# ============================================================
# ANALISIS UJI ASUMSI DAN ANOVA RAK
# Penelitian: Pemupukan Padi Inpari 32
# Perlakuan: A0, A1, A2, A3, A4
# Rancangan: Rancangan Acak Kelompok (RAK)
# Ulangan: 5 kelompok
# ============================================================
# 1. INSTALASI DAN MEMANGGIL PAKET
# ------------------------------------------------------------
if (!requireNamespace("readxl", quietly = TRUE)) {
install.packages("readxl")
}
if (!requireNamespace("car", quietly = TRUE)) {
install.packages("car")
}
library(readxl)
## Warning: package 'readxl' was built under R version 4.5.3
library(car)
## Warning: package 'car' was built under R version 4.5.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.5.3
# 2. IMPORT DATA EXCEL
# ------------------------------------------------------------
cat("Silakan pilih file Data jurnal.xlsx\n")
## Silakan pilih file Data jurnal.xlsx
data <- read_excel(file.choose(), sheet = 1)
data <- as.data.frame(data)
# Memeriksa nama kolom
print(names(data))
## [1] "Kelompok" "Perlakuan" "gabah.hampa"
## [4] "gabah.bernas" "jumlah.gabah.bernas" "bobot.1000butir"
## [7] "GKG" "PFPN" "Tinggi_H"
## [10] "Total.Chlo_H" "Biomasa_H" "LD_H"
## [13] "Tinggi_15" "Total.Chlo_15" "LD_15"
## [16] "N" "Anakan.produktif"
# Mengubah Kelompok dan Perlakuan menjadi faktor
data$Kelompok <- factor(data$Kelompok)
data$Perlakuan <- factor(
data$Perlakuan,
levels = c("A0", "A1", "A2", "A3", "A4")
)
# Menentukan seluruh peubah respons
peubah <- setdiff(
names(data),
c("Kelompok", "Perlakuan")
)
# Memastikan seluruh peubah respons numerik
for (v in peubah) {
data[[v]] <- as.numeric(data[[v]])
}
# Memeriksa struktur data
cat("\nJUMLAH PENGAMATAN:", nrow(data), "\n")
##
## JUMLAH PENGAMATAN: 25
cat("JUMLAH KELOMPOK:", nlevels(data$Kelompok), "\n")
## JUMLAH KELOMPOK: 5
cat("JUMLAH PERLAKUAN:", nlevels(data$Perlakuan), "\n")
## JUMLAH PERLAKUAN: 5
cat("JUMLAH PEUBAH:", length(peubah), "\n")
## JUMLAH PEUBAH: 15
# Memeriksa jumlah ulangan setiap perlakuan
print(table(data$Kelompok, data$Perlakuan))
##
## A0 A1 A2 A3 A4
## 1 1 1 1 1 1
## 2 1 1 1 1 1
## 3 1 1 1 1 1
## 4 1 1 1 1 1
## 5 1 1 1 1 1
# 3. MENYIAPKAN TABEL HASIL
# ------------------------------------------------------------
hasil_asumsi <- data.frame()
hasil_anova <- data.frame()
hasil_anova_lengkap <- list()
# 4. ANALISIS SETIAP PEUBAH
# ------------------------------------------------------------
for (v in peubah) {
cat("\n")
cat("================================================\n")
cat("PEUBAH:", v, "\n")
cat("================================================\n")
# Membuat data sementara
d <- data[, c("Kelompok", "Perlakuan", v)]
names(d)[3] <- "Y"
d <- na.omit(d)
d$Kelompok <- droplevels(d$Kelompok)
d$Perlakuan <- factor(
d$Perlakuan,
levels = c("A0", "A1", "A2", "A3", "A4")
)
# Model ANOVA RAK
model <- aov(
Y ~ Kelompok + Perlakuan,
data = d
)
# ----------------------------------------------------------
# A. UJI HOMOGENITAS RAGAM (LEVENE TEST)
# ----------------------------------------------------------
uji_levene <- car::leveneTest(
Y ~ Perlakuan,
data = d,
center = median
)
levene_F <- as.numeric(uji_levene[1, "F value"])
levene_p <- as.numeric(uji_levene[1, "Pr(>F)"])
# ----------------------------------------------------------
# B. UJI NORMALITAS (SHAPIRO-WILK)
# ----------------------------------------------------------
residual <- residuals(model)
if (
length(residual) >= 3 &&
length(residual) <= 5000 &&
length(unique(residual)) > 2
) {
uji_shapiro <- shapiro.test(residual)
shapiro_W <- as.numeric(uji_shapiro$statistic)
shapiro_p <- uji_shapiro$p.value
} else {
shapiro_W <- NA
shapiro_p <- NA
}
# ----------------------------------------------------------
# C. MENYIMPAN HASIL UJI ASUMSI
# ----------------------------------------------------------
hasil_asumsi <- rbind(
hasil_asumsi,
data.frame(
Peubah = v,
Levene_F = levene_F,
Levene_P = levene_p,
Shapiro_W = shapiro_W,
Shapiro_P = shapiro_p
)
)
# ----------------------------------------------------------
# D. ANOVA RAK
# ----------------------------------------------------------
tabel <- anova(model)
tabel <- as.data.frame(tabel)
tabel$Sumber <- rownames(tabel)
rownames(tabel) <- NULL
tabel <- tabel[, c(
"Sumber",
"Df",
"Sum Sq",
"Mean Sq",
"F value",
"Pr(>F)"
)]
tabel$Peubah <- v
hasil_anova_lengkap[[v]] <- tabel
# Mengambil hasil khusus perlakuan
baris <- tabel[tabel$Sumber == "Perlakuan", ]
hasil_anova <- rbind(
hasil_anova,
data.frame(
Peubah = v,
DB = baris$Df,
JK = baris$`Sum Sq`,
KT = baris$`Mean Sq`,
F_hitung = baris$`F value`,
P_value = baris$`Pr(>F)`,
Keputusan = ifelse(
baris$`Pr(>F)` < 0.05,
"NYATA",
"TIDAK NYATA"
)
)
)
# Menampilkan hasil setiap peubah
cat("\nHASIL UJI LEVENE:\n")
print(uji_levene)
cat("\nHASIL UJI SHAPIRO-WILK:\n")
print(shapiro.test(residual))
cat("\nHASIL ANOVA RAK:\n")
print(tabel)
}
##
## ================================================
## PEUBAH: gabah.hampa
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.3286 0.8554
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.97431, p-value = 0.7547
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 30.77508 7.693770 2.696633 0.06840578 gabah.hampa
## 2 Perlakuan 4 38.00481 9.501203 3.330130 0.03644060 gabah.hampa
## 3 Residuals 16 45.64965 2.853103 NA NA gabah.hampa
##
## ================================================
## PEUBAH: gabah.bernas
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.3286 0.8554
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.97431, p-value = 0.7547
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 30.77508 7.693770 2.696633 0.06840578 gabah.bernas
## 2 Perlakuan 4 38.00481 9.501203 3.330130 0.03644060 gabah.bernas
## 3 Residuals 16 45.64965 2.853103 NA NA gabah.bernas
##
## ================================================
## PEUBAH: jumlah.gabah.bernas
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.8125 0.5319
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.95774, p-value = 0.3714
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 2984.235 746.0588 2.361604 0.09698798 jumlah.gabah.bernas
## 2 Perlakuan 4 4017.385 1004.3462 3.179198 0.04218758 jumlah.gabah.bernas
## 3 Residuals 16 5054.590 315.9119 NA NA jumlah.gabah.bernas
##
## ================================================
## PEUBAH: bobot.1000butir
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.7131 0.5927
