A FUZZY TIME SERIES MARKOV CHAIN (FTSMC) MODEL WITH AN IMPLEMENTATION OF WEB APPLICATION TO FORECAST THE AVERAGE TEMPERATURE IN KOLAKA REGENCY, SOUTHEAST SULAWESI BASED ON DAILY DATA FROM THE METEOROLOGICAL, CLIMATOLOGICAL, AND GEOPHYSICAL AGENCY (BMKG) IN 2001-2024
Input Data
datauji <- read.csv("/cloud/project/avg_temp_sulteng.csv", header=T, sep =",")
head(datauji)data = datauji$tavg
n=length(data)
n[1] 8588Plot Data Aktual
## Plot Data Aktual
library(ggplot2)
library(dplyr)
library(lubridate)
# Pastikan kolom tanggal bertipe Date (Tanggal)
datauji$date <- as.Date(datauji$date, format = "%d-%m-%Y")
# Mengagregasi data berdasarkan bulan agar lebih mudah dibaca
monthly_data <- datauji %>%
mutate(month = floor_date(date, "month")) %>%
group_by(month) %>%
summarize(monthly_avg_temp = mean(tavg, na.rm = TRUE))
# Plot data time series menggunakan ggplot2
ggplot(monthly_data, aes(x = month, y = monthly_avg_temp)) +
geom_line(color = 'blue') +
geom_smooth(method = "loess", span = 0.2, color = 'red', se = FALSE) +
labs(title = "Time Series Plot of Average Monthly Temperature\nin Kolaka Regency Southeast Sulawesi from 2001-2024",
x = "Period",
y = "Average Temperature (°C)") +
theme_minimal() +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 14, hjust = 0.5, margin = margin(t = 20, b = 20))
)
Mencari Data Maksimum dan Minimum
minimum = min(data)
maximum = max(data)
minimum[1] 22.9maximum[1] 34.3Mencari Data Minimum Baru dan Data Maksimum Baru untuk Dijadikan sebagai Batas Bawah dan Batas Atas Interval Semesta Pembicaraan U
new.min = minimum-0.2
new.max = maximum+0.3
new.min[1] 22.7new.max[1] 34.6Menentukan Banyak Interval (N) dan Panjang Interval (L)
n = round(1 +(3.3 *logb(length(data), base = 10)))
n[1] 14L = (new.max - new.min)/n
L[1] 0.85Menentukan Batas-batas Interval
intrv.1 = seq(new.min,new.max,len = n+1)
intrv.1 [1] 22.70 23.55 24.40 25.25 26.10 26.95 27.80 28.65 29.50 30.35 31.20
[12] 32.05 32.90 33.75 34.60Pembagian Interval
box1 = data.frame(NA,nrow=length(intrv.1)-1,ncol=3)
names(box1) = c("bawah","atas","kel")
for (i in 1:length(intrv.1)-1) {
box1[i,1]=intrv.1[i]
box1[i,2]=intrv.1[i+1]
box1[i,3]=i
}
box1Menentukan Nilai Tengah Interval
n.tengah = data.frame(tengah=(box1[,1]+box1[,2])/2,kel=box1[,3])
n.tengahMenentukan Bilangan Fuzzy
fuzifikasi=c()
for (i in 1:length(data)){
for (j in 1:nrow(box1)){
if (i!=which.max(data)){
if (data[i]>=(box1[j,1])&data[i]<(box1[j,2])){
fuzifikasi[i]=j
break
}
}
else {
if (data[i]>=(box1[j,1])&data[i]<=(box1[j,2])){
fuzifikasi[i]=j
break
}
}
}
}
fuzifikasi [1] 7 6 6 6 6 6 7 7 7 8 6 7 7 7 7 6 7 7 6 7 7
[22] 6 6 4 4 5 7 7 7 6 6 5 6 6 7 7 6 8 8 8 7 6
[43] 7 8 7 8 8 8 8 6 9 8 6 6 7 6 7 7 8 9 6 8 6
[64] 7 7 6 6 6 6 7 8 9 6 6 7 7 7 6 6 6 6 7 7 6
[85] 8 6 5 6 5 6 6 6 7 6 7 6 7 7 6 5 5 5 7 6 6
[106] 5 6 6 5 6 8 5 7 6 6 5 6 6 5 5 6 7 7 6 6 8
[127] 6 7 6 7 8 7 7 7 7 6 8 6 6 8 8 7 8 6 5 7 7
[148] 7 5 5 5 6 7 6 6 6 4 6 4 5 5 5 6 6 5 3 5 5
[169] 4 5 5 3 5 7 6 6 6 7 7 5 6 5 4 5 3 3 7 7 7
[190] 7 6 6 6 8 8 7 7 5 6 5 7 7 5 4 6 6 5 5 6 6
[211] 5 6 5 5 5 5 6 6 6 7 6 6 5 3 4 7 6 6 6 6 6
[232] 7 5 5 6 6 6 6 7 7 8 7 7 5 6 7 6 8 7 7 6 5
[253] 6 7 7 6 6 7 7 7 8 7 7 6 5 5 5 7 6 7 7 5 4
[274] 5 7 6 7 7 7 7 6 6 7 8 8 7 9 7 10 9 6 7 7 9
[295] 8 6 8 8 9 9 9 7 7 8 6 3 7 7 8 7 7 7 7 6 5
[316] 6 7 5 6 6 6 7 8 7 6 5 6 8 7 7 8 6 6 7 8 7
[337] 7 8 6 7 6 7 7 7 7 5 7 6 7 7 7 5 7 8 7 7 7
[358] 8 8 6 6 6 6 7 7 5 6 7 8 8 7 7 7 7 6 7 7 7
[379] 5 6 5 5 6 7 6 6 5 6 7 5 5 5 5 6 4 6 6 6 7
[400] 6 6 7 7 6 6 7 6 7 6 5 5 6 6 7 7 7 7 6 9 7
[421] 8 6 7 6 7 7 5 7 8 8 8 7 6 6 5 5 7 7 5 6 7
[442] 7 7 7 7 6 7 6 7 7 6 7 6 6 7 6 7 6 5 6 7 7
[463] 6 6 7 8 7 7 6 5 6 6 8 7 7 7 7 6 7 6 5 7 6
[484] 7 6 5 5 6 8 6 7 6 5 5 5 5 6 5 7 8 7 7 7 8
[505] 7 8 7 7 8 7 7 7 8 7 8 7 8 7 6 8 4 5 7 7 4
[526] 6 5 5 4 6 6 7 4 5 3 3 6 5 6 5 6 6 5 6 5 6
[547] 6 5 3 4 6 6 5 6 7 7 7 7 7 7 7 7 6 6 7 6 6
[568] 6 7 7 6 6 6 7 7 8 7 7 7 7 7 7 7 6 7 5 8 5
[589] 3 5 5 6 6 5 6 6 9 10 6 7 7 7 6 6 5 5 5 5 7
[610] 7 7 7 7 6 6 7 7 8 7 7 7 8 7 7 8 9 8 8 8 7
[631] 7 6 5 5 9 7 7 7 5 8 8 9 8 7 6 7 7 8 6 8 9
[652] 8 9 9 9 7 7 8 8 7 8 8 8 8 8 8 9 8 8 8 8 9
[673] 9 8 8 8 8 8 9 9 8 9 7 9 7 7 9 9 9 8 7 7 8
[694] 10 8 7 7 7 7 8 7 7 7 6 7 8 8 9 8 7 5 7 6 7
[715] 5 8 8 7 7 6 10 10 8 7 7 8 8 7 7 7 8 4 7 8 7
[736] 8 7 8 7 8 9 8 9 8 8 9 8 7 6 6 8 7 7 5 7 7
[757] 5 5 6 6 7 6 7 7 7 5 8 8 6 5 6 5 6 8 7 7 6
[778] 6 6 6 8 7 7 8 7 7 8 7 8 7 6 8 8 6 6 7 7 7
[799] 6 7 7 7 6 6 5 6 6 6 7 7 7 6 4 7 8 7 6 6 4
[820] 5 7 7 7 7 7 8 8 7 6 6 7 6 7 8 7 7 6 7 8 9
[841] 6 7 8 7 8 7 5 4 6 5 6 5 5 7 6 6 8 6 4 6 7
[862] 6 7 5 6 7 7 7 7 8 7 8 8 7 8 6 8 7 7 7 8 7
[883] 9 9 9 7 6 7 7 8 5 7 5 6 6 5 7 7 7 5 5 5 6
[904] 5 5 5 5 6 7 5 5 6 5 5 6 7 6 3 3 3 3 5 6 7
[925] 5 5 5 6 4 5 6 4 3 5 3 4 5 6 6 7 7 4 5 6 6
[946] 4 6 5 6 8 6 7 7 6 7 5 7 5 6 6 6 6 7 5 5 6
[967] 4 6 6 7 6 5 6 7 9 7 7 7 7 6 6 8 6 5 7 6 6
[988] 6 5 6 6 6 6 6 7 8 8 6 8 8
[ reached getOption("max.print") -- omitted 7588 entries ]Fuzifikasi ke Data Asal
fuzzyfy = cbind(data,fuzifikasi)
fuzzyfy data fuzifikasi
[1,] 28.00 7
[2,] 27.70 6
[3,] 27.70 6
[4,] 27.70 6
[5,] 27.20 6
[6,] 27.10 6
[7,] 28.30 7
[8,] 28.50 7
[9,] 28.60 7
[10,] 28.70 8
[11,] 27.20 6
[12,] 28.50 7
[13,] 28.20 7
[14,] 28.60 7
[15,] 27.90 7
[16,] 27.00 6
[17,] 28.40 7
[18,] 28.50 7
[19,] 27.30 6
[20,] 27.80 7
[21,] 27.90 7
[22,] 27.20 6
[23,] 27.40 6
[24,] 26.00 4
[25,] 25.70 4
[26,] 26.60 5
[27,] 27.90 7
[28,] 27.90 7
[29,] 28.20 7
[30,] 27.00 6
[31,] 27.30 6
[32,] 26.60 5
[33,] 27.60 6
[34,] 27.20 6
[35,] 27.90 7
[36,] 28.60 7
[37,] 27.70 6
[38,] 28.80 8
[39,] 28.90 8
[40,] 29.40 8
[41,] 28.00 7
[42,] 27.00 6
[43,] 28.10 7
[44,] 28.80 8
[45,] 28.50 7
[46,] 29.00 8
[47,] 29.30 8
[48,] 29.30 8
[49,] 29.40 8
[50,] 27.70 6
[51,] 29.60 9
[52,] 28.90 8
[53,] 27.60 6
[54,] 27.30 6
[55,] 27.80 7
[56,] 27.00 6
[57,] 28.20 7
[58,] 28.20 7
[59,] 28.70 8
[60,] 29.70 9
[61,] 27.70 6
[62,] 29.00 8
[63,] 27.60 6
[64,] 28.40 7
[65,] 28.20 7
[66,] 27.30 6
[67,] 27.30 6
[68,] 27.20 6
[69,] 27.40 6
[70,] 28.50 7
[71,] 28.90 8
[72,] 29.60 9
[73,] 27.30 6
[74,] 27.00 6
[75,] 27.80 7
[76,] 27.90 7
[77,] 28.40 7
[78,] 27.50 6
[79,] 27.60 6
[80,] 27.50 6
[81,] 27.70 6
[82,] 28.00 7
[83,] 28.30 7
[84,] 27.50 6
[85,] 28.90 8
[86,] 27.10 6
[87,] 26.80 5
[88,] 27.10 6
[89,] 26.10 5
[90,] 27.70 6
[91,] 27.60 6
[92,] 27.50 6
[93,] 27.90 7
[94,] 27.40 6
[95,] 28.10 7
[96,] 27.60 6
[97,] 28.60 7
[98,] 27.80 7
[99,] 27.40 6
[100,] 26.80 5
[101,] 26.80 5
[102,] 26.80 5
[103,] 27.80 7
[104,] 27.50 6
[105,] 27.10 6
[106,] 26.80 5
[107,] 27.50 6
[108,] 27.60 6
[109,] 26.70 5
[110,] 27.60 6
[111,] 28.70 8
[112,] 26.70 5
[113,] 28.30 7
[114,] 27.20 6
[115,] 27.20 6
[116,] 26.60 5
[117,] 27.00 6
[118,] 27.40 6
[119,] 26.80 5
[120,] 26.90 5
[121,] 27.20 6
[122,] 28.10 7
[123,] 27.90 7
[124,] 27.70 6
[125,] 27.40 6
[126,] 29.00 8
[127,] 27.30 6
[128,] 28.50 7
[129,] 27.70 6
[130,] 28.20 7
[131,] 28.70 8
[132,] 28.10 7
[133,] 28.50 7
[134,] 27.80 7
[135,] 28.60 7
[136,] 27.50 6
[137,] 28.70 8
[138,] 27.00 6
[139,] 27.40 6
[140,] 29.20 8
[141,] 28.80 8
[142,] 28.40 7
[143,] 28.70 8
[144,] 27.10 6
[145,] 26.60 5
[146,] 28.10 7
[147,] 28.60 7
[148,] 28.20 7
