Hasil Output Uji Sensori Permutation Two Way Anova dan Uji Lanjut Pairwise Two Sample Permutation Test

library(readxl)
Data_Uji_Sensori_R<- read_excel("D:/SKRIPSI ANGGUN/Data Uji Sensori R.xlsx")

Data_Uji_Sensori_R$Konsentrasi_Starter <- as.factor(Data_Uji_Sensori_R$Konsentrasi_Starter)
Data_Uji_Sensori_R$Lama_Fermentasi <- as.factor(Data_Uji_Sensori_R$Lama_Fermentasi)
Data_Uji_Sensori_R$Warna_H<- as.numeric(Data_Uji_Sensori_R$Warna_H)

library(permuco)   
hasil_anova<-aovperm(Warna_H~Konsentrasi_Starter*Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

hasil_anova<-aovperm(Warna_H~Konsentrasi_Starter+Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Lama_Fermentasi <- pairwisePermutationTest(Warna_H~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison   Stat   p.value  p.adjust
## 1 18 Jam - 24 Jam = 0  3.019  0.002536 0.0038040
## 2 18 Jam - 36 Jam = 0  3.854 0.0001164 0.0003492
## 3 24 Jam - 36 Jam = 0 0.9197    0.3577 0.3577000

library(permuco) 
hasil_anova<-aovperm(Aroma_H~Konsentrasi_Starter*Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(permuco) 
hasil_anova<-aovperm(Aroma_H~Konsentrasi_Starter+Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(permuco)
hasil_anova<-aovperm(Rasa_H~Konsentrasi_Starter*Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(permuco)
hasil_anova<-aovperm(Rasa_H~Konsentrasi_Starter+Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Konsentrasi_Starter<- pairwisePermutationTest(Rasa_H~Konsentrasi_Starter, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Konsentrasi_Starter)

##        Comparison  Stat   p.value  p.adjust
## 1  0.05 - 0.1 = 0 1.151    0.2496 0.2496000
## 2 0.05 - 0.15 = 0 3.844 0.0001212 0.0003636
## 3  0.1 - 0.15 = 0 2.833  0.004613 0.0069200

library(rcompanion)
res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(Rasa_H~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison  Stat   p.value  p.adjust
## 1 18 Jam - 24 Jam = 0 4.988 6.097e-07 6.097e-07
## 2 18 Jam - 36 Jam = 0 10.49         0 0.000e+00
## 3 24 Jam - 36 Jam = 0 7.508 5.995e-14 8.992e-14

library(permuco)
hasil_anova<-aovperm(Kekentalan_H~Konsentrasi_Starter*Lama_Fermentasi, data=Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(permuco)
hasil_anova<-aovperm(Kekentalan_H~Konsentrasi_Starter+Lama_Fermentasi, data=Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(Kekentalan_H~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison   Stat p.value p.adjust
## 1 18 Jam - 24 Jam = 0  1.876 0.06067  0.09100
## 2 18 Jam - 36 Jam = 0    2.5 0.01242  0.03726
## 3 24 Jam - 36 Jam = 0 0.7621   0.446  0.44600

library(permuco)
hasil_anova<-aovperm(Warna_MH~Konsentrasi_Starter*Lama_Fermentasi,data= Data_Uji_Sensori_R,np= 10000)
summary(hasil_anova)

library(permuco)
hasil_anova<-aovperm(Warna_MH~Konsentrasi_Starter+Lama_Fermentasi,data= Data_Uji_Sensori_R,np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(Warna_MH~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison   Stat   p.value  p.adjust
## 1 18 Jam - 24 Jam = 0 -3.304 0.0009518 1.428e-03
## 2 18 Jam - 36 Jam = 0 -4.765 1.887e-06 5.661e-06
## 3 24 Jam - 36 Jam = 0 -1.659   0.09704 9.704e-02

library(permuco)
hasil_anova<-aovperm(Aroma_MH~Konsentrasi_Starter*Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Konsentrasi_Starter<- pairwisePermutationTest(Aroma_MH~Konsentrasi_Starter, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Konsentrasi_Starter)