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.97804, p-value = 0.8437
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 7.049974 1.7624934 5.843751 0.004273689 bobot.1000butir
## 2 Perlakuan 4 4.187953 1.0469883 3.471411 0.031836514 bobot.1000butir
## 3 Residuals 16 4.825650 0.3016031 NA NA bobot.1000butir
##
## ================================================
## PEUBAH: GKG
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.5484 0.7023
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.96344, p-value = 0.4872
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 2.409618 0.6024045 0.7268018 0.58649476 GKG
## 2 Perlakuan 4 8.505138 2.1262844 2.5653646 0.07833065 GKG
## 3 Residuals 16 13.261488 0.8288430 NA NA GKG
##
## ================================================
## PEUBAH: PFPN
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 1.0303 0.416
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.95722, p-value = 0.3619
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 161.7564 40.43910 0.9829281 4.445271e-01 PFPN
## 2 Perlakuan 4 3386.4150 846.60375 20.5778706 3.788826e-06 PFPN
## 3 Residuals 16 658.2634 41.14147 NA NA PFPN
##
## ================================================
## PEUBAH: Tinggi_H
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.8219 0.5265
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.96064, p-value = 0.4274
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 27.4344 6.85860 0.6904272 0.6091968 Tinggi_H
## 2 Perlakuan 4 101.2144 25.30360 2.5472098 0.0798229 Tinggi_H
## 3 Residuals 16 158.9416 9.93385 NA NA Tinggi_H
##
## ================================================
## PEUBAH: Total.Chlo_H
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.2758 0.8901
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.97184, p-value = 0.692
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 2.332404 0.5831009 4.096690 0.017910974 Total.Chlo_H
## 2 Perlakuan 4 3.651525 0.9128812 6.413626 0.002808708 Total.Chlo_H
## 3 Residuals 16 2.277354 0.1423347 NA NA Total.Chlo_H
##
## ================================================
## PEUBAH: Biomasa_H
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 1.226 0.3313
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.97198, p-value = 0.6955
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 7238.3 1809.5750 1.854441 0.1678305 Biomasa_H
## 2 Perlakuan 4 6950.3 1737.5750 1.780656 0.1820866 Biomasa_H
## 3 Residuals 16 15612.9 975.8063 NA NA Biomasa_H
##
## ================================================
## PEUBAH: LD_H
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 1.4957 0.2412
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.95537, p-value = 0.3299
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 449309.4 112327.3 0.320058 8.603895e-01 LD_H
## 2 Perlakuan 4 36214489.0 9053622.2 25.796788 8.359526e-07 LD_H
## 3 Residuals 16 5615348.6 350959.3 NA NA LD_H
##
## ================================================
## PEUBAH: Tinggi_15
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.6376 0.6417
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.96134, p-value = 0.4418
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 69.94 17.48500 1.208675 0.3455263 Tinggi_15
## 2 Perlakuan 4 166.14 41.53500 2.871166 0.0572812 Tinggi_15
## 3 Residuals 16 231.46 14.46625 NA NA Tinggi_15
##
## ================================================
## PEUBAH: Total.Chlo_15
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 1.3422 0.289
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.96363, p-value = 0.4914
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 4.176743 1.0441859 1.998219 0.1433459 Total.Chlo_15
## 2 Perlakuan 4 3.377766 0.8444415 1.615976 0.2187013 Total.Chlo_15
## 3 Residuals 16 8.360933 0.5225583 NA NA Total.Chlo_15
##
## ================================================
## PEUBAH: LD_15
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 1.3835 0.2753
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.97416, p-value = 0.7509
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 143741.6 35935.4 0.2553697 0.9021307341 LD_15
## 2 Perlakuan 4 6772082.9 1693020.7 12.0312063 0.0001052179 LD_15
## 3 Residuals 16 2251505.9 140719.1 NA NA LD_15
##
## ================================================
## PEUBAH: N
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.2297 0.9185
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.92564, p-value = 0.06897
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 0.610936 0.1527340 3.464417 0.03204866 N
## 2 Perlakuan 4 0.425936 0.1064840 2.415343 0.09163839 N
## 3 Residuals 16 0.705384 0.0440865 NA NA N
##
## ================================================
## PEUBAH: Anakan.produktif
## ================================================
##
## HASIL UJI LEVENE:
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.3476 0.8425
## 20
##
## HASIL UJI SHAPIRO-WILK:
##
## Shapiro-Wilk normality test
##
## data: residual
## W = 0.98976, p-value = 0.9949
##
##
## HASIL ANOVA RAK:
## Sumber Df Sum Sq Mean Sq F value Pr(>F) Peubah
## 1 Kelompok 4 143.14 35.7850 0.9811502 0.4454016 Anakan.produktif
## 2 Perlakuan 4 340.04 85.0100 2.3307972 0.1002069 Anakan.produktif
## 3 Residuals 16 583.56 36.4725 NA NA Anakan.produktif
# 5. MENGGABUNGKAN SELURUH TABEL ANOVA
# ------------------------------------------------------------
anova_lengkap <- do.call(
rbind,
hasil_anova_lengkap
)
# 6. MENAMPILKAN RINGKASAN HASIL
# ------------------------------------------------------------
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("TABEL 1. HASIL UJI ASUMSI ANOVA\n")
## TABEL 1. HASIL UJI ASUMSI ANOVA
cat("====================================================\n")
## ====================================================
print(hasil_asumsi, row.names = FALSE)
