit, Total Karoten,-ANN, Palm oil, Total Carotenoid
Subjects: 300 Social sciences > 338 Production > 338.1 Agriculture
Divisions: Fakultas Teknologi Pertanian > Keteknikan Pertanian
Depositing User: Budi Wahyono Wahyono
Date Deposited: 15 Apr 2016 10:44
Last Modified: 01 Dec 2021 03:36
URI: http://repository.ub.ac.id/id/eprint/150632
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