ahpdata <- read.csv("Data/ahp.csv")
ahpdata Responden SAL_QL SAL_IW SAL_LC QL_IW QL_LC IW_LC J1_J2 J1_j3 J2_J3 J1_J2.1
1 1 -5 -2 -4 2 2 -2 -2 -4 -2 2
2 2 -7 -3 -3 3 3 -4 -3 -7 -1 3
J1_j3.1 J2_J3.1 J1_J2.2 J1_j3.2 J2_J3.2 J1_J2.3 J1_j3.3 J2_J3.3
1 3 3 7 3 -3 4 7 -2
2 3 3 4 -1 -3 5 2 -4# Mendefinisikan faktor
faktor <- c("SAL", "QL", "IW", "LC")
# Menampilkan data frame
faktor_data <- ahpdata[, 2:7]
faktor_data SAL_QL SAL_IW SAL_LC QL_IW QL_LC IW_LC
1 -5 -2 -4 2 2 -2
2 -7 -3 -3 3 3 -4# install.packages("ahpsurvey")
library(ahpsurvey)
faktor_data_mat <- ahp.mat(df = faktor_data, faktor,
negconvert = TRUE)
faktor_data_mat[[1]]
SAL QL IW LC
SAL 1.00 5 2.0 4.0
QL 0.20 1 0.5 0.5
IW 0.50 2 1.0 2.0
LC 0.25 2 0.5 1.0
[[2]]
SAL QL IW LC
SAL 1.0000000 7 3.0000000 3.0000000
QL 0.1428571 1 0.3333333 0.3333333
IW 0.3333333 3 1.0000000 4.0000000
LC 0.3333333 3 0.2500000 1.0000000# Consistency
ri <- ahp.ri(nsims = 10000, dim = 4, seed = 42)
ahp.cr(faktor_data_mat, faktor, ri)[1] 0.01780548 0.09677931#Treatement Consistency (Jika Tidak Konsisten)
#faktor_data_mat <- ahp.harker(faktor_data_mat, faktor, iterations = 10, stopcr = 0.1)
#ahp.cr(faktor_data_mat, faktor)The ahp.cr function calculates the consistency ratio of each decision-maker, defined by the following equation:
CR = (λ − n)/((n − 1)(RI))
Where λ is the maximum eigenvalue of the pairwise comparison matrix, n is the number of attributes, and RI is the random index. Following Saaty and Tran (2007), the RI is a function of n and is the consistency ratio of randomly generated pairwise comparison matrices.
Saaty showed that when the CR is higher than 0.1, the choice is deemed to be inconsistent
library(tidyverse)
library(tibble)
faktor_ind <- ahp.indpref(faktor_data_mat,
faktor,
method = "arithmetic")
round(faktor_ind, 3) %>% rownames_to_column('ID') ID SAL QL IW LC
1 1 0.512 0.099 0.243 0.147
2 2 0.517 0.066 0.274 0.143faktor_agg <- ahp.aggpref(faktor_data_mat,
faktor,
method = "arithmetic",
aggmethod = "arithmetic")
round(faktor_agg, 3) %>% t() SAL QL IW LC
[1,] 0.514 0.082 0.259 0.145barplot(faktor_agg,main="Rangking Faktor")
library(ggplot2)
# Mengubah Cat menjadi factor dengan label yang diinginkan
data = data.frame("Cat"=row.names(data.frame(faktor_agg)),
data.frame(faktor_agg))
data$Cat <- factor(data$Cat,
levels = c("SAL", "QL", "IW", "LC"),
labels = c("Salary", "Quality of Life",
"Interes in Work", "Location"))
# Mengurutkan
data$warna <- ifelse(data$faktor_agg ==
max(data$faktor_agg),
"terbesar", "lainnya")
# Buat grafik batang
ggplot(data, aes(x = Cat,
y = faktor_agg,
fill = warna)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("terbesar" = "#4682B4",
"lainnya" = "#A9A9A9")) +
theme_minimal() +
theme(legend.position = "none") + # Sembunyikan legenda
labs(
title = "AHP: Rangking Faktor",
y = "Skor",
x = "")
library(dplyr)
alternatif <- c("J1", "J2", "J3")
# Menampilkan data frame
alternatif_data1 <- ahpdata[,8:10]
alternatif1 <- ahp.mat(df = alternatif_data1,
atts = alternatif,
negconvert = TRUE)
alternatif1_agg <- ahp.aggpref(alternatif1,
alternatif,
method = "arithmetic",
aggmethod = "arithmetic")
round(alternatif1_agg, 3) %>% t() J1 J2 J3
[1,] 0.628 0.232 0.139#Consistency
ri <- ahp.ri(nsims = 10000, dim = 3, seed = 42)
ahp.cr(alternatif1, alternatif, ri)[1] 0.00000000 0.07669698alternatif_data2 <- ahpdata[,11:13]
alternatif2 <- ahp.mat(df = alternatif_data2,
atts = alternatif,
negconvert = TRUE)
alternatif2_agg <- ahp.aggpref(alternatif2,
alternatif,
method = "arithmetic",
aggmethod = "arithmetic")
round(alternatif2_agg, 3) %>% t() J1 J2 J3
[1,] 0.15 0.269 0.581#Consistency
ahp.cr(alternatif2, alternatif, ri)[1] 0.05121571 0.12952632alternatif_data3 <- ahpdata[,14:16]
alternatif3 <- ahp.mat(df = alternatif_data3,
atts = alternatif,
negconvert = TRUE)
alternatif3_agg <- ahp.aggpref(alternatif3,
alternatif,
method = "arithmetic",
aggmethod = "arithmetic")
round(alternatif3_agg, 3) %>% t() J1 J2 J3
[1,] 0.132 0.651 0.218#Consistency
ahp.cr(alternatif3, alternatif, ri)[1] 0.006706716 0.008789809alternatif_data4 <- ahpdata[,17:19]
alternatif4 <- ahp.mat(df = alternatif_data4,
atts = alternatif,
negconvert = TRUE)
alternatif4_agg <- ahp.aggpref(alternatif4,
alternatif,
method = "arithmetic",
aggmethod = "arithmetic")
round(alternatif4_agg, 3) %>% t() J1 J2 J3
[1,] 0.104 0.597 0.299#Consistency
ahp.cr(alternatif4, alternatif, ri)[1] 0.16898990 0.02349155alternatif_agg <- cbind(alternatif1_agg,alternatif2_agg,
alternatif3_agg,alternatif4_agg) %*% faktor_agg
alternatif_agg [,1]
J1 0.3844544
J2 0.3964920
J3 0.2190537barplot(t(alternatif_agg) ,main="Rangking Alternatif")
data = data.frame("Cat"=row.names(data.frame(alternatif_agg)),
data.frame(alternatif_agg))
data$Cat <- factor(data$Cat,
levels = c( "J1" , "J2" ,"J3"),
labels = c("Job1", "Job2","Job3"))
# Buat grafik batang
data$warna <- ifelse(data$alternatif_agg == max(data$alternatif_agg),
"terbesar", "lainnya")
# Buat grafik batang
ggplot(data, aes(x = Cat, y = alternatif_agg, fill = warna)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("terbesar" = "#4682B4",
"lainnya" = "#A9A9A9")) +
theme_minimal() +
theme(legend.position = "none") +
labs(
title = "AHP: Rangking Alternatif",
y = "Skor",
x = "")