## Peubah Levene_F Levene_P Shapiro_W Shapiro_P
## gabah.hampa 0.3286383 0.8554167 0.9743116 0.75465967
## gabah.bernas 0.3286383 0.8554167 0.9743116 0.75465967
## jumlah.gabah.bernas 0.8125479 0.5319482 0.9577436 0.37136906
## bobot.1000butir 0.7130937 0.5926749 0.9780410 0.84372538
## GKG 0.5483951 0.7023066 0.9634401 0.48715104
## PFPN 1.0302526 0.4159772 0.9572231 0.36194859
## Tinggi_H 0.8218817 0.5264918 0.9606408 0.42738575
## Total.Chlo_H 0.2757875 0.8901299 0.9718428 0.69203845
## Biomasa_H 1.2259865 0.3313215 0.9719791 0.69551842
## LD_H 1.4956826 0.2411601 0.9553672 0.32994147
## Tinggi_15 0.6375628 0.6417311 0.9613415 0.44183718
## Total.Chlo_15 1.3421599 0.2890417 0.9636300 0.49139694
## LD_15 1.3834957 0.2752984 0.9741614 0.75089361
## N 0.2296718 0.9184763 0.9256412 0.06896761
## Anakan.produktif 0.3476259 0.8425364 0.9897578 0.99489976
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("TABEL 2. RINGKASAN ANOVA RAK SELURUH PEUBAH\n")
## TABEL 2. RINGKASAN ANOVA RAK SELURUH PEUBAH
cat("====================================================\n")
## ====================================================
print(hasil_anova, row.names = FALSE)
## Peubah DB JK KT F_hitung P_value
## gabah.hampa 4 3.800481e+01 9.501203e+00 3.330130 3.644060e-02
## gabah.bernas 4 3.800481e+01 9.501203e+00 3.330130 3.644060e-02
## jumlah.gabah.bernas 4 4.017385e+03 1.004346e+03 3.179198 4.218758e-02
## bobot.1000butir 4 4.187953e+00 1.046988e+00 3.471411 3.183651e-02
## GKG 4 8.505138e+00 2.126284e+00 2.565365 7.833065e-02
## PFPN 4 3.386415e+03 8.466038e+02 20.577871 3.788826e-06
## Tinggi_H 4 1.012144e+02 2.530360e+01 2.547210 7.982290e-02
## Total.Chlo_H 4 3.651525e+00 9.128812e-01 6.413626 2.808708e-03
## Biomasa_H 4 6.950300e+03 1.737575e+03 1.780656 1.820866e-01
## LD_H 4 3.621449e+07 9.053622e+06 25.796788 8.359526e-07
## Tinggi_15 4 1.661400e+02 4.153500e+01 2.871166 5.728120e-02
## Total.Chlo_15 4 3.377766e+00 8.444415e-01 1.615976 2.187013e-01
## LD_15 4 6.772083e+06 1.693021e+06 12.031206 1.052179e-04
## N 4 4.259360e-01 1.064840e-01 2.415343 9.163839e-02
## Anakan.produktif 4 3.400400e+02 8.501000e+01 2.330797 1.002069e-01
## Keputusan
## NYATA
## NYATA
## NYATA
## NYATA
## TIDAK NYATA
## NYATA
## TIDAK NYATA
## NYATA
## TIDAK NYATA
## NYATA
## TIDAK NYATA
## TIDAK NYATA
## NYATA
## TIDAK NYATA
## TIDAK NYATA
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("TABEL 3. ANOVA LENGKAP SELURUH PEUBAH\n")
## TABEL 3. ANOVA LENGKAP SELURUH PEUBAH
cat("====================================================\n")
## ====================================================
print(anova_lengkap, row.names = FALSE)
## Sumber Df Sum Sq Mean Sq F value Pr(>F)
## Kelompok 4 3.077508e+01 7.693770e+00 2.6966325 6.840578e-02
## Perlakuan 4 3.800481e+01 9.501203e+00 3.3301295 3.644060e-02
## Residuals 16 4.564965e+01 2.853103e+00 NA NA
## Kelompok 4 3.077508e+01 7.693770e+00 2.6966325 6.840578e-02
## Perlakuan 4 3.800481e+01 9.501203e+00 3.3301295 3.644060e-02
## Residuals 16 4.564965e+01 2.853103e+00 NA NA
## Kelompok 4 2.984235e+03 7.460588e+02 2.3616040 9.698798e-02
## Perlakuan 4 4.017385e+03 1.004346e+03 3.1791975 4.218758e-02
## Residuals 16 5.054590e+03 3.159119e+02 NA NA
## Kelompok 4 7.049974e+00 1.762493e+00 5.8437509 4.273689e-03
## Perlakuan 4 4.187953e+00 1.046988e+00 3.4714109 3.183651e-02
## Residuals 16 4.825650e+00 3.016031e-01 NA NA
## Kelompok 4 2.409618e+00 6.024045e-01 0.7268018 5.864948e-01
## Perlakuan 4 8.505138e+00 2.126284e+00 2.5653646 7.833065e-02
## Residuals 16 1.326149e+01 8.288430e-01 NA NA
## Kelompok 4 1.617564e+02 4.043910e+01 0.9829281 4.445271e-01
## Perlakuan 4 3.386415e+03 8.466038e+02 20.5778706 3.788826e-06
## Residuals 16 6.582634e+02 4.114147e+01 NA NA
## Kelompok 4 2.743440e+01 6.858600e+00 0.6904272 6.091968e-01
## Perlakuan 4 1.012144e+02 2.530360e+01 2.5472098 7.982290e-02
## Residuals 16 1.589416e+02 9.933850e+00 NA NA
## Kelompok 4 2.332404e+00 5.831009e-01 4.0966900 1.791097e-02
## Perlakuan 4 3.651525e+00 9.128812e-01 6.4136261 2.808708e-03
## Residuals 16 2.277354e+00 1.423347e-01 NA NA
## Kelompok 4 7.238300e+03 1.809575e+03 1.8544409 1.678305e-01
## Perlakuan 4 6.950300e+03 1.737575e+03 1.7806557 1.820866e-01
## Residuals 16 1.561290e+04 9.758063e+02 NA NA
## Kelompok 4 4.493094e+05 1.123273e+05 0.3200580 8.603895e-01
## Perlakuan 4 3.621449e+07 9.053622e+06 25.7967875 8.359526e-07
## Residuals 16 5.615349e+06 3.509593e+05 NA NA
## Kelompok 4 6.994000e+01 1.748500e+01 1.2086754 3.455263e-01
## Perlakuan 4 1.661400e+02 4.153500e+01 2.8711656 5.728120e-02
## Residuals 16 2.314600e+02 1.446625e+01 NA NA
## Kelompok 4 4.176743e+00 1.044186e+00 1.9982189 1.433459e-01
## Perlakuan 4 3.377766e+00 8.444415e-01 1.6159757 2.187013e-01
## Residuals 16 8.360933e+00 5.225583e-01 NA NA
## Kelompok 4 1.437416e+05 3.593540e+04 0.2553697 9.021307e-01
## Perlakuan 4 6.772083e+06 1.693021e+06 12.0312063 1.052179e-04
## Residuals 16 2.251506e+06 1.407191e+05 NA NA
## Kelompok 4 6.109360e-01 1.527340e-01 3.4644165 3.204866e-02
## Perlakuan 4 4.259360e-01 1.064840e-01 2.4153426 9.163839e-02
## Residuals 16 7.053840e-01 4.408650e-02 NA NA
## Kelompok 4 1.431400e+02 3.578500e+01 0.9811502 4.454016e-01
## Perlakuan 4 3.400400e+02 8.501000e+01 2.3307972 1.002069e-01
## Residuals 16 5.835600e+02 3.647250e+01 NA NA
## Peubah
## gabah.hampa
## gabah.hampa
## gabah.hampa
## gabah.bernas
## gabah.bernas
## gabah.bernas
## jumlah.gabah.bernas
## jumlah.gabah.bernas
## jumlah.gabah.bernas
## bobot.1000butir
## bobot.1000butir
## bobot.1000butir
## GKG
## GKG
## GKG
## PFPN
## PFPN
## PFPN
## Tinggi_H
## Tinggi_H
## Tinggi_H
## Total.Chlo_H
## Total.Chlo_H
## Total.Chlo_H
## Biomasa_H
## Biomasa_H
## Biomasa_H
## LD_H
## LD_H
## LD_H
## Tinggi_15
## Tinggi_15
## Tinggi_15
## Total.Chlo_15
## Total.Chlo_15
## Total.Chlo_15
## LD_15
## LD_15
## LD_15
## N
## N
## N
## Anakan.produktif
## Anakan.produktif
## Anakan.produktif
# 7. MENYIMPAN HASIL DALAM BENTUK CSV
# ------------------------------------------------------------
write.csv(
hasil_asumsi,
"Hasil_Uji_Asumsi.csv",
row.names = FALSE
)
write.csv(
hasil_anova,
"Hasil_Ringkasan_ANOVA.csv",
row.names = FALSE
)
write.csv(
anova_lengkap,
"Hasil_ANOVA_Lengkap.csv",
row.names = FALSE
)
# 8. SELESAI
# ------------------------------------------------------------
cat("\n")
cat("============================================\n")
## ============================================
cat("ANALISIS UJI ASUMSI DAN ANOVA RAK SELESAI\n")
## ANALISIS UJI ASUMSI DAN ANOVA RAK SELESAI
cat("============================================\n")
## ============================================
cat("Jumlah peubah dianalisis:", length(peubah), "\n")
## Jumlah peubah dianalisis: 15
cat("File hasil CSV telah disimpan di Working Directory.\n")
