ANALISIS VARIAN PERCOBAAN FAKTORIAL DUA FAKTOR RAKL DENGAN METODE FIXED ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTION - Akhmad Zaki; Triastuti Wuryandari; Suparti Suparti (Jurnal Gaussian, 2014)
Factorial experiment is an experiment where is in a condition (experiment unit) were attempted simultaneously from several single experiment. Two-factor factorial experiment with the basic design CRBD (Completely Randomized Block Design) is used to assess the interaction of genotype and environment on multi-location trials. The analysis can be applied in multi-location trials is AMMI analysis (additive main effects and multiplicative interaction). AMMI analysis in the calculations using analysis of variance in a factorial experiment to test the effect of the interaction and Principal Component Analysis (PCA)  to elucidate the effect of the interaction with the interpretation of the results using the biplot-AMMI. Based on research with seven genotypes of rice (S382b-2-2-3, 3-2-3-1 S2389d-, S24871-65-4, S2824-1d-6, S2945f-59, Poso, and C22) and four locations (Sukamandi 94, Batang 94, Taman Bogo 94, and Garut 94) there is the influence of genotype, location, and interaction with genotype and location on rice production. Obtained three Principal Component Interactions (KUI1, KUI2 and KUI3) with the contribution of diversity respectively 78.29%, 13.94% and 7.77%. Interpretation of the AMMI Biplot is obtained genotype 1 (S382b-2-2-3) very suitable to be planted in a location 4 (Garut 94), genotype 2 (S2389d-3-2-3-1) very suitable to be planted in a location 3 (Taman Bogo 94), genotype 3 (S24871-65-4) is more suitable to be planted in locations 2 (Batang 94), genotype 4 (S2824-1d-6) are very suitable to be planted in a location 4 (Garut 94), genotype 5 (S2945f-59) is more suitable to be planted in locations 2 (Batang 94), genotype 6 (Poso) very suitable to be planted in a location 1 (Sukamandi 94) and genotype 7 (C22) is very suitable to be planted in locations 2 (Batang 94). Keywords: Factorial Experiment, CRBD, AMMI, Analysis of Variance, PCA, Biplot