[149,] 26.20 5
[150,] 26.40 5
[151,] 26.50 5
[152,] 27.30 6
[153,] 28.00 7
[154,] 27.20 6
[155,] 27.30 6
[156,] 27.20 6
[157,] 25.90 4
[158,] 27.00 6
[159,] 25.80 4
[160,] 26.40 5
[161,] 26.50 5
[162,] 26.50 5
[163,] 27.70 6
[164,] 27.40 6
[165,] 26.50 5
[166,] 24.70 3
[167,] 26.60 5
[168,] 26.80 5
[169,] 25.50 4
[170,] 26.70 5
[171,] 26.30 5
[172,] 24.50 3
[173,] 26.10 5
[174,] 28.10 7
[175,] 27.40 6
[176,] 27.70 6
[177,] 27.00 6
[178,] 28.20 7
[179,] 27.90 7
[180,] 26.60 5
[181,] 27.40 6
[182,] 26.20 5
[183,] 25.60 4
[184,] 26.40 5
[185,] 25.10 3
[186,] 24.70 3
[187,] 28.10 7
[188,] 28.10 7
[189,] 28.50 7
[190,] 27.90 7
[191,] 27.10 6
[192,] 27.50 6
[193,] 27.60 6
[194,] 28.80 8
[195,] 28.70 8
[196,] 28.00 7
[197,] 28.10 7
[198,] 26.90 5
[199,] 27.30 6
[200,] 26.60 5
[201,] 28.30 7
[202,] 28.30 7
[203,] 26.60 5
[204,] 25.30 4
[205,] 27.00 6
[206,] 27.50 6
[207,] 26.90 5
[208,] 26.20 5
[209,] 27.20 6
[210,] 27.30 6
[211,] 26.80 5
[212,] 27.40 6
[213,] 26.70 5
[214,] 26.60 5
[215,] 26.40 5
[216,] 26.30 5
[217,] 27.40 6
[218,] 27.50 6
[219,] 27.30 6
[220,] 28.00 7
[221,] 27.30 6
[222,] 27.50 6
[223,] 26.60 5
[224,] 25.10 3
[225,] 25.90 4
[226,] 27.80 7
[227,] 27.50 6
[228,] 27.10 6
[229,] 27.50 6
[230,] 27.40 6
[231,] 27.50 6
[232,] 27.80 7
[233,] 26.60 5
[234,] 26.90 5
[235,] 27.40 6
[236,] 27.50 6
[237,] 27.60 6
[238,] 27.60 6
[239,] 27.90 7
[240,] 27.80 7
[241,] 29.40 8
[242,] 28.60 7
[243,] 28.00 7
[244,] 26.70 5
[245,] 27.70 6
[246,] 28.20 7
[247,] 27.40 6
[248,] 29.00 8
[249,] 28.00 7
[250,] 28.20 7
[251,] 27.70 6
[252,] 26.30 5
[253,] 27.00 6
[254,] 27.80 7
[255,] 27.80 7
[256,] 27.70 6
[257,] 27.30 6
[258,] 27.80 7
[259,] 28.20 7
[260,] 28.40 7
[261,] 28.90 8
[262,] 28.30 7
[263,] 27.90 7
[264,] 27.30 6
[265,] 26.60 5
[266,] 26.40 5
[267,] 26.80 5
[268,] 28.20 7
[269,] 27.60 6
[270,] 27.90 7
[271,] 27.80 7
[272,] 26.70 5
[273,] 25.40 4
[274,] 26.70 5
[275,] 28.00 7
[276,] 27.40 6
[277,] 27.80 7
[278,] 28.40 7
[279,] 27.90 7
[280,] 27.80 7
[281,] 27.20 6
[282,] 27.30 6
[283,] 28.20 7
[284,] 29.10 8
[285,] 28.90 8
[286,] 28.00 7
[287,] 29.60 9
[288,] 28.40 7
[289,] 30.50 10
[290,] 29.50 9
[291,] 27.40 6
[292,] 28.00 7
[293,] 28.50 7
[294,] 29.50 9
[295,] 29.40 8
[296,] 27.70 6
[297,] 28.90 8
[298,] 28.70 8
[299,] 30.10 9
[300,] 30.20 9
[301,] 30.00 9
[302,] 28.50 7
[303,] 28.00 7
[304,] 28.70 8
[305,] 27.20 6
[306,] 25.20 3
[307,] 28.20 7
[308,] 28.30 7
[309,] 28.70 8
[310,] 28.40 7
[311,] 28.00 7
[312,] 27.90 7
[313,] 27.90 7
[314,] 27.50 6
[315,] 26.80 5
[316,] 27.10 6
[317,] 28.60 7
[318,] 26.60 5
[319,] 27.70 6
[320,] 27.70 6
[321,] 27.40 6
[322,] 28.00 7
[323,] 28.90 8
[324,] 28.60 7
[325,] 27.60 6
[326,] 26.60 5
[327,] 27.10 6
[328,] 28.80 8
[329,] 28.00 7
[330,] 27.90 7
[331,] 28.80 8
[332,] 27.50 6
[333,] 27.50 6
[334,] 28.30 7
[335,] 29.00 8
[336,] 27.80 7
[337,] 28.20 7
[338,] 29.10 8
[339,] 27.70 6
[340,] 28.00 7
[341,] 27.40 6
[342,] 28.10 7
[343,] 28.50 7
[344,] 28.30 7
[345,] 28.20 7
[346,] 26.90 5
[347,] 28.00 7
[348,] 27.40 6
[349,] 27.80 7
[350,] 28.00 7
[351,] 28.50 7
[352,] 26.10 5
[353,] 28.50 7
[354,] 29.20 8
[355,] 28.00 7
[356,] 28.00 7
[357,] 28.50 7
[358,] 28.90 8
[359,] 28.70 8
[360,] 27.60 6
[361,] 27.50 6
[362,] 27.30 6
[363,] 27.50 6
[364,] 28.00 7
[365,] 28.10 7
[366,] 26.70 5
[367,] 27.20 6
[368,] 28.50 7
[369,] 28.80 8
[370,] 29.40 8
[371,] 28.40 7
[372,] 28.30 7
[373,] 27.80 7
[374,] 27.90 7
[375,] 27.40 6
[376,] 27.90 7
[377,] 27.90 7
[378,] 28.50 7
[379,] 26.80 5
[380,] 27.40 6
[381,] 26.50 5
[382,] 26.80 5
[383,] 27.30 6
[384,] 27.90 7
[385,] 27.20 6
[386,] 27.70 6
[387,] 26.50 5
[388,] 27.10 6
[389,] 28.00 7
[390,] 26.80 5
[391,] 26.70 5
[392,] 26.90 5
[393,] 26.80 5
[394,] 27.00 6
[395,] 25.80 4
[396,] 27.00 6
[397,] 27.60 6
[398,] 27.10 6
[399,] 28.50 7
[400,] 27.20 6
[401,] 27.70 6
[402,] 28.40 7
[403,] 28.40 7
[404,] 27.70 6
[405,] 27.00 6
[406,] 28.50 7
[407,] 27.70 6
[408,] 28.30 7
[409,] 27.20 6
[410,] 26.10 5
[411,] 26.80 5
[412,] 27.10 6
[413,] 27.60 6
[414,] 28.50 7
[415,] 28.10 7
[416,] 28.30 7
[417,] 28.00 7
[418,] 27.10 6
[419,] 29.70 9
[420,] 28.00 7
[421,] 28.80 8
[422,] 27.70 6
[423,] 27.90 7
[424,] 27.20 6
[425,] 27.80 7
[426,] 28.60 7
[427,] 26.50 5
[428,] 28.30 7
[429,] 29.10 8
[430,] 28.90 8
[431,] 28.80 8
[432,] 28.00 7
[433,] 27.10 6
[434,] 27.70 6
[435,] 26.20 5
[436,] 26.40 5
[437,] 28.20 7
[438,] 27.80 7
[439,] 26.60 5
[440,] 27.30 6
[441,] 28.10 7
[442,] 28.30 7
[443,] 28.60 7
[444,] 27.80 7
[445,] 28.40 7
[446,] 27.50 6
[447,] 28.30 7
[448,] 27.20 6
[449,] 27.80 7
[450,] 28.00 7
[451,] 27.50 6
[452,] 28.60 7
[453,] 27.40 6
[454,] 27.70 6
[455,] 27.90 7
[456,] 27.20 6
[457,] 27.80 7
[458,] 27.20 6
[459,] 26.90 5
[460,] 27.20 6
[461,] 28.60 7
[462,] 28.40 7
[463,] 27.30 6
[464,] 27.70 6
[465,] 27.90 7
[466,] 29.30 8
[467,] 27.80 7
[468,] 27.90 7
[469,] 27.40 6
[470,] 26.40 5
[471,] 27.40 6
[472,] 27.20 6
[473,] 28.80 8
[474,] 27.90 7
[475,] 27.90 7
[476,] 28.10 7
[477,] 28.60 7
[478,] 27.70 6
[479,] 28.50 7
[480,] 27.10 6
[481,] 26.60 5
[482,] 28.10 7
[483,] 27.20 6
[484,] 28.60 7
[485,] 27.50 6
[486,] 26.90 5
[487,] 26.60 5
[488,] 27.00 6
[489,] 29.30 8
[490,] 27.10 6
[491,] 28.20 7
[492,] 27.40 6
[493,] 26.50 5
[494,] 26.20 5
[495,] 26.80 5
[496,] 26.80 5
[497,] 27.70 6
[498,] 26.90 5
[499,] 27.90 7
[500,] 28.70 8
[ reached getOption("max.print") -- omitted 8088 rows ]Membuat Fuzzy Logical Relation (FLR)
FLR = data.frame(fuzzifikasi=0,left=NA,right =NA)
for (i in 1:length(fuzifikasi)) {
FLR[i,1] = fuzifikasi[i]
FLR[i+1,2] = fuzifikasi[i]
FLR[i,3] = fuzifikasi[i]
}
FLR = FLR[-nrow(FLR),]
FLR = FLR[-1,]
FLRMembuat Fuzyy Logical Relation Group (FLRG)
FLRG = table(FLR[,2:3])
FLRG right
left 1 2 3 4 5 6 7 8 9 10 11 12 13
1 0 0 1 0 0 0 0 0 0 0 0 0 0
2 0 1 4 5 5 1 0 0 0 0 0 0 0
3 1 7 9 18 29 11 3 0 0 0 0 0 0
4 0 5 20 47 94 66 28 5 0 0 0 0 0
5 0 3 25 88 322 311 196 40 7 0 1 0 0
6 0 0 11 61 287 590 603 164 32 2 2 0 0
7 0 0 7 34 195 548 1117 602 133 15 2 0 2
8 0 0 1 8 53 185 539 695 264 41 3 0 0
9 0 0 0 2 7 32 154 252 267 59 9 0 0
10 0 0 0 2 0 6 12 26 70 44 17 2 0
11 0 0 0 0 1 2 1 3 10 16 26 4 1
12 0 0 0 0 0 0 0 1 0 2 3 2 0
13 0 0 0 0 0 0 1 1 0 0 1 0 0
14 0 0 0 0 0 0 0 0 0 1 0 0 0
right
left 14
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 1
11 0
12 0
13 0
14 0Membuat Matriks Transisi
bobot = round(prop.table(table(FLR[,2:3]),1),5)
bobot right
left 1 2 3 4 5 6 7 8
1 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000
2 0.00000 0.06250 0.25000 0.31250 0.31250 0.06250 0.00000 0.00000
3 0.01282 0.08974 0.11538 0.23077 0.37179 0.14103 0.03846 0.00000
4 0.00000 0.01887 0.07547 0.17736 0.35472 0.24906 0.10566 0.01887
5 0.00000 0.00302 0.02518 0.08862 0.32427 0.31319 0.19738 0.04028
6 0.00000 0.00000 0.00628 0.03482 0.16381 0.33676 0.34418 0.09361
7 0.00000 0.00000 0.00264 0.01281 0.07345 0.20640 0.42072 0.22674
8 0.00000 0.00000 0.00056 0.00447 0.02963 0.10341 0.30129 0.38849
9 0.00000 0.00000 0.00000 0.00256 0.00895 0.04092 0.19693 0.32225
10 0.00000 0.00000 0.00000 0.01111 0.00000 0.03333 0.06667 0.14444
11 0.00000 0.00000 0.00000 0.00000 0.01562 0.03125 0.01562 0.04688