## File hasil CSV telah disimpan di Working Directory.
cat("============================================\n")
## ============================================
# ============================================================
# MENGHITUNG KOEFISIEN KERAGAMAN (KK / CV%)
# UNTUK SELURUH PEUBAH ANOVA RAK
# ============================================================
hasil_KK <- data.frame()
for (v in peubah) {
# Mengambil data setiap peubah
d <- data[, c("Kelompok", "Perlakuan", v)]
names(d)[3] <- "Y"
d <- na.omit(d)
# Model ANOVA RAK
model <- aov(
Y ~ Kelompok + Perlakuan,
data = d
)
# Mengambil tabel ANOVA
tabel <- anova(model)
# Kuadrat Tengah Galat
KT_galat <- tabel["Residuals", "Mean Sq"]
# Rataan umum
rataan <- mean(d$Y)
# Menghitung Koefisien Keragaman
if (!is.na(KT_galat) &&
KT_galat >= 0 &&
!is.na(rataan) &&
rataan != 0) {
KK <- (sqrt(KT_galat) / abs(rataan)) * 100
} else {
KK <- NA_real_
}
# Menyimpan hasil
hasil_KK <- rbind(
hasil_KK,
data.frame(
Peubah = v,
Rataan = rataan,
KT_Galat = KT_galat,
KK_Persen = KK
)
)
}
# Menampilkan hasil
cat("\n========================================\n")
##
## ========================================
cat("KOEFISIEN KERAGAMAN SELURUH PEUBAH\n")
## KOEFISIEN KERAGAMAN SELURUH PEUBAH
cat("========================================\n")
## ========================================
print(hasil_KK, row.names = FALSE)
## Peubah Rataan KT_Galat KK_Persen
## gabah.hampa 5.422770 2.853103e+00 31.148528
## gabah.bernas 94.577230 2.853103e+00 1.785962
## jumlah.gabah.bernas 172.540000 3.159119e+02 10.301327
## bobot.1000butir 25.886802 3.016031e-01 2.121483
## GKG 8.241581 8.288430e-01 11.046522
## PFPN 55.500560 4.114147e+01 11.556931
## Tinggi_H 104.328000 9.933850e+00 3.021050
## Total.Chlo_H 3.761336 1.423347e-01 10.030283
## Biomasa_H 175.900000 9.758063e+02 17.758896
## LD_H 5977.960000 3.509593e+05 9.910039
## Tinggi_15 107.720000 1.446625e+01 3.530869
## Total.Chlo_15 3.064819 5.225583e-01 23.586451
## LD_15 4529.865773 1.407191e+05 8.281161
## N 2.162400 4.408650e-02 9.709945
## Anakan.produktif 33.480000 3.647250e+01 18.038371
# Menambahkan KK pada ringkasan ANOVA
hasil_anova$KK_Persen <- hasil_KK$KK_Persen[
match(hasil_anova$Peubah, hasil_KK$Peubah)
]
cat("\n========================================\n")
##
## ========================================
cat("RINGKASAN ANOVA RAK DENGAN KK (%)\n")
## RINGKASAN ANOVA RAK DENGAN KK (%)
cat("========================================\n")
## ========================================
print(hasil_anova, row.names = FALSE)
## Peubah DB JK KT F_hitung P_value
## gabah.hampa 4 3.800481e+01 9.501203e+00 3.330130 3.644060e-02
## gabah.bernas 4 3.800481e+01 9.501203e+00 3.330130 3.644060e-02
## jumlah.gabah.bernas 4 4.017385e+03 1.004346e+03 3.179198 4.218758e-02
## bobot.1000butir 4 4.187953e+00 1.046988e+00 3.471411 3.183651e-02
## GKG 4 8.505138e+00 2.126284e+00 2.565365 7.833065e-02
## PFPN 4 3.386415e+03 8.466038e+02 20.577871 3.788826e-06
## Tinggi_H 4 1.012144e+02 2.530360e+01 2.547210 7.982290e-02
## Total.Chlo_H 4 3.651525e+00 9.128812e-01 6.413626 2.808708e-03
## Biomasa_H 4 6.950300e+03 1.737575e+03 1.780656 1.820866e-01
## LD_H 4 3.621449e+07 9.053622e+06 25.796788 8.359526e-07
## Tinggi_15 4 1.661400e+02 4.153500e+01 2.871166 5.728120e-02
## Total.Chlo_15 4 3.377766e+00 8.444415e-01 1.615976 2.187013e-01
## LD_15 4 6.772083e+06 1.693021e+06 12.031206 1.052179e-04
## N 4 4.259360e-01 1.064840e-01 2.415343 9.163839e-02
## Anakan.produktif 4 3.400400e+02 8.501000e+01 2.330797 1.002069e-01
## Keputusan KK_Persen
## NYATA 31.148528
## NYATA 1.785962
## NYATA 10.301327
## NYATA 2.121483
## TIDAK NYATA 11.046522
## NYATA 11.556931
## TIDAK NYATA 3.021050
## NYATA 10.030283
## TIDAK NYATA 17.758896
## NYATA 9.910039
## TIDAK NYATA 3.530869
## TIDAK NYATA 23.586451
## NYATA 8.281161
## TIDAK NYATA 9.709945
## TIDAK NYATA 18.038371
# Menyimpan hasil CSV
write.csv(
hasil_KK,
"Hasil_Koefisien_Keragaman.csv",
row.names = FALSE
)
write.csv(
hasil_anova,
"Hasil_ANOVA_dengan_KK.csv",
row.names = FALSE
)
cat("\nPerhitungan KK selesai.\n")
##
## Perhitungan KK selesai.
# ============================================================
# UJI LANJUT DMRT 5% - RANCANGAN ACAK KELOMPOK (RAK)
# Hanya untuk peubah dengan ANOVA perlakuan nyata
# ============================================================
# 1. Memanggil paket agricolae
if (!requireNamespace("agricolae", quietly = TRUE)) {
install.packages("agricolae")
}
library(agricolae)
## Warning: package 'agricolae' was built under R version 4.5.3
# 2. Menentukan peubah yang nyata berdasarkan ANOVA
peubah_nyata <- hasil_anova$Peubah[
!is.na(hasil_anova$P_value) &
hasil_anova$P_value < 0.05
]
cat("\n============================================\n")
##
## ============================================
cat("PEUBAH YANG DILANJUTKAN KE UJI DMRT 5%\n")
## PEUBAH YANG DILANJUTKAN KE UJI DMRT 5%
cat("============================================\n")
## ============================================
print(peubah_nyata)
## [1] "gabah.hampa" "gabah.bernas" "jumlah.gabah.bernas"
## [4] "bobot.1000butir" "PFPN" "Total.Chlo_H"
## [7] "LD_H" "LD_15"
# 3. Menyiapkan tabel hasil DMRT
hasil_dmrt <- data.frame()
# 4. Melakukan uji DMRT untuk setiap peubah nyata
for (v in peubah_nyata) {
cat("\n")
cat("================================================\n")
cat("UJI DMRT 5% - PEUBAH:", v, "\n")
cat("================================================\n")
# Menyiapkan data
d <- data[, c("Kelompok", "Perlakuan", v)]
names(d)[3] <- "Y"
d <- na.omit(d)
d$Kelompok <- droplevels(d$Kelompok)
d$Perlakuan <- factor(
d$Perlakuan,
levels = c("A0", "A1", "A2", "A3", "A4")
)
# Model ANOVA RAK
model <- aov(
Y ~ Kelompok + Perlakuan,
data = d
)
# Uji Duncan Multiple Range Test 5%
uji_dmrt <- agricolae::duncan.test(
model,
"Perlakuan",
alpha = 0.05,
group = TRUE,
console = FALSE
)
# Mengambil kelompok huruf DMRT
tabel <- as.data.frame(uji_dmrt$groups)
# Menyusun tabel
tabel_dmrt <- data.frame(
Peubah = v,
Perlakuan = rownames(tabel),
Rataan = tabel[[1]],
Kelompok_DMRT = tabel[[2]]
)
rownames(tabel_dmrt) <- NULL
# Menampilkan hasil
print(tabel_dmrt, row.names = FALSE)
# Menggabungkan seluruh hasil DMRT
hasil_dmrt <- rbind(
hasil_dmrt,
tabel_dmrt
)
}
##
## ================================================
## UJI DMRT 5% - PEUBAH: gabah.hampa
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## gabah.hampa A0 7.481719 a