##        Comparison   Stat  p.value p.adjust
## 1  0.05 - 0.1 = 0 -1.059   0.2897  0.28970
## 2 0.05 - 0.15 = 0 -2.618 0.008849  0.02655
## 3  0.1 - 0.15 = 0 -1.543   0.1228  0.18420

library(rcompanion)
res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(Aroma_MH~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison   Stat  p.value p.adjust
## 1 18 Jam - 24 Jam = 0  -2.26  0.02385 0.035780
## 2 18 Jam - 36 Jam = 0 -3.255 0.001133 0.003399
## 3 24 Jam - 36 Jam = 0 -1.172   0.2411 0.241100

library(permuco)
hasil_anova<-aovperm(RasaManis_MH~Konsentrasi_Starter*Lama_Fermentasi, Data_Uji_Sensori_R, method = "freedman_lane", np= 10000)
summary(hasil_anova)

library(rcompanion)
Data_Uji_Sensori_R$Kombinasi_Perlakuan <- interaction(Data_Uji_Sensori_R$Konsentrasi_Starter, Data_Uji_Sensori_R$Lama_Fermentasi, sep = ".")
res_posthoc_Kombinasi <- pairwisePermutationTest(RasaManis_MH~Kombinasi_Perlakuan, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Kombinasi)

##                       Comparison    Stat   p.value  p.adjust
## 1   0.05.18 Jam - 0.1.18 Jam = 0    4.96 7.043e-07 1.334e-06
## 2  0.05.18 Jam - 0.15.18 Jam = 0   4.828  1.38e-06 2.484e-06
## 3  0.05.18 Jam - 0.05.24 Jam = 0   6.369 1.899e-10 8.546e-10
## 4   0.05.18 Jam - 0.1.24 Jam = 0    5.08  3.77e-07 7.540e-07
## 5  0.05.18 Jam - 0.15.24 Jam = 0   7.654 1.932e-14 1.739e-13
## 6  0.05.18 Jam - 0.05.36 Jam = 0   8.801         0 0.000e+00
## 7   0.05.18 Jam - 0.1.36 Jam = 0   8.859         0 0.000e+00
## 8  0.05.18 Jam - 0.15.36 Jam = 0   9.002         0 0.000e+00
## 9   0.1.18 Jam - 0.15.18 Jam = 0 -0.3967    0.6916 7.243e-01
## 10  0.1.18 Jam - 0.05.24 Jam = 0  0.6645    0.5064 5.881e-01
## 11   0.1.18 Jam - 0.1.24 Jam = 0 -0.7955    0.4263 5.116e-01
## 12  0.1.18 Jam - 0.15.24 Jam = 0   2.845  0.004435 6.141e-03
## 13  0.1.18 Jam - 0.05.36 Jam = 0   5.626 1.843e-08 4.147e-08
## 14   0.1.18 Jam - 0.1.36 Jam = 0   5.522 3.345e-08 7.084e-08
## 15  0.1.18 Jam - 0.15.36 Jam = 0   5.857 4.725e-09 1.134e-08
## 16 0.15.18 Jam - 0.05.24 Jam = 0   1.158    0.2468 3.064e-01
## 17  0.15.18 Jam - 0.1.24 Jam = 0 -0.3797    0.7042 7.243e-01
## 18 0.15.18 Jam - 0.15.24 Jam = 0   3.382 0.0007198 1.037e-03
## 19 0.15.18 Jam - 0.05.36 Jam = 0   6.077 1.222e-09 3.999e-09
## 20  0.15.18 Jam - 0.1.36 Jam = 0   5.999 1.985e-09 5.586e-09
## 21 0.15.18 Jam - 0.15.36 Jam = 0   6.319 2.636e-10 1.009e-09
## 22  0.05.24 Jam - 0.1.24 Jam = 0  -1.748   0.08052 1.035e-01
## 23 0.05.24 Jam - 0.15.24 Jam = 0   2.722  0.006483 8.644e-03
## 24 0.05.24 Jam - 0.05.36 Jam = 0   5.996 2.017e-09 5.586e-09
## 25  0.05.24 Jam - 0.1.36 Jam = 0   5.928 3.071e-09 7.897e-09
## 26 0.05.24 Jam - 0.15.36 Jam = 0   6.309 2.804e-10 1.009e-09
## 27  0.1.24 Jam - 0.15.24 Jam = 0   4.152  3.29e-05 5.384e-05
## 28  0.1.24 Jam - 0.05.36 Jam = 0   6.788 1.137e-11 6.822e-11
## 29   0.1.24 Jam - 0.1.36 Jam = 0   6.761 1.373e-11 7.061e-11
## 30  0.1.24 Jam - 0.15.36 Jam = 0   7.066 1.597e-12 1.150e-11
## 31 0.15.24 Jam - 0.05.36 Jam = 0   4.139 3.483e-05 5.452e-05
## 32  0.15.24 Jam - 0.1.36 Jam = 0   3.945 7.997e-05 1.200e-04
## 33 0.15.24 Jam - 0.15.36 Jam = 0   4.459 8.251e-06 1.414e-05
## 34  0.05.36 Jam - 0.1.36 Jam = 0 -0.4292    0.6678 7.243e-01
## 35 0.05.36 Jam - 0.15.36 Jam = 0  0.1726     0.863 8.630e-01
## 36  0.1.36 Jam - 0.15.36 Jam = 0  0.6311     0.528 5.940e-01