12 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.12500
13 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.33333 0.33333
14 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
right
left 9 10 11 12 13 14
1 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
2 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
3 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
4 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
5 0.00705 0.00000 0.00101 0.00000 0.00000 0.00000
6 0.01826 0.00114 0.00114 0.00000 0.00000 0.00000
7 0.05009 0.00565 0.00075 0.00000 0.00075 0.00000
8 0.14757 0.02292 0.00168 0.00000 0.00000 0.00000
9 0.34143 0.07545 0.01151 0.00000 0.00000 0.00000
10 0.38889 0.24444 0.09444 0.01111 0.00000 0.00556
11 0.15625 0.25000 0.40625 0.06250 0.01562 0.00000
12 0.00000 0.25000 0.37500 0.25000 0.00000 0.00000
13 0.00000 0.00000 0.33333 0.00000 0.00000 0.00000
14 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000Peramalan Awal
diagonal = diag(bobot)
m.diagonal = diag(diagonal)
#mengambil nilai selain diagonal
pinggir = bobot-m.diagonal
ramal=NULL
for (i in 1:(length(fuzifikasi))){
for (j in 1:(nrow(bobot))){
if (fuzifikasi[i]==j)
{ramal[i+1]=(diagonal[j]*data[i])+sum(pinggir[j,]*n.tengah[,1]) }else
if (fuzifikasi[i]==j)
{ramal[i]=0}
}
}
ramal = ramal[-length(ramal)]
ramal [1] NA 28.08706 27.77697 27.77697 27.77697 27.60859 27.57492
[8] 28.21327 28.29742 28.33949 28.57355 27.60859 28.29742 28.17120
[15] 28.33949 28.04498 27.54124 28.25534 28.29742 27.64227 28.00291
[22] 28.04498 27.60859 27.67595 26.69518 26.64197 27.15708 28.04498
[29] 28.04498 28.17120 27.54124 27.64227 27.15708 27.74330 27.60859
[36] 28.04498 28.33949 27.77697 28.61240 28.65125 28.84550 28.08706
[43] 27.54124 28.12913 28.61240 28.29742 28.69010 28.80665 28.80665
[50] 28.84550 27.77697 29.14339 28.65125 27.74330 27.64227 28.00291
[57] 27.54124 28.17120 28.17120 28.57355 29.17753 27.77697 28.69010
[64] 27.74330 28.25534 28.17120 27.64227 27.64227 27.60859 27.67595
[71] 28.29742 28.65125 29.14339 27.64227 27.54124 28.00291 28.04498
[78] 28.25534 27.70962 27.74330 27.70962 27.77697 28.08706 28.21327
[85] 27.70962 28.65125 27.57492 27.22193 27.57492 26.99494 27.77697
[92] 27.74330 27.70962 28.04498 27.67595 28.12913 27.74330 28.33949
[99] 28.00291 27.67595 27.22193 27.22193 27.22193 28.00291 27.70962
[106] 27.57492 27.22193 27.70962 27.74330 27.18951 27.74330 28.57355
[113] 27.18951 28.21327 27.60859 27.60859 27.15708 27.54124 27.67595
[120] 27.22193 27.25436 27.60859 28.12913 28.04498 27.77697 27.67595
[127] 28.69010 27.64227 28.29742 27.77697 28.17120 28.57355 28.12913
[134] 28.29742 28.00291 28.33949 27.70962 28.57355 27.54124 27.67595
[141] 28.76780 28.61240 28.25534 28.57355 27.57492 27.15708 28.12913
[148] 28.33949 28.17120 27.02737 27.09222 27.12465 27.64227 28.08706
[155] 27.60859 27.64227 27.60859 26.67744 27.54124 26.65970 27.09222
[162] 27.12465 27.12465 27.77697 27.67595 27.12465 26.03084 27.15708
[169] 27.22193 26.60650 27.18951 27.05980 26.00777 26.99494 28.12913
[176] 27.67595 27.77697 27.54124 28.17120 28.04498 27.15708 27.67595
[183] 27.02737 26.62423 27.09222 26.07700 26.03084 28.12913 28.12913
[190] 28.29742 28.04498 27.57492 27.70962 27.74330 28.61240 28.57355
[197] 28.08706 28.12913 27.25436 27.64227 27.15708 28.21327 28.21327
[204] 27.15708 26.57102 27.54124 27.70962 27.25436 27.02737 27.60859
[211] 27.64227 27.22193 27.67595 27.18951 27.15708 27.09222 27.05980
[218] 27.67595 27.70962 27.64227 28.08706 27.64227 27.70962 27.15708
[225] 26.07700 26.67744 28.00291 27.70962 27.57492 27.70962 27.67595
[232] 27.70962 28.00291 27.15708 27.25436 27.67595 27.70962 27.74330
[239] 27.74330 28.04498 28.00291 28.84550 28.33949 28.08706 27.18951
[246] 27.77697 28.17120 27.67595 28.69010 28.08706 28.17120 27.77697
[253] 27.05980 27.54124 28.00291 28.00291 27.77697 27.64227 28.00291
[260] 28.17120 28.25534 28.65125 28.21327 28.04498 27.64227 27.15708
[267] 27.09222 27.22193 28.17120 27.74330 28.04498 28.00291 27.18951
[274] 26.58876 27.18951 28.08706 27.67595 28.00291 28.25534 28.04498
[281] 28.00291 27.60859 27.64227 28.17120 28.72895 28.65125 28.08706
[288] 29.14339 28.25534 29.90944 29.10924 27.67595 28.08706 28.29742
[295] 29.10924 28.84550 27.77697 28.65125 28.57355 29.31410 29.34824
[302] 29.27996 28.29742 28.08706 28.57355 27.60859 26.08853 28.17120
[309] 28.21327 28.57355 28.25534 28.08706 28.04498 28.04498 27.70962
[316] 27.22193 27.57492 28.33949 27.15708 27.77697 27.77697 27.67595
[323] 28.08706 28.65125 28.33949 27.74330 27.15708 27.57492 28.61240
[330] 28.08706 28.04498 28.61240 27.70962 27.70962 28.21327 28.69010
[337] 28.00291 28.17120 28.72895 27.77697 28.08706 27.67595 28.12913
[344] 28.29742 28.21327 28.17120 27.25436 28.08706 27.67595 28.00291
[351] 28.08706 28.29742 26.99494 28.29742 28.76780 28.08706 28.08706
[358] 28.29742 28.65125 28.57355 27.74330 27.70962 27.64227 27.70962
[365] 28.08706 28.12913 27.18951 27.60859 28.29742 28.61240 28.84550
[372] 28.25534 28.21327 28.00291 28.04498 27.67595 28.04498 28.04498
[379] 28.29742 27.22193 27.67595 27.12465 27.22193 27.64227 28.04498
[386] 27.60859 27.77697 27.12465 27.57492 28.08706 27.22193 27.18951
[393] 27.25436 27.22193 27.54124 26.65970 27.54124 27.74330 27.57492
[400] 28.29742 27.60859 27.77697 28.25534 28.25534 27.77697 27.54124
[407] 28.29742 27.77697 28.21327 27.60859 26.99494 27.22193 27.57492
[414] 27.74330 28.29742 28.12913 28.21327 28.08706 27.57492 29.17753
[421] 28.08706 28.61240 27.77697 28.04498 27.60859 28.00291 28.33949
[428] 27.12465 28.21327 28.72895 28.65125 28.61240 28.08706 27.57492
[435] 27.77697 27.02737 27.09222 28.17120 28.00291 27.15708 27.64227
[442] 28.12913 28.21327 28.33949 28.00291 28.25534 27.70962 28.21327
[449] 27.60859 28.00291 28.08706 27.70962 28.33949 27.67595 27.77697
[456] 28.04498 27.60859 28.00291 27.60859 27.25436 27.60859 28.33949
[463] 28.25534 27.64227 27.77697 28.04498 28.80665 28.00291 28.04498
[470] 27.67595 27.09222 27.67595 27.60859 28.61240 28.04498 28.04498
[477] 28.12913 28.33949 27.77697 28.29742 27.57492 27.15708 28.12913
[484] 27.60859 28.33949 27.70962 27.25436 27.15708 27.54124 28.80665
[491] 27.57492 28.17120 27.67595 27.12465 27.02737 27.22193 27.22193
[498] 27.77697 27.25436 28.04498 28.57355 28.25534 28.25534 28.17120
[505] 28.65125 28.08706 28.65125 28.17120 28.08706 28.57355 28.08706
[512] 28.04498 28.29742 28.57355 28.29742 28.69010 28.29742 28.61240
[519] 28.17120 27.67595 28.57355 26.69518 27.22193 28.00291 28.25534
[526] 26.67744 27.60859 27.12465 27.12465 26.69518 27.64227 27.57492
[533] 28.04498 26.69518 27.18951 26.06546 26.08853 27.60859 27.18951
[540] 27.70962 27.09222 27.60859 27.54124 27.09222 27.74330 27.15708
[547] 27.67595 27.60859 27.09222 26.08853 26.57102 27.74330 27.67595
[554] 27.15708 27.54124 28.25534 28.04498 28.33949 28.21327 28.12913
[561] 28.25534 28.29742 28.29742 27.70962 27.70962 28.21327 27.74330
[568] 27.70962 27.70962 28.17120 28.08706 27.74330 27.64227 27.57492
[575] 28.04498 28.33949 28.69010 28.17120 28.29742 28.21327 28.00291
[582] 28.17120 28.21327 28.25534 27.57492 28.29742 27.09222 28.72895