## gabah.hampa A3 5.983488 ab
## gabah.hampa A2 5.253203 ab
## gabah.hampa A1 4.290034 b
## gabah.hampa A4 4.105407 b
##
## ================================================
## UJI DMRT 5% - PEUBAH: gabah.bernas
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## gabah.bernas A4 95.89459 a
## gabah.bernas A1 95.70997 a
## gabah.bernas A2 94.74680 ab
## gabah.bernas A3 94.01651 ab
## gabah.bernas A0 92.51828 b
##
## ================================================
## UJI DMRT 5% - PEUBAH: jumlah.gabah.bernas
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## jumlah.gabah.bernas A3 195.40 a
## jumlah.gabah.bernas A1 173.25 ab
## jumlah.gabah.bernas A4 170.35 b
## jumlah.gabah.bernas A2 166.70 b
## jumlah.gabah.bernas A0 157.00 b
##
## ================================================
## UJI DMRT 5% - PEUBAH: bobot.1000butir
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## bobot.1000butir A4 26.42876 a
## bobot.1000butir A1 26.22544 a
## bobot.1000butir A3 25.93036 ab
## bobot.1000butir A0 25.43643 b
## bobot.1000butir A2 25.41302 b
##
## ================================================
## UJI DMRT 5% - PEUBAH: PFPN
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## PFPN A4 67.81913 a
## PFPN A1 62.01667 ab
## PFPN A2 57.63370 b
## PFPN A3 56.38989 b
## PFPN A0 33.64341 c
##
## ================================================
## UJI DMRT 5% - PEUBAH: Total.Chlo_H
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## Total.Chlo_H A0 4.365570 a
## Total.Chlo_H A3 3.998144 ab
## Total.Chlo_H A2 3.661708 bc
## Total.Chlo_H A4 3.500979 bc
## Total.Chlo_H A1 3.280278 c
##
## ================================================
## UJI DMRT 5% - PEUBAH: LD_H
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## LD_H A4 7398.2 a
## LD_H A1 7386.8 a
## LD_H A0 5376.4 b
## LD_H A3 5310.4 b
## LD_H A2 4418.0 c
##
## ================================================
## UJI DMRT 5% - PEUBAH: LD_15
## ================================================
## Peubah Perlakuan Rataan Kelompok_DMRT
## LD_15 A4 5260.907 a
## LD_15 A0 4755.414 b
## LD_15 A3 4591.215 b
## LD_15 A1 4372.508 b
## LD_15 A2 3669.285 c
# 5. MENAMPILKAN SELURUH HASIL DMRT
# ============================================================
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("REKAPITULASI HASIL UJI DMRT 5% SELURUH PEUBAH\n")
## REKAPITULASI HASIL UJI DMRT 5% SELURUH PEUBAH
cat("====================================================\n")
## ====================================================
print(hasil_dmrt, row.names = FALSE)
## Peubah Perlakuan Rataan Kelompok_DMRT
## gabah.hampa A0 7.481719 a
## gabah.hampa A3 5.983488 ab
## gabah.hampa A2 5.253203 ab
## gabah.hampa A1 4.290034 b
## gabah.hampa A4 4.105407 b
## gabah.bernas A4 95.894593 a
## gabah.bernas A1 95.709966 a
## gabah.bernas A2 94.746797 ab
## gabah.bernas A3 94.016512 ab
## gabah.bernas A0 92.518281 b
## jumlah.gabah.bernas A3 195.400000 a
## jumlah.gabah.bernas A1 173.250000 ab
## jumlah.gabah.bernas A4 170.350000 b
## jumlah.gabah.bernas A2 166.700000 b
## jumlah.gabah.bernas A0 157.000000 b
## bobot.1000butir A4 26.428762 a
## bobot.1000butir A1 26.225439 a
## bobot.1000butir A3 25.930355 ab
## bobot.1000butir A0 25.436433 b
## bobot.1000butir A2 25.413023 b
## PFPN A4 67.819131 a
## PFPN A1 62.016667 ab
## PFPN A2 57.633695 b
## PFPN A3 56.389895 b
## PFPN A0 33.643411 c
## Total.Chlo_H A0 4.365570 a
## Total.Chlo_H A3 3.998144 ab
## Total.Chlo_H A2 3.661708 bc
## Total.Chlo_H A4 3.500979 bc
## Total.Chlo_H A1 3.280278 c
## LD_H A4 7398.200000 a
## LD_H A1 7386.800000 a
## LD_H A0 5376.400000 b
## LD_H A3 5310.400000 b
## LD_H A2 4418.000000 c
## LD_15 A4 5260.906742 a
## LD_15 A0 4755.413936 b
## LD_15 A3 4591.215000 b
## LD_15 A1 4372.507686 b
## LD_15 A2 3669.285500 c
# 6. MENYIMPAN HASIL DMRT KE EXCEL
# ============================================================
if (!requireNamespace("openxlsx", quietly = TRUE)) {
install.packages("openxlsx")
}
library(openxlsx)
## Warning: package 'openxlsx' was built under R version 4.5.3
write.xlsx(
hasil_dmrt,
file = "Hasil_DMRT_5_Persen.xlsx",
rowNames = FALSE,
overwrite = TRUE
)
# 7. MENYIMPAN HASIL DMRT KE CSV
# ============================================================
write.csv(
hasil_dmrt,
file = "Hasil_DMRT_5_Persen.csv",
row.names = FALSE
)
# 8. SELESAI
# ============================================================
cat("\n============================================\n")
##
## ============================================
cat("UJI DMRT 5% SELESAI\n")
## UJI DMRT 5% SELESAI
cat("Jumlah peubah diuji:", length(peubah_nyata), "\n")
## Jumlah peubah diuji: 8
cat("Hasil tersimpan dalam Excel dan CSV.\n")
## Hasil tersimpan dalam Excel dan CSV.
cat("============================================\n")
## ============================================
# ============================================================
# UJI LANJUT TUKEY HSD 5%
# RANCANGAN ACAK KELOMPOK (RAK)
# PENELITIAN PEMUPUKAN PADI INPARI 32
# ============================================================
# 1. MEMANGGIL PAKET
# ------------------------------------------------------------
if (!requireNamespace("agricolae", quietly = TRUE)) {
install.packages("agricolae")
}
if (!requireNamespace("openxlsx", quietly = TRUE)) {
install.packages("openxlsx")
}
library(agricolae)
library(openxlsx)
# 2. MENENTUKAN PEUBAH YANG NYATA DARI ANOVA
# ------------------------------------------------------------
peubah_nyata <- hasil_anova$Peubah[
!is.na(hasil_anova$P_value) &
hasil_anova$P_value < 0.05
]
cat("\n============================================\n")
##
## ============================================
cat("DAFTAR PEUBAH YANG DILANJUTKAN KE TUKEY HSD\n")
## DAFTAR PEUBAH YANG DILANJUTKAN KE TUKEY HSD
cat("============================================\n")
## ============================================
print(peubah_nyata)
## [1] "gabah.hampa" "gabah.bernas" "jumlah.gabah.bernas"
## [4] "bobot.1000butir" "PFPN" "Total.Chlo_H"
## [7] "LD_H" "LD_15"
# 3. MENYIAPKAN TABEL HASIL
# ------------------------------------------------------------
hasil_tukey <- data.frame()
hasil_perbandingan <- data.frame()
# 4. ANALISIS TUKEY HSD UNTUK SETIAP PEUBAH
# ------------------------------------------------------------
for (v in peubah_nyata) {
cat("\n\n")
cat("================================================\n")
cat("PEUBAH:", v, "\n")