library(permuco)
hasil_anova<-aovperm(RasaManis_MH~Konsentrasi_Starter+Lama_Fermentasi, Data_Uji_Sensori_R, method = "freedman_lane", np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Konsentrasi_Starter<- pairwisePermutationTest(RasaManis_MH~Konsentrasi_Starter, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Konsentrasi_Starter)

##        Comparison  Stat   p.value  p.adjust
## 1  0.05 - 0.1 = 0 1.738   0.08222 0.0822200
## 2 0.05 - 0.15 = 0 3.657 0.0002548 0.0007644
## 3  0.1 - 0.15 = 0 2.017   0.04374 0.0656100

res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(RasaManis_MH~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison  Stat   p.value  p.adjust
## 1 18 Jam - 24 Jam = 0 4.862 1.163e-06 1.163e-06
## 2 18 Jam - 36 Jam = 0 11.92         0 0.000e+00
## 3 24 Jam - 36 Jam = 0 9.935         0 0.000e+00

library(permuco)
hasil_anova<-aovperm(RasaAsam_MH~Konsentrasi_Starter*Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(rcompanion)
Data_Uji_Sensori_R$Kombinasi_Perlakuan <- interaction(Data_Uji_Sensori_R$Konsentrasi_Starter, Data_Uji_Sensori_R$Lama_Fermentasi, sep = ".")
res_posthoc_Kombinasi <- pairwisePermutationTest(RasaAsam_MH~Kombinasi_Perlakuan, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Kombinasi)

##                       Comparison    Stat   p.value  p.adjust
## 1   0.05.18 Jam - 0.1.18 Jam = 0  -4.343 1.404e-05 2.660e-05
## 2  0.05.18 Jam - 0.15.18 Jam = 0  -4.183 2.879e-05 5.182e-05
## 3  0.05.18 Jam - 0.05.24 Jam = 0  -6.745 1.532e-11 1.103e-10
## 4   0.05.18 Jam - 0.1.24 Jam = 0  -5.203 1.957e-07 4.697e-07
## 5  0.05.18 Jam - 0.15.24 Jam = 0  -7.625 2.447e-14 2.202e-13
## 6  0.05.18 Jam - 0.05.36 Jam = 0  -8.547 1.268e-17 2.282e-16
## 7   0.05.18 Jam - 0.1.36 Jam = 0  -8.415 3.917e-17 4.700e-16
## 8  0.05.18 Jam - 0.15.36 Jam = 0  -8.966 3.084e-19 1.110e-17
## 9   0.1.18 Jam - 0.15.18 Jam = 0  0.5738    0.5661 6.163e-01
## 10  0.1.18 Jam - 0.05.24 Jam = 0  -1.461    0.1441 1.729e-01
## 11   0.1.18 Jam - 0.1.24 Jam = 0 -0.2017    0.8401 8.401e-01
## 12  0.1.18 Jam - 0.15.24 Jam = 0  -3.258   0.00112 1.680e-03
## 13  0.1.18 Jam - 0.05.36 Jam = 0  -5.166 2.393e-07 5.384e-07
## 14   0.1.18 Jam - 0.1.36 Jam = 0  -4.873 1.099e-06 2.198e-06
## 15  0.1.18 Jam - 0.15.36 Jam = 0  -5.575 2.473e-08 7.419e-08
## 16 0.15.18 Jam - 0.05.24 Jam = 0   -2.33   0.01979 2.544e-02
## 17  0.15.18 Jam - 0.1.24 Jam = 0 -0.8798     0.379 4.264e-01
## 18 0.15.18 Jam - 0.15.24 Jam = 0  -4.149 3.333e-05 5.714e-05
## 19 0.15.18 Jam - 0.05.36 Jam = 0      -6 1.975e-09 7.900e-09
## 20  0.15.18 Jam - 0.1.36 Jam = 0  -5.724 1.042e-08 3.410e-08
## 21 0.15.18 Jam - 0.15.36 Jam = 0  -6.473 9.599e-11 5.759e-10
## 22  0.05.24 Jam - 0.1.24 Jam = 0   1.514      0.13 1.614e-01
## 23 0.05.24 Jam - 0.15.24 Jam = 0   -2.71  0.006729 9.317e-03
## 24 0.05.24 Jam - 0.05.36 Jam = 0    -5.4 6.675e-08 1.716e-07
## 25  0.05.24 Jam - 0.1.36 Jam = 0  -5.006  5.57e-07 1.180e-06
## 26 0.05.24 Jam - 0.15.36 Jam = 0  -6.157 7.434e-10 3.345e-09
## 27  0.1.24 Jam - 0.15.24 Jam = 0  -3.624 0.0002904 4.545e-04
## 28  0.1.24 Jam - 0.05.36 Jam = 0   -5.75  8.93e-09 3.215e-08
## 29   0.1.24 Jam - 0.1.36 Jam = 0  -5.435 5.494e-08 1.521e-07
## 30  0.1.24 Jam - 0.15.36 Jam = 0  -6.306 2.862e-10 1.472e-09
## 31 0.15.24 Jam - 0.05.36 Jam = 0   -3.11   0.00187 2.693e-03
## 32  0.15.24 Jam - 0.1.36 Jam = 0  -2.615  0.008932 1.191e-02
## 33 0.15.24 Jam - 0.15.36 Jam = 0  -3.726 0.0001948 3.188e-04
## 34  0.05.36 Jam - 0.1.36 Jam = 0  0.5503    0.5821 6.163e-01
## 35 0.05.36 Jam - 0.15.36 Jam = 0 -0.3085    0.7577 7.793e-01
## 36  0.1.36 Jam - 0.15.36 Jam = 0 -0.9244    0.3553 4.126e-01