[589] 26.99494 26.03084 27.22193 27.15708 27.60859 27.57492 27.25436
[596] 27.67595 27.60859 29.27996 29.90944 27.74330 28.12913 28.21327
[603] 28.12913 27.64227 27.74330 27.25436 27.02737 27.09222 27.15708
[610] 28.25534 28.21327 28.25534 28.08706 28.08706 27.67595 27.77697
[617] 28.08706 28.04498 28.72895 28.04498 28.00291 28.17120 28.65125
[624] 28.04498 28.33949 28.57355 29.10924 28.65125 28.65125 28.80665
[631] 28.29742 28.25534 27.77697 27.25436 27.09222 29.14339 28.12913
[638] 28.17120 28.04498 27.15708 28.84550 28.76780 29.31410 28.80665
[645] 28.17120 27.70962 28.12913 28.21327 28.61240 27.70962 28.69010
[652] 29.17753 28.80665 29.31410 29.10924 29.31410 28.21327 28.25534
[659] 28.69010 28.84550 28.12913 28.76780 28.72895 28.72895 28.69010
[666] 28.80665 28.69010 29.31410 28.61240 28.80665 28.65125 28.76780
[673] 29.21167 29.10924 28.57355 28.57355 28.80665 28.65125 28.80665
[680] 29.27996 29.31410 28.84550 29.21167 28.25534 29.14339 28.12913
[687] 28.00291 29.34824 29.14339 29.21167 28.80665 28.33949 28.33949
[694] 28.76780 29.90944 28.72895 28.17120 28.17120 28.33949 28.12913
[701] 28.57355 28.12913 28.25534 28.33949 27.77697 28.12913 28.84550
[708] 28.80665 29.14339 28.65125 28.08706 27.12465 28.21327 27.74330
[715] 28.08706 26.99494 28.69010 28.76780 28.12913 28.25534 27.74330
[722] 29.88500 30.03166 28.65125 28.04498 28.00291 28.57355 28.65125
[729] 28.33949 28.12913 28.25534 28.57355 26.67744 28.12913 28.57355
[736] 28.29742 28.65125 28.25534 28.57355 28.29742 28.80665 29.14339
[743] 28.57355 29.27996 28.80665 28.69010 29.14339 28.57355 28.08706
[750] 27.60859 27.67595 28.69010 28.25534 28.04498 27.25436 28.25534
[757] 28.12913 27.02737 27.15708 27.57492 27.57492 28.17120 27.74330
[764] 28.17120 28.29742 28.33949 27.15708 28.65125 28.72895 27.60859
[771] 27.18951 27.77697 27.15708 27.60859 28.84550 28.21327 28.33949
[778] 27.74330 27.74330 27.77697 27.77697 28.69010 28.29742 28.29742
[785] 28.69010 28.12913 28.29742 28.57355 28.08706 28.65125 28.04498
[792] 27.67595 28.72895 28.80665 27.57492 27.74330 28.33949 28.25534
[799] 28.12913 27.60859 28.04498 28.25534 28.08706 27.64227 27.67595
[806] 27.18951 27.74330 27.70962 27.74330 28.12913 28.00291 28.21327
[813] 27.70962 26.60650 28.25534 28.65125 28.17120 27.64227 27.64227
[820] 26.60650 27.22193 28.21327 28.25534 28.12913 28.08706 28.21327
[827] 28.84550 28.65125 28.29742 27.57492 27.60859 28.21327 27.74330
[834] 28.00291 28.69010 28.21327 28.08706 27.70962 28.00291 28.80665
[841] 29.14339 27.54124 28.29742 28.57355 28.12913 28.69010 28.25534
[848] 27.15708 26.65970 27.57492 27.25436 27.64227 27.22193 27.18951
[855] 28.04498 27.70962 27.77697 28.69010 27.74330 26.67744 27.67595
[862] 28.08706 27.57492 28.08706 27.02737 27.67595 28.04498 28.21327
[869] 28.29742 28.08706 28.65125 28.12913 28.57355 28.61240 28.29742
[876] 28.72895 27.67595 28.61240 28.17120 28.21327 28.08706 28.80665
[883] 28.29742 29.14339 29.10924 29.10924 28.17120 27.64227 28.33949
[890] 28.12913 28.61240 27.18951 28.21327 27.09222 27.64227 27.60859
[897] 26.99494 28.29742 28.33949 28.25534 27.18951 27.22193 27.15708
[904] 27.77697 27.18951 27.25436 27.05980 27.15708 27.74330 28.25534
[911] 27.18951 27.18951 27.67595 27.12465 27.18951 27.57492 28.00291
[918] 27.54124 26.03084 26.04238 26.08853 26.04238 27.25436 27.74330
[925] 28.04498 27.25436 27.18951 27.22193 27.64227 26.65970 27.09222
[932] 27.64227 26.64197 26.04238 27.09222 26.04238 26.67744 27.18951
[939] 27.57492 27.64227 28.21327 28.12913 26.62423 27.12465 27.60859
[946] 27.77697 26.62423 27.60859 27.22193 27.67595 28.69010 27.77697
[953] 28.04498 28.08706 27.54124 28.04498 27.25436 28.08706 27.05980
[960] 27.67595 27.57492 27.57492 27.77697 28.04498 27.09222 27.18951
[967] 27.74330 26.69518 27.74330 27.60859 28.00291 27.67595 27.02737
[974] 27.67595 28.12913 29.10924 28.29742 28.29742 28.25534 28.12913
[981] 27.70962 27.74330 28.57355 27.74330 27.25436 28.17120 27.74330
[988] 27.60859 27.77697 27.12465 27.57492 27.67595 27.60859 27.57492
[995] 27.77697 28.08706 28.57355 28.57355 27.74330 28.80665
[ reached getOption("max.print") -- omitted 7588 entries ]Adjusted Forecasting Value (Peramalan Tahap Kedua)
adjusted = rep(0,nrow(FLR))
selisih = FLR[,3]-FLR[,2]
for(i in 1:nrow(FLR))
{
if (FLR[i,2]!=FLR[i,3] && diagonal[FLR[i,2]]==0)
{adjusted[i]=selisih[i]*(L/2)} else #Untuk tidak komunicate
if (selisih[i]==1 && diagonal[FLR[i,2]]>0)
{adjusted[i]=(L)} else #Untuk komunicate
if (FLR[i,2]!=FLR[i,3] && diagonal[FLR[i,2]]>0)
{adjusted[i]=selisih[i]*L/2} #Untuk komunicate
}
adjusted [1] -0.425 0.000 0.000 0.000 0.000 0.850 0.000 0.000 0.850
[10] -0.850 0.850 0.000 0.000 0.000 -0.425 0.850 0.000 -0.425
[19] 0.850 0.000 -0.425 0.000 -0.850 0.000 0.850 0.850 0.000
[28] 0.000 -0.425 0.000 -0.425 0.850 0.000 0.850 0.000 -0.425
[37] 0.850 0.000 0.000 -0.425 -0.425 0.850 0.850 -0.425 0.850
[46] 0.000 0.000 0.000 -0.850 1.275 -0.425 -0.850 0.000 0.850
[55] -0.425 0.850 0.000 0.850 0.850 -1.275 0.850 -0.850 0.850
[64] 0.000 -0.425 0.000 0.000 0.000 0.850 0.850 0.850 -1.275
[73] 0.000 0.850 0.000 0.000 -0.425 0.000 0.000 0.000 0.850
[82] 0.000 -0.425 0.850 -0.850 -0.425 0.850 -0.425 0.850 0.000
[91] 0.000 0.850 -0.425 0.850 -0.425 0.850 0.000 -0.425 -0.425
[100] 0.000 0.000 0.850 -0.425 0.000 -0.425 0.850 0.000 -0.425
[109] 0.850 0.850 -1.275 0.850 -0.425 0.000 -0.425 0.850 0.000
[118] -0.425 0.000 0.850 0.850 0.000 -0.425 0.000 0.850 -0.850
[127] 0.850 -0.425 0.850 0.850 -0.425 0.000 0.000 0.000 -0.425
[136] 0.850 -0.850 0.000 0.850 0.000 -0.425 0.850 -0.850 -0.425
[145] 0.850 0.000 0.000 -0.850 0.000 0.000 0.850 0.850 -0.425
[154] 0.000 0.000 -0.850 0.850 -0.850 0.850 0.000 0.000 0.850
[163] 0.000 -0.425 -0.850 0.850 0.000 -0.425 0.850 0.000 -0.850
[172] 0.850 0.850 -0.425 0.000 0.000 0.850 0.000 -0.850 0.850
[181] -0.425 -0.425 0.850 -0.850 0.000 1.700 0.000 0.000 0.000
[190] -0.425 0.000 0.000 0.850 0.000 -0.425 0.000 -0.850 0.850
[199] -0.425 0.850 0.000 -0.850 -0.425 0.850 0.000 -0.425 0.000
[208] 0.850 0.000 -0.425 0.850 -0.425 0.000 0.000 0.000 0.850
[217] 0.000 0.000 0.850 -0.425 0.000 -0.425 -0.850 0.850 1.275
[226] -0.425 0.000 0.000 0.000 0.000 0.850 -0.850 0.000 0.850
[235] 0.000 0.000 0.000 0.850 0.000 0.850 -0.425 0.000 -0.850
[244] 0.850 0.850 -0.425 0.850 -0.425 0.000 -0.425 -0.425 0.850
[253] 0.850 0.000 -0.425 0.000 0.850 0.000 0.000 0.850 -0.425
[262] 0.000 -0.425 -0.425 0.000 0.000 0.850 -0.425 0.850 0.000
[271] -0.850 -0.425 0.850 0.850 -0.425 0.850 0.000 0.000 0.000
[280] -0.425 0.000 0.850 0.850 0.000 -0.425 0.850 -0.850 1.275
[289] -0.425 -1.275 0.850 0.000 0.850 -0.425 -0.850 0.850 0.000
[298] 0.850 0.000 0.000 -0.850 0.000 0.850 -0.850 -1.275 1.700
[307] 0.000 0.850 -0.425 0.000 0.000 0.000 -0.425 -0.425 0.850
[316] 0.850 -0.850 0.850 0.000 0.000 0.850 0.850 -0.425 -0.425
[325] -0.425 0.850 0.850 -0.425 0.000 0.850 -0.850 0.000 0.850
[334] 0.850 -0.425 0.000 0.850 -0.850 0.850 -0.425 0.850 0.000
[343] 0.000 0.000 -0.850 0.850 -0.425 0.850 0.000 0.000 -0.850
[352] 0.850 0.850 -0.425 0.000 0.000 0.850 0.000 -0.850 0.000
[361] 0.000 0.000 0.850 0.000 -0.850 0.850 0.850 0.850 0.000