cat("================================================\n")
# A. MENYIAPKAN DATA
d <- data[, c("Kelompok", "Perlakuan", v)]
names(d)[3] <- "Y"
d <- na.omit(d)
d$Kelompok <- droplevels(d$Kelompok)
d$Perlakuan <- factor(
d$Perlakuan,
levels = c("A0", "A1", "A2", "A3", "A4")
)
# B. MODEL ANOVA RAK
model <- aov(
Y ~ Kelompok + Perlakuan,
data = d
)
# C. UJI TUKEY HSD 5% MENGGUNAKAN AGRICOLAE
uji_tukey <- agricolae::HSD.test(
model,
"Perlakuan",
alpha = 0.05,
group = TRUE,
console = FALSE
)
# D. MENGAMBIL HASIL PENGELOMPOKAN HURUF
tabel <- as.data.frame(uji_tukey$groups)
tabel$Perlakuan <- rownames(tabel)
rownames(tabel) <- NULL
tabel_tukey <- data.frame(
Peubah = v,
Perlakuan = tabel$Perlakuan,
Rataan = tabel[[1]],
Kelompok_Tukey = tabel[[2]]
)
cat("\nHASIL PENGELOMPOKAN TUKEY HSD 5%:\n")
print(tabel_tukey, row.names = FALSE)
# E. MENGGABUNGKAN HASIL PENGELOMPOKAN
hasil_tukey <- rbind(
hasil_tukey,
tabel_tukey
)
# F. UJI TUKEY HSD MENGGUNAKAN FUNGSI BAWAAN R
uji_tukey_pair <- stats::TukeyHSD(
model,
which = "Perlakuan",
conf.level = 0.95
)
# G. MENGAMBIL HASIL PERBANDINGAN PASANGAN
perbandingan <- as.data.frame(
uji_tukey_pair$Perlakuan
)
perbandingan$Perbandingan <- rownames(perbandingan)
rownames(perbandingan) <- NULL
perbandingan$Peubah <- v
perbandingan <- perbandingan[, c(
"Peubah",
"Perbandingan",
"diff",
"lwr",
"upr",
"p adj"
)]
names(perbandingan) <- c(
"Peubah",
"Perbandingan",
"Selisih_Rataan",
"Batas_Bawah",
"Batas_Atas",
"P_Adjusted"
)
# H. MENAMPILKAN HASIL PERBANDINGAN
cat("\nHASIL PERBANDINGAN PASANGAN TUKEY HSD:\n")
print(perbandingan, row.names = FALSE)
# I. MENGGABUNGKAN HASIL PERBANDINGAN
hasil_perbandingan <- rbind(
hasil_perbandingan,
perbandingan
)
}
##
##
## ================================================
## PEUBAH: gabah.hampa
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## gabah.hampa A0 7.481719 a
## gabah.hampa A3 5.983488 ab
## gabah.hampa A2 5.253203 ab
## gabah.hampa A1 4.290034 ab
## gabah.hampa A4 4.105407 b
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## gabah.hampa A1-A0 -3.1916846 -6.464572 0.08120329 0.05775690
## gabah.hampa A2-A0 -2.2285160 -5.501404 1.04437185 0.27282798
## gabah.hampa A3-A0 -1.4982312 -4.771119 1.77465667 0.63485711
## gabah.hampa A4-A0 -3.3763115 -6.649199 -0.10342359 0.04153123
## gabah.hampa A2-A1 0.9631686 -2.309719 4.23605644 0.89212804
## gabah.hampa A3-A1 1.6934534 -1.579434 4.96634127 0.52669979
## gabah.hampa A4-A1 -0.1846269 -3.457515 3.08826101 0.99977653
## gabah.hampa A3-A2 0.7302848 -2.542603 4.00317271 0.95720477
## gabah.hampa A4-A2 -1.1477954 -4.420683 2.12509245 0.81675181
## gabah.hampa A4-A3 -1.8780803 -5.150968 1.39480762 0.42927911
##
##
## ================================================
## PEUBAH: gabah.bernas
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## gabah.bernas A4 95.89459 a
## gabah.bernas A1 95.70997 ab
## gabah.bernas A2 94.74680 ab
## gabah.bernas A3 94.01651 ab
## gabah.bernas A0 92.51828 b
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## gabah.bernas A1-A0 3.1916846 -0.08120329 6.464572 0.05775690
## gabah.bernas A2-A0 2.2285160 -1.04437185 5.501404 0.27282798
## gabah.bernas A3-A0 1.4982312 -1.77465667 4.771119 0.63485711
## gabah.bernas A4-A0 3.3763115 0.10342359 6.649199 0.04153123
## gabah.bernas A2-A1 -0.9631686 -4.23605644 2.309719 0.89212804
## gabah.bernas A3-A1 -1.6934534 -4.96634127 1.579434 0.52669979
## gabah.bernas A4-A1 0.1846269 -3.08826101 3.457515 0.99977653
## gabah.bernas A3-A2 -0.7302848 -4.00317271 2.542603 0.95720477
## gabah.bernas A4-A2 1.1477954 -2.12509245 4.420683 0.81675181
## gabah.bernas A4-A3 1.8780803 -1.39480762 5.150968 0.42927911
##
##
## ================================================
## PEUBAH: jumlah.gabah.bernas
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## jumlah.gabah.bernas A3 195.40 a
## jumlah.gabah.bernas A1 173.25 ab
## jumlah.gabah.bernas A4 170.35 ab
## jumlah.gabah.bernas A2 166.70 ab
## jumlah.gabah.bernas A0 157.00 b
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas
## jumlah.gabah.bernas A1-A0 16.25 -18.189384 50.689384
## jumlah.gabah.bernas A2-A0 9.70 -24.739384 44.139384
## jumlah.gabah.bernas A3-A0 38.40 3.960616 72.839384
## jumlah.gabah.bernas A4-A0 13.35 -21.089384 47.789384
## jumlah.gabah.bernas A2-A1 -6.55 -40.989384 27.889384
## jumlah.gabah.bernas A3-A1 22.15 -12.289384 56.589384
## jumlah.gabah.bernas A4-A1 -2.90 -37.339384 31.539384
## jumlah.gabah.bernas A3-A2 28.70 -5.739384 63.139384
## jumlah.gabah.bernas A4-A2 3.65 -30.789384 38.089384
## jumlah.gabah.bernas A4-A3 -25.05 -59.489384 9.389384
## P_Adjusted
## 0.60923806
## 0.90623453
## 0.02523809
## 0.75817414
## 0.97577316
## 0.32295202
## 0.99891381
## 0.12747736
## 0.99733455
## 0.21908456
##
##
## ================================================
## PEUBAH: bobot.1000butir
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## bobot.1000butir A4 26.42876 a
## bobot.1000butir A1 26.22544 a
## bobot.1000butir A3 25.93036 a
## bobot.1000butir A0 25.43643 a
## bobot.1000butir A2 25.41302 a
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## bobot.1000butir A1-A0 0.78900543 -0.27511382 1.8531247 0.20449272
## bobot.1000butir A2-A0 -0.02341008 -1.08752932 1.0407092 0.99999475
## bobot.1000butir A3-A0 0.49392171 -0.57019754 1.5580409 0.62323452
## bobot.1000butir A4-A0 0.99232868 -0.07179056 2.0564479 0.07374986
## bobot.1000butir A2-A1 -0.81241550 -1.87653475 0.2517037 0.18326726
## bobot.1000butir A3-A1 -0.29508372 -1.35920296 0.7690355 0.91084118
## bobot.1000butir A4-A1 0.20332326 -0.86079599 1.2674425 0.97536336
## bobot.1000butir A3-A2 0.51733178 -0.54678746 1.5814510 0.58315924
## bobot.1000butir A4-A2 1.01573876 -0.04838048 2.0798580 0.06505239
## bobot.1000butir A4-A3 0.49840698 -0.56571226 1.5625262 0.61555800
##
##
## ================================================
## PEUBAH: PFPN
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## PFPN A4 67.81913 a
## PFPN A1 62.01667 a
## PFPN A2 57.63370 a
## PFPN A3 56.38989 a
## PFPN A0 33.64341 b
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## PFPN A1-A0 28.373257 15.9449409 40.801572 2.625953e-05
