TY  - GEN
CY  - UNIVERSITAS LAMPUNG
ID  - eprints24854
UR  - http://digilib.unila.ac.id/24854/
A1  - NOVI APRATIWI , 1214071056
Y1  - 2016/12/06/
N2  - Penelitian ini bertujuan untuk mengidentifikasi kemurnian kopi luwak
menggunakan metode soft independent modeling of class analogy (SIMCA) dan
principal component analysis (PCA). Pengujian dilakukan pada bubuk kopi
berukuran 0.297 milimeter (mesh 50).
Perbandingan campuran, sampel 1- 50 masing-masing 1 g kopi luwak murni,
sampel 51? 60 masing-masing 0.9 g luwak dan 0.1 g arabika, sampel 61 ? 70
masing-masing 0.8 g luwak dan 0.2 g arabika, sampel 71 ? 80 masing-masing 0.7
g luwak dan 0.3 g arabika,sampel 81 ? 90 masing-masing 0.6 g luwak dan 0.4 g
arabika, sampel 90 ? 100 masing-masing 0.5 g luwak dan 0.5 g arabika.
Hasil klasifikasi menunjukkan metode PCA dan SIMCA mampu mengidentifikasi
campuran kopi luwak. PC1 menjelaskan 75% keragaman data dan PC2
menjelaskan 17% keragaman data. Sedangkan untuk klasifikasi SIMCA
diperoleh nilai persentase untuk nilai spesifisitas 76%, sensitivitas 84% dan
akurasi sebesar 80%, dengan nilai eror sebesar 23%.
Kata Kunci : Kopi arabika, kopi luwak, PCA, SIMCA, UV-Vis spectroscopy

ABSTRACT

This study aims to identify the authentication of civet coffee using a Soft
independent modeling of class analogy (SIMCA) method and principal
component analysis (PCA). The test carried out on the coffee powder measuring
0.297 millimeters (mesh 50).
Comparison of blend that is samples 1- 50 each 1 g of pure civet coffee, samples
51- 60 each 0.9 g civet coffee and 0.1 g arabica coffee, samples 61-70 each 0.8 g
civet coffee and 0.2 g arabica coffee, samples 71-80 each 0.7 g civet coffee and
0.3 g arabica coffee, samples 81-90 each 0.6 g civet coffee and 0.4 g arabica
coffee, samples 90-100 each 0.5 g civet coffee and 0.5 g arabica coffee.
The classification results show SIMCA and PCA methods are able to identify
civet coffee mixture. PC 1 explains 75% the variance of data and PC2 explains
17% the variance of data. Values obtained on SIMCA classification are
specificity 76%, sensitivity of 84% and accuracy of 80%, with a value error of
23%.
Keywords: Arabica coffee,civet coffee, PCA, SIMCA, UV-Vis spectroscopy.
PB  - FAKULTAS PERTANIAN
TI  - STUDI PENGGUNAAN UV-VIS SPECTROSCOPY UNTUK
IDENTIFIKASI CAMPURAN KOPI LUWAK DENGAN KOPI ARABIKA
AV  - restricted
ER  -