[370] -0.425 0.000 0.000 0.000 -0.425 0.850 0.000 0.000 -0.850
[379] 0.850 -0.425 0.000 0.850 0.850 -0.425 0.000 -0.425 0.850
[388] 0.850 -0.850 0.000 0.000 0.000 0.850 -0.850 0.850 0.000
[397] 0.000 0.850 -0.425 0.000 0.850 0.000 -0.425 0.000 0.850
[406] -0.425 0.850 -0.425 -0.425 0.000 0.850 0.000 0.850 0.000
[415] 0.000 0.000 -0.425 1.275 -0.850 0.850 -0.850 0.850 -0.425
[424] 0.850 0.000 -0.850 0.850 0.850 0.000 0.000 -0.425 -0.425
[433] 0.000 -0.425 0.000 0.850 0.000 -0.850 0.850 0.850 0.000
[442] 0.000 0.000 0.000 -0.425 0.850 -0.425 0.850 0.000 -0.425
[451] 0.850 -0.425 0.000 0.850 -0.425 0.850 -0.425 -0.425 0.850
[460] 0.850 0.000 -0.425 0.000 0.850 0.850 -0.425 0.000 -0.425
[469] -0.425 0.850 0.000 0.850 -0.425 0.000 0.000 0.000 -0.425
[478] 0.850 -0.425 -0.425 0.850 -0.425 0.850 -0.425 -0.425 0.000
[487] 0.850 0.850 -0.850 0.850 -0.425 -0.425 0.000 0.000 0.000
[496] 0.850 -0.425 0.850 0.850 -0.425 0.000 0.000 0.850 -0.425
[505] 0.850 -0.425 0.000 0.850 -0.425 0.000 0.000 0.850 -0.425
[514] 0.850 -0.425 0.850 -0.425 -0.425 0.850 -1.700 0.850 0.850
[523] 0.000 -1.275 0.850 -0.425 0.000 -0.425 0.850 0.000 0.850
[532] -1.275 0.850 -0.850 0.000 1.275 -0.425 0.850 -0.425 0.850
[541] 0.000 -0.425 0.850 -0.425 0.850 0.000 -0.425 -0.850 0.850
[550] 0.850 0.000 -0.425 0.850 0.850 0.000 0.000 0.000 0.000
[559] 0.000 0.000 0.000 -0.425 0.000 0.850 -0.425 0.000 0.000
[568] 0.850 0.000 -0.425 0.000 0.000 0.850 0.000 0.850 -0.425
[577] 0.000 0.000 0.000 0.000 0.000 0.000 -0.425 0.850 -0.850
[586] 1.275 -1.275 -0.850 0.850 0.000 0.850 0.000 -0.425 0.850
[595] 0.000 1.275 0.850 -1.700 0.850 0.000 0.000 -0.425 0.000
[604] -0.425 0.000 0.000 0.000 0.850 0.000 0.000 0.000 0.000
[613] -0.425 0.000 0.850 0.000 0.850 -0.425 0.000 0.000 0.850
[622] -0.425 0.000 0.850 0.850 -0.425 0.000 0.000 -0.425 0.000
[631] -0.425 -0.425 0.000 1.700 -0.850 0.000 0.000 -0.850 1.275
[640] 0.000 0.850 -0.425 -0.425 -0.425 0.850 0.000 0.850 -0.850
[649] 0.850 0.850 -0.425 0.850 0.000 0.000 -0.850 0.000 0.850
[658] 0.000 -0.425 0.850 0.000 0.000 0.000 0.000 0.000 0.850
[667] -0.425 0.000 0.000 0.000 0.850 0.000 -0.425 0.000 0.000
[676] 0.000 0.000 0.850 0.000 -0.425 0.850 -0.850 0.850 -0.850
[685] 0.000 0.850 0.000 0.000 -0.425 -0.425 0.000 0.850 0.850
[694] -0.850 -0.425 0.000 0.000 0.000 0.850 -0.425 0.000 0.000
[703] -0.425 0.850 0.850 0.000 0.850 -0.425 -0.425 -0.850 0.850
[712] -0.425 0.850 -0.850 1.275 0.000 -0.425 0.000 -0.425 1.700
[721] 0.000 -0.850 -0.425 0.000 0.850 0.000 -0.425 0.000 0.000
[730] 0.850 -1.700 1.275 0.850 -0.425 0.850 -0.425 0.850 -0.425
[739] 0.850 0.850 -0.425 0.850 -0.425 0.000 0.850 -0.425 -0.425
[748] -0.425 0.000 0.850 -0.425 0.000 -0.850 0.850 0.000 -0.850
[757] 0.000 0.850 0.000 0.850 -0.425 0.850 0.000 0.000 -0.850
[766] 1.275 0.000 -0.850 -0.425 0.850 -0.425 0.850 0.850 -0.425
[775] 0.000 -0.425 0.000 0.000 0.000 0.850 -0.425 0.000 0.850
[784] -0.425 0.000 0.850 -0.425 0.850 -0.425 -0.425 0.850 0.000
[793] -0.850 0.000 0.850 0.000 0.000 -0.425 0.850 0.000 0.000
[802] -0.425 0.000 -0.425 0.850 0.000 0.000 0.850 0.000 0.000
[811] -0.425 -0.850 1.275 0.850 -0.425 -0.425 0.000 -0.850 0.850
[820] 0.850 0.000 0.000 0.000 0.000 0.850 0.000 -0.425 -0.425
[829] 0.000 0.850 -0.425 0.850 0.850 -0.425 0.000 -0.425 0.850
[838] 0.850 0.850 -1.275 0.850 0.850 -0.425 0.850 -0.425 -0.850
[847] -0.425 0.850 -0.425 0.850 -0.425 0.000 0.850 -0.425 0.000
[856] 0.850 -0.850 -0.850 0.850 0.850 -0.425 0.850 -0.850 0.850
[865] 0.850 0.000 0.000 0.000 0.850 -0.425 0.850 0.000 -0.425
[874] 0.850 -0.850 0.850 -0.425 0.000 0.000 0.850 -0.425 0.850
[883] 0.000 0.000 -0.850 -0.425 0.850 0.000 0.850 -1.275 0.850
[892] -0.850 0.850 0.000 -0.425 0.850 0.000 0.000 -0.850 0.000
[901] 0.000 0.850 -0.425 0.000 0.000 0.000 0.850 0.850 -0.850
[910] 0.000 0.850 -0.425 0.000 0.850 0.850 -0.425 -1.275 0.000
[919] 0.000 0.000 0.850 0.850 0.850 -0.850 0.000 0.000 0.850
[928] -0.850 0.850 0.850 -0.850 -0.425 0.850 -0.850 0.850 0.850
[937] 0.850 0.000 0.850 0.000 -1.275 0.850 0.850 0.000 -0.850
[946] 0.850 -0.425 0.850 0.850 -0.850 0.850 0.000 -0.425 0.850
[955] -0.850 0.850 -0.850 0.850 0.000 0.000 0.000 0.850 -0.850
[964] 0.000 0.850 -0.850 0.850 0.000 0.850 -0.425 -0.425 0.850
[973] 0.850 0.850 -0.850 0.000 0.000 0.000 -0.425 0.000 0.850
[982] -0.850 -0.425 0.850 -0.425 0.000 0.000 -0.425 0.850 0.000
[991] 0.000 0.000 0.000 0.850 0.850 0.000 -0.850 0.850 0.000
[1000] 0.000
[ reached getOption("max.print") -- omitted 7587 entries ]Peramalan Tahap Terakhir
ramal=ramal[c(2:length(ramal))]
adj.forecast=adjusted + ramal
adj.forecast [1] 27.66206 27.77697 27.77697 27.77697 27.60859 28.42492 28.21327
[8] 28.29742 29.18949 27.72355 28.45859 28.29742 28.17120 28.33949
[15] 27.61998 28.39124 28.25534 27.87242 28.49227 28.00291 27.61998
[22] 27.60859 26.82595 26.69518 27.49197 28.00708 28.04498 28.04498
[29] 27.74620 27.54124 27.21727 28.00708 27.74330 28.45859 28.04498
[36] 27.91449 28.62697 28.61240 28.65125 28.42050 27.66206 28.39124
[43] 28.97913 28.18740 29.14742 28.69010 28.80665 28.80665 27.99550
[50] 29.05197 28.71839 27.80125 27.74330 28.49227 27.57791 28.39124
[57] 28.17120 29.02120 29.42355 27.90253 28.62697 27.84010 28.59330
[64] 28.25534 27.74620 27.64227 27.64227 27.60859 28.52595 29.14742
[71] 29.50125 27.86839 27.64227 28.39124 28.00291 28.04498 27.83034
[78] 27.70962 27.74330 27.70962 28.62697 28.08706 27.78827 28.55962
[85] 27.80125 27.14992 28.07193 27.14992 27.84494 27.77697 27.74330
[92] 28.55962 27.61998 28.52595 27.70413 28.59330 28.33949 27.57791
[99] 27.25095 27.22193 27.22193 28.07193 27.57791 27.70962 27.14992
[106] 28.07193 27.70962 27.31830 28.03951 28.59330 27.29855 28.03951
[113] 27.78827 27.60859 27.18359 28.00708 27.54124 27.25095 27.22193
[120] 28.10436 28.45859 28.12913 27.61998 27.77697 28.52595 27.84010
[127] 28.49227 27.87242 28.62697 29.02120 28.14855 28.12913 28.29742
[134] 28.00291 27.91449 28.55962 27.72355 27.54124 28.52595 28.76780
[141] 28.18740 29.10534 27.72355 27.14992 28.00708 28.12913 28.33949
[148] 27.32120 27.02737 27.09222 27.97465 28.49227 27.66206 27.60859
[155] 27.64227 26.75859 27.52744 26.69124 27.50970 27.09222 27.12465
[162] 27.97465 27.77697 27.25095 26.27465 26.88084 27.15708 26.79693
[169] 27.45650 27.18951 26.20980 26.85777 27.84494 27.70413 27.67595
[176] 27.77697 28.39124 28.17120 27.19498 28.00708 27.25095 26.60237
[183] 27.47423 26.24222 26.07700 27.73084 28.12913 28.12913 28.29742
[190] 27.61998 27.57492 27.70962 28.59330 28.61240 28.14855 28.08706
[197] 27.27913 28.10436 27.21727 28.00708 28.21327 27.36327 26.73208
[204] 27.42102 27.54124 27.28462 27.25436 27.87737 27.60859 27.21727
[211] 28.07193 27.25095 27.18951 27.15708 27.09222 27.90980 27.67595
[218] 27.70962 28.49227 27.66206 27.64227 27.28462 26.30708 26.92700
[225] 27.95244 27.57791 27.70962 27.57492 27.70962 27.67595 28.55962
[232] 27.15291 27.15708 28.10436 27.67595 27.70962 27.74330 28.59330
[239] 28.04498 28.85291 28.42050 28.33949 27.23706 28.03951 28.62697
[246] 27.74620 28.52595 28.26510 28.08706 27.74620 27.35197 27.90980