## PFPN A2-A0 23.990285 11.5619689 36.418600 1.845706e-04
## PFPN A3-A0 22.746484 10.3181683 35.174800 3.294514e-04
## PFPN A4-A0 34.175720 21.7474046 46.604036 2.491979e-06
## PFPN A2-A1 -4.382972 -16.8112876 8.045344 8.138010e-01
## PFPN A3-A1 -5.626773 -18.0550882 6.801543 6.439920e-01
## PFPN A4-A1 5.802464 -6.6258519 18.230779 6.182916e-01
## PFPN A3-A2 -1.243801 -13.6721163 11.184515 9.978668e-01
## PFPN A4-A2 10.185436 -2.2428800 22.613751 1.371879e-01
## PFPN A4-A3 11.429236 -0.9990794 23.857552 7.934184e-02
##
##
## ================================================
## PEUBAH: Total.Chlo_H
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## Total.Chlo_H A0 4.365570 a
## Total.Chlo_H A3 3.998144 ab
## Total.Chlo_H A2 3.661708 ab
## Total.Chlo_H A4 3.500979 b
## Total.Chlo_H A1 3.280278 b
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## Total.Chlo_H A1-A0 -1.0852919 -1.81630930 -0.35427448 0.002619392
## Total.Chlo_H A2-A0 -0.7038616 -1.43487906 0.02715576 0.062017693
## Total.Chlo_H A3-A0 -0.3674259 -1.09844326 0.36359156 0.553295764
## Total.Chlo_H A4-A0 -0.8645911 -1.59560848 -0.13357366 0.016733641
## Total.Chlo_H A2-A1 0.3814302 -0.34958717 1.11244765 0.518921501
## Total.Chlo_H A3-A1 0.7178660 -0.01315137 1.44888345 0.055519652
## Total.Chlo_H A4-A1 0.2207008 -0.51031659 0.95171823 0.883099169
## Total.Chlo_H A3-A2 0.3364358 -0.39458161 1.06745321 0.630386700
## Total.Chlo_H A4-A2 -0.1607294 -0.89174683 0.57028799 0.959353906
## Total.Chlo_H A4-A3 -0.4971652 -1.22818262 0.23385219 0.273832435
##
##
## ================================================
## PEUBAH: LD_H
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## LD_H A4 7398.2 a
## LD_H A1 7386.8 a
## LD_H A0 5376.4 b
## LD_H A3 5310.4 b
## LD_H A2 4418.0 b
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## LD_H A1-A0 2010.4 862.5087 3158.2913 5.238520e-04
## LD_H A2-A0 -958.4 -2106.2913 189.4913 1.264093e-01
## LD_H A3-A0 -66.0 -1213.8913 1081.8913 9.997590e-01
## LD_H A4-A0 2021.8 873.9087 3169.6913 4.939522e-04
## LD_H A2-A1 -2968.8 -4116.6913 -1820.9087 5.523644e-06
## LD_H A3-A1 -2076.4 -3224.2913 -928.5087 3.732743e-04
## LD_H A4-A1 11.4 -1136.4913 1159.2913 9.999998e-01
## LD_H A3-A2 892.4 -255.4913 2040.2913 1.707440e-01
## LD_H A4-A2 2980.2 1832.3087 4128.0913 5.258396e-06
## LD_H A4-A3 2087.8 939.9087 3235.6913 3.521704e-04
##
##
## ================================================
## PEUBAH: LD_15
## ================================================
##
## HASIL PENGELOMPOKAN TUKEY HSD 5%:
## Peubah Perlakuan Rataan Kelompok_Tukey
## LD_15 A4 5260.907 a
## LD_15 A0 4755.414 ab
## LD_15 A3 4591.215 ab
## LD_15 A1 4372.508 bc
## LD_15 A2 3669.285 c
##
## HASIL PERBANDINGAN PASANGAN TUKEY HSD:
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas P_Adjusted
## LD_15 A1-A0 -382.9063 -1109.76320 343.95070 5.100137e-01
## LD_15 A2-A0 -1086.1284 -1812.98538 -359.27149 2.469181e-03
## LD_15 A3-A0 -164.1989 -891.05588 562.65801 9.553226e-01
## LD_15 A4-A0 505.4928 -221.36414 1232.34975 2.550561e-01
## LD_15 A2-A1 -703.2222 -1430.07913 23.63476 6.038509e-02
## LD_15 A3-A1 218.7073 -508.14963 945.56426 8.843231e-01
## LD_15 A4-A1 888.3991 161.54211 1615.25600 1.314166e-02
## LD_15 A3-A2 921.9295 195.07255 1648.78645 9.901384e-03
## LD_15 A4-A2 1591.6212 864.76429 2318.47819 4.334315e-05
## LD_15 A4-A3 669.6917 -57.16521 1396.54869 7.856697e-02
# 5. MENAMPILKAN REKAPITULASI TUKEY HSD
# ------------------------------------------------------------
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("REKAPITULASI KELOMPOK TUKEY HSD 5%\n")
## REKAPITULASI KELOMPOK TUKEY HSD 5%
cat("====================================================\n")
## ====================================================
print(hasil_tukey, row.names = FALSE)
## Peubah Perlakuan Rataan Kelompok_Tukey
## gabah.hampa A0 7.481719 a
## gabah.hampa A3 5.983488 ab
## gabah.hampa A2 5.253203 ab
## gabah.hampa A1 4.290034 ab
## gabah.hampa A4 4.105407 b
## gabah.bernas A4 95.894593 a
## gabah.bernas A1 95.709966 ab
## gabah.bernas A2 94.746797 ab
## gabah.bernas A3 94.016512 ab
## gabah.bernas A0 92.518281 b
## jumlah.gabah.bernas A3 195.400000 a
## jumlah.gabah.bernas A1 173.250000 ab
## jumlah.gabah.bernas A4 170.350000 ab
## jumlah.gabah.bernas A2 166.700000 ab
## jumlah.gabah.bernas A0 157.000000 b
## bobot.1000butir A4 26.428762 a
## bobot.1000butir A1 26.225439 a
## bobot.1000butir A3 25.930355 a
## bobot.1000butir A0 25.436433 a
## bobot.1000butir A2 25.413023 a
## PFPN A4 67.819131 a
## PFPN A1 62.016667 a
## PFPN A2 57.633695 a
## PFPN A3 56.389895 a
## PFPN A0 33.643411 b
## Total.Chlo_H A0 4.365570 a
## Total.Chlo_H A3 3.998144 ab
## Total.Chlo_H A2 3.661708 ab
## Total.Chlo_H A4 3.500979 b
## Total.Chlo_H A1 3.280278 b
## LD_H A4 7398.200000 a
## LD_H A1 7386.800000 a
## LD_H A0 5376.400000 b
## LD_H A3 5310.400000 b
## LD_H A2 4418.000000 b
## LD_15 A4 5260.906742 a
## LD_15 A0 4755.413936 ab
## LD_15 A3 4591.215000 ab
## LD_15 A1 4372.507686 bc
## LD_15 A2 3669.285500 c
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("REKAPITULASI PERBANDINGAN PASANGAN TUKEY HSD\n")
## REKAPITULASI PERBANDINGAN PASANGAN TUKEY HSD
cat("====================================================\n")
## ====================================================
print(hasil_perbandingan, row.names = FALSE)
## Peubah Perbandingan Selisih_Rataan Batas_Bawah Batas_Atas
## gabah.hampa A1-A0 -3.191685e+00 -6.464572e+00 8.120329e-02
## gabah.hampa A2-A0 -2.228516e+00 -5.501404e+00 1.044372e+00
## gabah.hampa A3-A0 -1.498231e+00 -4.771119e+00 1.774657e+00
## gabah.hampa A4-A0 -3.376311e+00 -6.649199e+00 -1.034236e-01
## gabah.hampa A2-A1 9.631686e-01 -2.309719e+00 4.236056e+00
## gabah.hampa A3-A1 1.693453e+00 -1.579434e+00 4.966341e+00
## gabah.hampa A4-A1 -1.846269e-01 -3.457515e+00 3.088261e+00
## gabah.hampa A3-A2 7.302848e-01 -2.542603e+00 4.003173e+00
## gabah.hampa A4-A2 -1.147795e+00 -4.420683e+00 2.125092e+00
## gabah.hampa A4-A3 -1.878080e+00 -5.150968e+00 1.394808e+00
## gabah.bernas A1-A0 3.191685e+00 -8.120329e-02 6.464572e+00
## gabah.bernas A2-A0 2.228516e+00 -1.044372e+00 5.501404e+00
## gabah.bernas A3-A0 1.498231e+00 -1.774657e+00 4.771119e+00
## gabah.bernas A4-A0 3.376311e+00 1.034236e-01 6.649199e+00
## gabah.bernas A2-A1 -9.631686e-01 -4.236056e+00 2.309719e+00
## gabah.bernas A3-A1 -1.693453e+00 -4.966341e+00 1.579434e+00
## gabah.bernas A4-A1 1.846269e-01 -3.088261e+00 3.457515e+00
## gabah.bernas A3-A2 -7.302848e-01 -4.003173e+00 2.542603e+00
## gabah.bernas A4-A2 1.147795e+00 -2.125092e+00 4.420683e+00
## gabah.bernas A4-A3 1.878080e+00 -1.394808e+00 