library(permuco)
hasil_anova<-aovperm(RasaAsam_MH~Konsentrasi_Starter+Lama_Fermentasi, data= Data_Uji_Sensori_R, np= 10000)
summary(hasil_anova)

library(rcompanion)
res_posthoc_Konsentrasi_Starter<- pairwisePermutationTest(RasaAsam_MH~Konsentrasi_Starter, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Konsentrasi_Starter)

##        Comparison   Stat   p.value p.adjust
## 1  0.05 - 0.1 = 0 -1.665   0.09596  0.09596
## 2 0.05 - 0.15 = 0 -3.505 0.0004567  0.00137
## 3  0.1 - 0.15 = 0 -1.808   0.07063  0.09596

library(rcompanion)
res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(RasaAsam_MH~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison   Stat   p.value  p.adjust
## 1 18 Jam - 24 Jam = 0  -5.98 2.227e-09 2.227e-09
## 2 18 Jam - 36 Jam = 0 -11.46 2.101e-30 6.303e-30
## 3 24 Jam - 36 Jam = 0 -8.386 5.028e-17 7.542e-17

library(permuco)
hasil_anova<-aovperm(Sensasisoda_MH~Konsentrasi_Starter*Lama_Fermentasi, Data_Uji_Sensori_R, method = "freedman_lane", np= 10000)
summary(hasil_anova)

library(permuco)
hasil_anova<-aovperm(Sensasisoda_MH~Konsentrasi_Starter+Lama_Fermentasi, Data_Uji_Sensori_R, method = "freedman_lane", np= 10000)
summary(hasil_anova)

res_posthoc_Lama_Fermentasi<- pairwisePermutationTest(Sensasisoda_MH~Lama_Fermentasi, data = Data_Uji_Sensori_R, method = "fdr")
print(res_posthoc_Lama_Fermentasi)

##            Comparison   Stat   p.value  p.adjust
## 1 18 Jam - 24 Jam = 0 -4.739 2.145e-06 3.217e-06
## 2 18 Jam - 36 Jam = 0  -7.19 6.498e-13 1.949e-12
## 3 24 Jam - 36 Jam = 0 -3.007  0.002634 2.634e-03

tinytex::tinytex_root("C:/Users/ASUS/Downloads/TinyTeX.zip")

## [1] "C:\\Users\\ASUS\\AppData\\Roaming\\TinyTeX"

tinytex::is_tinytex()

## [1] TRUE

Hasil Output Uji Sensori Permutation Two Way Anova dan Uji Lanjut Pairwise Two Sample Permutation Test_R

2025-09-25