[253] 28.39124 28.00291 27.57791 27.77697 28.49227 28.00291 28.17120
[260] 29.10534 28.22625 28.21327 27.61998 27.21727 27.15708 27.09222
[267] 28.07193 27.74620 28.59330 28.04498 27.15291 26.76451 27.43876
[274] 28.03951 27.66206 28.52595 28.00291 28.25534 28.04498 27.57791
[281] 27.60859 28.49227 29.02120 28.72895 28.22625 28.93706 28.29339
[288] 29.53034 29.48444 27.83424 28.52595 28.08706 29.14742 28.68424
[295] 27.99550 28.62697 28.65125 29.42355 29.31410 29.34824 28.42996
[302] 28.29742 28.93706 27.72355 26.33359 27.78853 28.17120 29.06327
[309] 28.14855 28.25534 28.08706 28.04498 27.61998 27.28462 28.07193
[316] 28.42492 27.48949 28.00708 27.77697 27.77697 28.52595 28.93706
[323] 28.22625 27.91449 27.31830 28.00708 28.42492 28.18740 28.08706
[330] 28.89498 27.76240 27.70962 28.55962 29.06327 28.26510 28.00291
[337] 29.02120 27.87895 28.62697 27.66206 28.52595 28.12913 28.29742
[344] 28.21327 27.32120 28.10436 27.66206 28.52595 28.00291 28.08706
[351] 27.44742 27.84494 29.14742 28.34280 28.08706 28.08706 29.14742
[358] 28.65125 27.72355 27.74330 27.70962 27.64227 28.55962 28.08706
[365] 27.27913 28.03951 28.45859 29.14742 28.61240 28.42050 28.25534
[372] 28.21327 28.00291 27.61998 28.52595 28.04498 28.04498 27.44742
[379] 28.07193 27.25095 27.12465 28.07193 28.49227 27.61998 27.60859
[386] 27.35197 27.97465 28.42492 27.23706 27.22193 27.18951 27.25436
[393] 28.07193 26.69124 27.50970 27.54124 27.74330 28.42492 27.87242
[400] 27.60859 28.62697 28.25534 27.83034 27.77697 28.39124 27.87242
[407] 28.62697 27.78827 27.18359 26.99494 28.07193 27.57492 28.59330
[414] 28.29742 28.12913 28.21327 27.66206 28.84992 28.32753 28.93706
[421] 27.76240 28.62697 27.61998 28.45859 28.00291 27.48949 27.97465
[428] 29.06327 28.72895 28.65125 28.18740 27.66206 27.57492 27.35197
[435] 27.02737 27.94222 28.17120 27.15291 28.00708 28.49227 28.12913
[442] 28.21327 28.33949 28.00291 27.83034 28.55962 27.78827 28.45859
[449] 28.00291 27.66206 28.55962 27.91449 27.67595 28.62697 27.61998
[456] 28.45859 27.57791 27.18359 28.10436 28.45859 28.33949 27.83034
[463] 27.64227 28.62697 28.89498 28.38165 28.00291 27.61998 27.25095
[470] 27.94222 27.67595 28.45859 28.18740 28.04498 28.04498 28.12913
[477] 27.91449 28.62697 27.87242 27.14992 28.00708 27.70413 28.45859
[484] 27.91449 27.28462 27.25436 28.00708 28.39124 27.95665 28.42492
[491] 27.74620 27.25095 27.12465 27.02737 27.22193 28.07193 27.35197
[498] 28.10436 28.89498 28.14855 28.25534 28.25534 29.02120 28.22625
[505] 28.93706 28.22625 28.17120 28.93706 28.14855 28.08706 28.04498
[512] 29.14742 28.14855 29.14742 28.26510 29.14742 28.18740 27.74620
[519] 28.52595 26.87355 27.54518 28.07193 28.00291 26.98034 27.52744
[526] 27.18359 27.12465 26.69965 27.54518 27.64227 28.42492 26.76998
[533] 27.54518 26.33951 26.06546 27.36353 27.18359 28.03951 27.28462
[540] 27.94222 27.60859 27.11624 27.94222 27.31830 28.00708 27.67595
[547] 27.18359 26.24222 26.93853 27.42102 27.74330 27.25095 28.00708
[554] 28.39124 28.25534 28.04498 28.33949 28.21327 28.12913 28.25534
[561] 28.29742 27.87242 27.70962 28.55962 27.78827 27.74330 27.70962
[568] 28.55962 28.17120 27.66206 27.74330 27.64227 28.42492 28.04498
[575] 29.18949 28.26510 28.17120 28.29742 28.21327 28.00291 28.17120
[582] 28.21327 27.83034 28.42492 27.44742 28.36722 27.45395 26.14494
[589] 26.88084 27.22193 28.00708 27.60859 27.14992 28.10436 27.67595
[596] 28.88359 30.12996 28.20944 28.59330 28.12913 28.21327 27.70413
[603] 27.64227 27.31830 27.25436 27.02737 27.09222 28.00708 28.25534
[610] 28.21327 28.25534 28.08706 27.66206 27.67595 28.62697 28.08706
[617] 28.89498 28.30395 28.04498 28.00291 29.02120 28.22625 28.04498
[624] 29.18949 29.42355 28.68424 28.65125 28.65125 28.38165 28.29742
[631] 27.83034 27.35197 27.25436 28.79222 28.29339 28.12913 28.17120
[638] 27.19498 28.43208 28.84550 29.61780 28.88910 28.38165 27.74620
[645] 28.55962 28.12913 29.06327 27.76240 28.55962 29.54010 28.75253
[652] 29.65665 29.31410 29.10924 28.46410 28.21327 29.10534 28.69010
[659] 28.42050 28.97913 28.76780 28.72895 28.72895 28.69010 28.80665
[666] 29.54010 28.88910 28.61240 28.80665 28.65125 29.61780 29.21167
[673] 28.68424 28.57355 28.57355 28.80665 28.65125 29.65665 29.27996
[680] 28.88910 29.69550 28.36167 29.10534 28.29339 28.12913 28.85291
[687] 29.34824 29.14339 28.78667 28.38165 28.33949 29.18949 29.61780
[694] 29.05944 28.30395 28.17120 28.17120 28.33949 28.97913 28.14855
[701] 28.12913 28.25534 27.91449 28.62697 28.97913 28.84550 29.65665
[708] 28.71839 28.22625 27.23706 27.97465 27.78827 28.59330 27.23706
[715] 28.26994 28.69010 28.34280 28.12913 27.83034 29.44330 29.88500
[722] 29.18166 28.22625 28.04498 28.85291 28.57355 28.22625 28.33949
[729] 28.12913 29.10534 26.87355 27.95244 28.97913 28.14855 29.14742
[736] 28.22625 29.10534 28.14855 29.14742 29.65665 28.71839 29.42355
[743] 28.85496 28.80665 29.54010 28.71839 28.14855 27.66206 27.60859
[750] 28.52595 28.26510 28.25534 27.19498 28.10436 28.25534 27.27913
[757] 27.02737 28.00708 27.57492 28.42492 27.74620 28.59330 28.17120
[764] 28.29742 27.48949 28.43208 28.65125 27.87895 27.18359 28.03951
[771] 27.35197 28.00708 28.45859 28.42050 28.21327 27.91449 27.74330
[778] 27.74330 27.77697 28.62697 28.26510 28.29742 29.14742 28.26510
[785] 28.12913 29.14742 28.14855 28.93706 28.22625 27.61998 28.52595
[792] 28.72895 27.95665 27.57492 28.59330 28.33949 28.25534 27.70413
[799] 28.45859 28.04498 28.25534 27.66206 27.64227 27.25095 28.03951
[806] 27.74330 27.70962 28.59330 28.12913 28.00291 27.78827 26.85962
[813] 27.88150 29.10534 28.22625 27.74620 27.64227 26.79227 27.45650
[820] 28.07193 28.21327 28.25534 28.12913 28.08706 29.06327 28.84550
[827] 28.22625 27.87242 27.57492 28.45859 27.78827 28.59330 28.85291
[834] 28.26510 28.21327 27.66206 28.55962 28.85291 29.65665 27.86839
[841] 28.39124 29.14742 28.14855 28.97913 28.26510 27.40534 26.73208
[848] 27.50970 27.14992 28.10436 27.21727 27.22193 28.03951 27.61998
[855] 27.70962 28.62697 27.84010 26.89330 27.52744 28.52595 27.66206
[862] 28.42492 27.23706 27.87737 28.52595 28.04498 28.21327 28.29742
[869] 28.93706 28.22625 28.97913 28.57355 28.18740 29.14742 27.87895
[876] 28.52595 28.18740 28.17120 28.21327 28.93706 28.38165 29.14742
[883] 29.14339 29.10924 28.25924 27.74620 28.49227 28.33949 28.97913
[890] 27.33740 28.03951 27.36327 27.94222 27.64227 27.18359 27.84494
[897] 28.29742 28.33949 27.40534 27.18951 27.22193 28.00708 27.35197
[904] 27.18951 27.25436 27.05980 28.00708 28.59330 27.40534 27.18951
[911] 28.03951 27.25095 27.12465 28.03951 28.42492 27.57791 26.26624
[918] 26.03084 26.04238 26.08853 26.89238 28.10436 28.59330 27.19498
[925] 27.25436 27.18951 28.07193 26.79227 27.50970 27.94222 26.79227
[932] 26.21697 26.89238 26.24222 26.89238 27.52744 28.03951 27.57492
[939] 28.49227 28.21327 26.85413 27.47423 27.97465 27.60859 26.92697
[946] 27.47423 27.18359 28.07193 28.52595 27.84010 28.62697 28.04498
[953] 27.66206 28.39124 27.19498 28.10436 27.23706 27.90980 27.67595
[960] 27.57492 27.57492 28.62697 27.19498 27.09222 28.03951 26.89330
[967] 27.54518 27.74330 28.45859 27.57791 27.25095 27.87737 28.52595