5.150968e+00
## jumlah.gabah.bernas A1-A0 1.625000e+01 -1.818938e+01 5.068938e+01
## jumlah.gabah.bernas A2-A0 9.700000e+00 -2.473938e+01 4.413938e+01
## jumlah.gabah.bernas A3-A0 3.840000e+01 3.960616e+00 7.283938e+01
## jumlah.gabah.bernas A4-A0 1.335000e+01 -2.108938e+01 4.778938e+01
## jumlah.gabah.bernas A2-A1 -6.550000e+00 -4.098938e+01 2.788938e+01
## jumlah.gabah.bernas A3-A1 2.215000e+01 -1.228938e+01 5.658938e+01
## jumlah.gabah.bernas A4-A1 -2.900000e+00 -3.733938e+01 3.153938e+01
## jumlah.gabah.bernas A3-A2 2.870000e+01 -5.739384e+00 6.313938e+01
## jumlah.gabah.bernas A4-A2 3.650000e+00 -3.078938e+01 3.808938e+01
## jumlah.gabah.bernas A4-A3 -2.505000e+01 -5.948938e+01 9.389384e+00
## bobot.1000butir A1-A0 7.890054e-01 -2.751138e-01 1.853125e+00
## bobot.1000butir A2-A0 -2.341008e-02 -1.087529e+00 1.040709e+00
## bobot.1000butir A3-A0 4.939217e-01 -5.701975e-01 1.558041e+00
## bobot.1000butir A4-A0 9.923287e-01 -7.179056e-02 2.056448e+00
## bobot.1000butir A2-A1 -8.124155e-01 -1.876535e+00 2.517037e-01
## bobot.1000butir A3-A1 -2.950837e-01 -1.359203e+00 7.690355e-01
## bobot.1000butir A4-A1 2.033233e-01 -8.607960e-01 1.267442e+00
## bobot.1000butir A3-A2 5.173318e-01 -5.467875e-01 1.581451e+00
## bobot.1000butir A4-A2 1.015739e+00 -4.838048e-02 2.079858e+00
## bobot.1000butir A4-A3 4.984070e-01 -5.657123e-01 1.562526e+00
## PFPN A1-A0 2.837326e+01 1.594494e+01 4.080157e+01
## PFPN A2-A0 2.399028e+01 1.156197e+01 3.641860e+01
## PFPN A3-A0 2.274648e+01 1.031817e+01 3.517480e+01
## PFPN A4-A0 3.417572e+01 2.174740e+01 4.660404e+01
## PFPN A2-A1 -4.382972e+00 -1.681129e+01 8.045344e+00
## PFPN A3-A1 -5.626773e+00 -1.805509e+01 6.801543e+00
## PFPN A4-A1 5.802464e+00 -6.625852e+00 1.823078e+01
## PFPN A3-A2 -1.243801e+00 -1.367212e+01 1.118452e+01
## PFPN A4-A2 1.018544e+01 -2.242880e+00 2.261375e+01
## PFPN A4-A3 1.142924e+01 -9.990794e-01 2.385755e+01
## Total.Chlo_H A1-A0 -1.085292e+00 -1.816309e+00 -3.542745e-01
## Total.Chlo_H A2-A0 -7.038616e-01 -1.434879e+00 2.715576e-02
## Total.Chlo_H A3-A0 -3.674259e-01 -1.098443e+00 3.635916e-01
## Total.Chlo_H A4-A0 -8.645911e-01 -1.595608e+00 -1.335737e-01
## Total.Chlo_H A2-A1 3.814302e-01 -3.495872e-01 1.112448e+00
## Total.Chlo_H A3-A1 7.178660e-01 -1.315137e-02 1.448883e+00
## Total.Chlo_H A4-A1 2.207008e-01 -5.103166e-01 9.517182e-01
## Total.Chlo_H A3-A2 3.364358e-01 -3.945816e-01 1.067453e+00
## Total.Chlo_H A4-A2 -1.607294e-01 -8.917468e-01 5.702880e-01
## Total.Chlo_H A4-A3 -4.971652e-01 -1.228183e+00 2.338522e-01
## LD_H A1-A0 2.010400e+03 8.625087e+02 3.158291e+03
## LD_H A2-A0 -9.584000e+02 -2.106291e+03 1.894913e+02
## LD_H A3-A0 -6.600000e+01 -1.213891e+03 1.081891e+03
## LD_H A4-A0 2.021800e+03 8.739087e+02 3.169691e+03
## LD_H A2-A1 -2.968800e+03 -4.116691e+03 -1.820909e+03
## LD_H A3-A1 -2.076400e+03 -3.224291e+03 -9.285087e+02
## LD_H A4-A1 1.140000e+01 -1.136491e+03 1.159291e+03
## LD_H A3-A2 8.924000e+02 -2.554913e+02 2.040291e+03
## LD_H A4-A2 2.980200e+03 1.832309e+03 4.128091e+03
## LD_H A4-A3 2.087800e+03 9.399087e+02 3.235691e+03
## LD_15 A1-A0 -3.829063e+02 -1.109763e+03 3.439507e+02
## LD_15 A2-A0 -1.086128e+03 -1.812985e+03 -3.592715e+02
## LD_15 A3-A0 -1.641989e+02 -8.910559e+02 5.626580e+02
## LD_15 A4-A0 5.054928e+02 -2.213641e+02 1.232350e+03
## LD_15 A2-A1 -7.032222e+02 -1.430079e+03 2.363476e+01
## LD_15 A3-A1 2.187073e+02 -5.081496e+02 9.455643e+02
## LD_15 A4-A1 8.883991e+02 1.615421e+02 1.615256e+03
## LD_15 A3-A2 9.219295e+02 1.950726e+02 1.648786e+03
## LD_15 A4-A2 1.591621e+03 8.647643e+02 2.318478e+03
## LD_15 A4-A3 6.696917e+02 -5.716521e+01 1.396549e+03
## P_Adjusted
## 5.775690e-02
## 2.728280e-01
## 6.348571e-01
## 4.153123e-02
## 8.921280e-01
## 5.266998e-01
## 9.997765e-01
## 9.572048e-01
## 8.167518e-01
## 4.292791e-01
## 5.775690e-02
## 2.728280e-01
## 6.348571e-01
## 4.153123e-02
## 8.921280e-01
## 5.266998e-01
## 9.997765e-01
## 9.572048e-01
## 8.167518e-01
## 4.292791e-01
## 6.092381e-01
## 9.062345e-01
## 2.523809e-02
## 7.581741e-01
## 9.757732e-01
## 3.229520e-01
## 9.989138e-01
## 1.274774e-01
## 9.973346e-01
## 2.190846e-01
## 2.044927e-01
## 9.999948e-01
## 6.232345e-01
## 7.374986e-02
## 1.832673e-01
## 9.108412e-01
## 9.753634e-01
## 5.831592e-01
## 6.505239e-02
## 6.155580e-01
## 2.625953e-05
## 1.845706e-04
## 3.294514e-04
## 2.491979e-06
## 8.138010e-01
## 6.439920e-01
## 6.182916e-01
## 9.978668e-01
## 1.371879e-01
## 7.934184e-02
## 2.619392e-03
## 6.201769e-02
## 5.532958e-01
## 1.673364e-02
## 5.189215e-01
## 5.551965e-02
## 8.830992e-01
## 6.303867e-01
## 9.593539e-01
## 2.738324e-01
## 5.238520e-04
## 1.264093e-01
## 9.997590e-01
## 4.939522e-04
## 5.523644e-06
## 3.732743e-04
## 9.999998e-01
## 1.707440e-01
## 5.258396e-06
## 3.521704e-04
## 5.100137e-01
## 2.469181e-03
## 9.553226e-01
## 2.550561e-01
## 6.038509e-02
## 8.843231e-01
## 1.314166e-02
## 9.901384e-03
## 4.334315e-05
## 7.856697e-02
# 6. MENYIMPAN HASIL KE EXCEL
# ------------------------------------------------------------
daftar_hasil <- list(
Kelompok_Tukey = hasil_tukey,
Perbandingan_Tukey = hasil_perbandingan
)
openxlsx::write.xlsx(
daftar_hasil,
file = "Hasil_Tukey_HSD_5_Persen.xlsx",
overwrite = TRUE
)
# 7. MENYIMPAN HASIL KE CSV
# ------------------------------------------------------------
write.csv(
hasil_tukey,
file = "Hasil_Kelompok_Tukey.csv",
row.names = FALSE
)
write.csv(
hasil_perbandingan,
file = "Hasil_Perbandingan_Tukey.csv",
row.names = FALSE
)
# 8. SELESAI
# ------------------------------------------------------------
cat("\n\n")
cat("====================================================\n")
## ====================================================
cat("ANALISIS TUKEY HSD 5% SELESAI\n")
## ANALISIS TUKEY HSD 5% SELESAI
cat("====================================================\n")
## ====================================================
cat("Jumlah peubah yang diuji:",
length(peubah_nyata), "\n")
## Jumlah peubah yang diuji: 8
cat("Jumlah hasil pengelompokan:",
nrow(hasil_tukey), "\n")
## Jumlah hasil pengelompokan: 40
cat("Jumlah perbandingan pasangan:",
nrow(hasil_perbandingan), "\n")
## Jumlah perbandingan pasangan: 80
cat("\nFile Excel dan CSV tersimpan di Working Directory.\n")
##
## File Excel dan CSV tersimpan di Working Directory.
cat("====================================================\n")
## ====================================================