[974] 28.97913 28.25924 28.29742 28.29742 28.25534 27.70413 27.70962
[981] 28.59330 27.72355 27.31830 28.10436 27.74620 27.74330 27.60859
[988] 27.35197 27.97465 27.57492 27.67595 27.60859 27.57492 28.62697
[995] 28.93706 28.57355 27.72355 28.59330 28.80665 28.65125
[ reached getOption("max.print") -- omitted 7587 entries ]Tabel Pembanding dengan Hasil Penyesuaian Kedua
datapakai = data[c(2:length(data))]
tanggal = data[c(1:length(data))]
galat = abs(datapakai-adj.forecast)
tabel = cbind(datapakai,ramal,adjusted,adj.forecast,galat)
tabel datapakai ramal adjusted adj.forecast galat
[1,] 27.70 28.08706 -0.425 27.66206 0.03794400
[2,] 27.70 27.77697 0.000 27.77697 0.07697450
[3,] 27.70 27.77697 0.000 27.77697 0.07697450
[4,] 27.20 27.77697 0.000 27.77697 0.57697450
[5,] 27.10 27.60859 0.000 27.60859 0.50859450
[6,] 28.30 27.57492 0.850 28.42492 0.12491850
[7,] 28.50 28.21327 0.000 28.21327 0.28672800
[8,] 28.60 28.29742 0.000 28.29742 0.30258400
[9,] 28.70 28.33949 0.850 29.18949 0.48948800
[10,] 27.20 28.57355 -0.850 27.72355 0.52355225
[11,] 28.50 27.60859 0.850 28.45859 0.04140550
[12,] 28.20 28.29742 0.000 28.29742 0.09741600
[13,] 28.60 28.17120 0.000 28.17120 0.42880000
[14,] 27.90 28.33949 0.000 28.33949 0.43948800
[15,] 27.00 28.04498 -0.425 27.61998 0.61998400
[16,] 28.40 27.54124 0.850 28.39124 0.00875750
[17,] 28.50 28.25534 0.000 28.25534 0.24465600
[18,] 27.30 28.29742 -0.425 27.87242 0.57241600
[19,] 27.80 27.64227 0.850 28.49227 0.69227050
[20,] 27.90 28.00291 0.000 28.00291 0.10291200
[21,] 27.20 28.04498 -0.425 27.61998 0.41998400
[22,] 27.40 27.60859 0.000 27.60859 0.20859450
[23,] 26.00 27.67595 -0.850 26.82595 0.82594650
[24,] 25.70 26.69518 0.000 26.69518 0.99517525
[25,] 26.60 26.64197 0.850 27.49197 0.89196725
[26,] 27.90 27.15708 0.850 28.00708 0.10707875
[27,] 27.90 28.04498 0.000 28.04498 0.14498400
[28,] 28.20 28.04498 0.000 28.04498 0.15501600
[29,] 27.00 28.17120 -0.425 27.74620 0.74620000
[30,] 27.30 27.54124 0.000 27.54124 0.24124250
[31,] 26.60 27.64227 -0.425 27.21727 0.61727050
[32,] 27.60 27.15708 0.850 28.00708 0.40707875
[33,] 27.20 27.74330 0.000 27.74330 0.54329850
[34,] 27.90 27.60859 0.850 28.45859 0.55859450
[35,] 28.60 28.04498 0.000 28.04498 0.55501600
[36,] 27.70 28.33949 -0.425 27.91449 0.21448800
[37,] 28.80 27.77697 0.850 28.62697 0.17302550
[38,] 28.90 28.61240 0.000 28.61240 0.28759875
[39,] 29.40 28.65125 0.000 28.65125 0.74874975
[40,] 28.00 28.84550 -0.425 28.42050 0.42049525
[41,] 27.00 28.08706 -0.425 27.66206 0.66205600
[42,] 28.10 27.54124 0.850 28.39124 0.29124250
[43,] 28.80 28.12913 0.850 28.97913 0.17912800
[44,] 28.50 28.61240 -0.425 28.18740 0.31259875
[45,] 29.00 28.29742 0.850 29.14742 0.14741600
[46,] 29.30 28.69010 0.000 28.69010 0.60990075
[47,] 29.30 28.80665 0.000 28.80665 0.49335375
[48,] 29.40 28.80665 0.000 28.80665 0.59335375
[49,] 27.70 28.84550 -0.850 27.99550 0.29549525
[50,] 29.60 27.77697 1.275 29.05197 0.54802550
[51,] 28.90 29.14339 -0.425 28.71839 0.18161475
[52,] 27.60 28.65125 -0.850 27.80125 0.20125025
[53,] 27.30 27.74330 0.000 27.74330 0.44329850
[54,] 27.80 27.64227 0.850 28.49227 0.69227050
[55,] 27.00 28.00291 -0.425 27.57791 0.57791200
[56,] 28.20 27.54124 0.850 28.39124 0.19124250
[57,] 28.20 28.17120 0.000 28.17120 0.02880000
[58,] 28.70 28.17120 0.850 29.02120 0.32120000
[59,] 29.70 28.57355 0.850 29.42355 0.27644775
[60,] 27.70 29.17753 -1.275 27.90253 0.20252825
[61,] 29.00 27.77697 0.850 28.62697 0.37302550
[62,] 27.60 28.69010 -0.850 27.84010 0.24009925
[63,] 28.40 27.74330 0.850 28.59330 0.19329850
[64,] 28.20 28.25534 0.000 28.25534 0.05534400
[65,] 27.30 28.17120 -0.425 27.74620 0.44620000
[66,] 27.30 27.64227 0.000 27.64227 0.34227050
[67,] 27.20 27.64227 0.000 27.64227 0.44227050
[68,] 27.40 27.60859 0.000 27.60859 0.20859450
[69,] 28.50 27.67595 0.850 28.52595 0.02594650
[70,] 28.90 28.29742 0.850 29.14742 0.24741600
[71,] 29.60 28.65125 0.850 29.50125 0.09874975
[72,] 27.30 29.14339 -1.275 27.86839 0.56838525
[73,] 27.00 27.64227 0.000 27.64227 0.64227050
[74,] 27.80 27.54124 0.850 28.39124 0.59124250
[75,] 27.90 28.00291 0.000 28.00291 0.10291200
[76,] 28.40 28.04498 0.000 28.04498 0.35501600
[77,] 27.50 28.25534 -0.425 27.83034 0.33034400
[78,] 27.60 27.70962 0.000 27.70962 0.10962250
[79,] 27.50 27.74330 0.000 27.74330 0.24329850
[80,] 27.70 27.70962 0.000 27.70962 0.00962250
[81,] 28.00 27.77697 0.850 28.62697 0.62697450
[82,] 28.30 28.08706 0.000 28.08706 0.21294400
[83,] 27.50 28.21327 -0.425 27.78827 0.28827200
[84,] 28.90 27.70962 0.850 28.55962 0.34037750
[85,] 27.10 28.65125 -0.850 27.80125 0.70125025
[86,] 26.80 27.57492 -0.425 27.14992 0.34991850
[87,] 27.10 27.22193 0.850 28.07193 0.97193275
[88,] 26.10 27.57492 -0.425 27.14992 1.04991850
[89,] 27.70 26.99494 0.850 27.84494 0.14494375
[90,] 27.60 27.77697 0.000 27.77697 0.17697450
[91,] 27.50 27.74330 0.000 27.74330 0.24329850
[92,] 27.90 27.70962 0.850 28.55962 0.65962250
[93,] 27.40 28.04498 -0.425 27.61998 0.21998400
[94,] 28.10 27.67595 0.850 28.52595 0.42594650
[95,] 27.60 28.12913 -0.425 27.70413 0.10412800
[96,] 28.60 27.74330 0.850 28.59330 0.00670150
[97,] 27.80 28.33949 0.000 28.33949 0.53948800
[98,] 27.40 28.00291 -0.425 27.57791 0.17791200
[99,] 26.80 27.67595 -0.425 27.25095 0.45094650
[100,] 26.80 27.22193 0.000 27.22193 0.42193275
[101,] 26.80 27.22193 0.000 27.22193 0.42193275
[102,] 27.80 27.22193 0.850 28.07193 0.27193275
[103,] 27.50 28.00291 -0.425 27.57791 0.07791200
[104,] 27.10 27.70962 0.000 27.70962 0.60962250
[105,] 26.80 27.57492 -0.425 27.14992 0.34991850
[106,] 27.50 27.22193 0.850 28.07193 0.57193275
[107,] 27.60 27.70962 0.000 27.70962 0.10962250
[108,] 26.70 27.74330 -0.425 27.31830 0.61829850
[109,] 27.60 27.18951 0.850 28.03951 0.43950575
[110,] 28.70 27.74330 0.850 28.59330 0.10670150
[111,] 26.70 28.57355 -1.275 27.29855 0.59855225
[112,] 28.30 27.18951 0.850 28.03951 0.26049425
[113,] 27.20 28.21327 -0.425 27.78827 0.58827200
[114,] 27.20 27.60859 0.000 27.60859 0.40859450
[115,] 26.60 27.60859 -0.425 27.18359 0.58359450
[116,] 27.00 27.15708 0.850 28.00708 1.00707875
[117,] 27.40 27.54124 0.000 27.54124 0.14124250
[118,] 26.80 27.67595 -0.425 27.25095 0.45094650
[119,] 26.90 27.22193 0.000 27.22193 0.32193275
[120,] 27.20 27.25436 0.850 28.10436 0.90435975
[121,] 28.10 27.60859 0.850 28.45859 0.35859450
[122,] 27.90 28.12913 0.000 28.12913 0.22912800
[123,] 27.70 28.04498 -0.425 27.61998 0.08001600
[124,] 27.40 27.77697 0.000 27.77697 0.37697450
[125,] 29.00 27.67595 0.850 28.52595 0.47405350
[126,] 27.30 28.69010 -0.850 27.84010 0.54009925
[127,] 28.50 27.64227 0.850 28.49227 0.00772950
[128,] 27.70 28.29742 -0.425 27.87242 0.17241600
[129,] 28.20 27.77697 0.850 28.62697 0.42697450
[130,] 28.70 28.17120 0.850 29.02120 0.32120000
[131,] 28.10 28.57355 -0.425 28.14855 0.04855225
[132,] 28.50 28.12913 0.000 28.12913 0.37087200
[133,] 27.80 28.29742 0.000 28.29742 0.49741600
[134,] 28.60 28.00291 0.000 28.00291 0.59708800
[135,] 27.50 28.33949 -0.425 27.91449 0.41448800
[136,] 28.70 27.70962 0.850 28.55962 0.14037750
[137,] 27.00 28.57355 -0.850 27.72355 0.72355225
[138,] 27.40 27.54124 0.000 27.54124 0.14124250
[139,] 29.20 27.67595 0.850 28.52595 0.67405350
[140,] 28.80 28.76780 0.000 28.76780 0.03220275
[141,] 28.40 28.61240 -0.425 28.18740 0.21259875
[142,] 28.70 28.25534 0.850 29.10534 0.40534400
[143,] 27.10 28.57355 -0.850 27.72355 0.62355225
[144,] 26.60 27.57492 -0.425 27.14992 0.54991850
[145,] 28.10 27.15708 0.850 28.00708 0.09292125
[146,] 28.60 28.12913 0.000 28.12913 0.47087200
[147,] 28.20 28.33949 0.000 28.33949 0.13948800
[148,] 26.20 28.17120 -0.850 27.32120 1.12120000
[149,] 26.40 27.02737 0.000 27.02737 0.62737075
[150,] 26.50 27.09222 0.000 27.09222 0.59222475
[151,] 27.30 27.12465 0.850 27.97465 0.67465175
[152,] 28.00 27.64227 0.850 28.49227 0.49227050
[153,] 27.20 28.08706 -0.425 27.66206 0.46205600
[154,] 27.30 27.60859 0.000 27.60859 0.30859450
[155,] 27.20 27.64227 0.000 27.64227 0.44227050
[156,] 25.90 27.60859 -0.850 26.75859 0.85859450
[157,] 27.00 26.67744 0.850 27.52744 0.52743925
[158,] 25.80 27.54124 -0.850 26.69124 0.89124250
[159,] 26.40 26.65970 0.850 27.50970 1.10970325
[160,] 26.50 27.09222 0.000 27.09222 0.59222475
[161,] 26.50 27.12465 0.000 27.12465 0.62465175
[162,] 27.70 27.12465 0.850 27.97465 0.27465175
[163,] 27.40 27.77697 0.000 27.77697 0.37697450
[164,] 26.50 27.67595 -0.425 27.25095 0.75094650
[165,] 24.70 27.12465 -0.850 26.27465 1.57465175
[166,] 26.60 26.03084 0.850 26.88084 0.28084425
[167,] 26.80 27.15708 0.000 27.15708 0.35707875
[168,] 25.50 27.22193 -0.425 26.79693 1.29693275
[169,] 26.70 26.60650 0.850 27.45650 0.75649525
[170,] 26.30 27.18951 0.000 27.18951 0.88950575
[171,] 24.50 27.05980 -0.850 26.20980 1.70979775
[172,] 26.10 26.00777 0.850 26.85777 0.75776825
[173,] 28.10 26.99494 0.850 27.84494 0.25505625
[174,] 27.40 28.12913 -0.425 27.70413 0.30412800
[175,] 27.70 27.67595 0.000 27.67595 0.02405350
[176,] 27.00 27.77697 0.000 27.77697 0.77697450
[177,] 28.20 27.54124 0.850 28.39124 0.19124250
[178,] 27.90 28.17120 0.000 28.17120 0.27120000
[179,] 26.60 28.04498 -0.850 27.19498 0.59498400
[180,] 27.40 27.15708 0.850 28.00708 0.60707875
[181,] 26.20 27.67595 -0.425 27.25095 1.05094650
[182,] 25.60 27.02737 -0.425 26.60237 1.00237075
[183,] 26.40 26.62423 0.850 27.47423 1.07423125
[184,] 25.10 27.09222 -0.850 26.24222 1.14222475
[185,] 24.70 26.07700 0.000 26.07700 1.37699625
[186,] 28.10 26.03084 1.700 27.73084 0.36915575
[187,] 28.10 28.12913 0.000 28.12913 0.02912800
[188,] 28.50 28.12913 0.000 28.12913 0.37087200
[189,] 27.90 28.29742 0.000 28.29742 0.39741600
[190,] 27.10 28.04498 -0.425 27.61998 0.51998400
[191,] 27.50 27.57492 0.000 27.57492 0.07491850
[192,] 27.60 27.70962 0.000 27.70962 0.10962250
[193,] 28.80 27.74330 0.850 28.59330 0.20670150
[194,] 28.70 28.61240 0.000 28.61240 0.08759875
[195,] 28.00 28.57355 -0.425 28.14855 0.14855225
[196,] 28.10 28.08706 0.000 28.08706 0.01294400
[197,] 26.90 28.12913 -0.850 27.27913 0.37912800
[198,] 27.30 27.25436 0.850 28.10436 0.80435975
[199,] 26.60 27.64227 -0.425 27.21727 0.61727050
[200,] 28.30 27.15708 0.850 28.00708 0.29292125
[ reached getOption("max.print") -- omitted 8387 rows ]Penyesuaian Overestimate Ramalan
adj.forecast2 = rep(0,length(datapakai))
for(i in 1:length(datapakai)){
if((ramal[i]-datapakai[i])<(adj.forecast[i]-datapakai[i])) {
adj.forecast2[i]=ramal[i]+(L/2)
} else {
adj.forecast2[i]=adj.forecast[i]}
}
adj.forecast2 [1] 27.66206 27.77697 27.77697 27.77697 27.60859 27.99992 28.21327
[8] 28.29742 28.76449 27.72355 28.03359 28.29742 28.17120 28.33949
[15] 27.61998 27.96624 28.25534 27.87242 28.06727 28.00291 27.61998
[22] 27.60859 26.82595 26.69518 27.06697 27.58208 28.04498 28.04498
[29] 27.74620 27.54124 27.21727 27.58208 27.74330 28.03359 28.04498
[36] 27.91449 28.20197 28.61240 28.65125 28.42050 27.66206 27.96624
[43] 28.55413 28.18740 28.72242 28.69010 28.80665 28.80665 27.99550
[50] 28.20197 28.71839 27.80125 27.74330 28.06727 27.57791 27.96624
[57] 28.17120 28.59620 28.99855 27.90253 28.20197 27.84010 28.16830
[64] 28.25534 27.74620 27.64227 27.64227 27.60859 28.10095 28.72242
[71] 29.07625 27.86839 27.64227 27.96624 28.00291 28.04498 27.83034
[78] 27.70962 27.74330 27.70962 28.20197 28.08706 27.78827 28.13462
[85] 27.80125 27.14992 27.64693 27.14992 27.41994 27.77697 27.74330
[92] 28.13462 27.61998 28.10095 27.70413 28.16830 28.33949 27.57791
[99] 27.25095 27.22193 27.22193 27.64693 27.57791 27.70962 27.14992
[106] 27.64693 27.70962 27.31830 27.61451 28.16830 27.29855 27.61451
[113] 27.78827 27.60859 27.18359 27.58208 27.54124 27.25095 27.22193
[120] 27.67936 28.03359 28.12913 27.61998 27.77697 28.10095 27.84010
[127] 28.06727 27.87242 28.20197 28.59620 28.14855 28.12913 28.29742
[134] 28.00291 27.91449 28.13462 27.72355 27.54124 28.10095 28.76780
[141] 28.18740 28.68034 27.72355 27.14992 27.58208 28.12913 28.33949
[148] 27.32120 27.02737 27.09222 27.54965 28.06727 27.66206 27.60859
[155] 27.64227 26.75859 27.10244 26.69124 27.08470 27.09222 27.12465
[162] 27.54965 27.77697 27.25095 26.27465 26.45584 27.15708 26.79693
[169] 27.03150 27.18951 26.20980 26.43277 27.41994 27.70413 27.67595
[176] 27.77697 27.96624 28.17120 27.19498 27.58208 27.25095 26.60237
[183] 27.04923 26.24222 26.07700 26.45584 28.12913 28.12913 28.29742
[190] 27.61998 27.57492 27.70962 28.16830 28.61240 28.14855 28.08706
[197] 27.27913 27.67936 27.21727 27.58208 28.21327 27.36327 26.73208
[204] 26.99602 27.54124 27.28462 27.25436 27.45237 27.60859 27.21727
[211] 27.64693 27.25095 27.18951 27.15708 27.09222 27.48480 27.67595
[218] 27.70962 28.06727 27.66206 27.64227 27.28462 26.30708 26.50200
[225] 27.10244 27.57791 27.70962 27.57492 27.70962 27.67595 28.13462
[232] 27.15291 27.15708 27.67936 27.67595 27.70962 27.74330 28.16830
[239] 28.04498 28.42791 28.42050 28.33949 27.23706 27.61451 28.20197
[246] 27.74620 28.10095 28.26510 28.08706 27.74620 27.35197 27.48480
[253] 27.96624 28.00291 27.57791 27.77697 28.06727 28.00291 28.17120
[260] 28.68034 28.22625 28.21327 27.61998 27.21727 27.15708 27.09222
[267] 27.64693 27.74620 28.16830 28.04498 27.15291 26.76451 27.01376
[274] 27.61451 27.66206 28.10095 28.00291 28.25534 28.04498 27.57791
[281] 27.60859 28.06727 28.59620 28.72895 28.22625 28.51206 28.29339
[288] 28.68034 29.48444 27.83424 28.10095 28.08706 28.72242 28.68424
[295] 27.99550 28.20197 28.65125 28.99855 29.31410 29.34824 28.42996
[302] 28.29742 28.51206 27.72355 26.33359 26.51353 28.17120 28.63827
[309] 28.14855 28.25534 28.08706 28.04498 27.61998 27.28462 27.64693
[316] 27.99992 27.48949 27.58208 27.77697 27.77697 28.10095 28.51206
[323] 28.22625 27.91449 27.31830 27.58208 27.99992 28.18740 28.08706
[330] 28.46998 27.76240 27.70962 28.13462 28.63827 28.26510 28.00291
[337] 28.59620 27.87895 28.20197 27.66206 28.10095 28.12913 28.29742
[344] 28.21327 27.32120 27.67936 27.66206 28.10095 28.00291 28.08706
[351] 27.44742 27.41994 28.72242 28.34280 28.08706 28.08706 28.72242
[358] 28.65125 27.72355 27.74330 27.70962 27.64227 28.13462 28.08706
[365] 27.27913 27.61451 28.03359 28.72242 28.61240 28.42050 28.25534
[372] 28.21327 28.00291 27.61998 28.10095 28.04498 28.04498 27.44742
[379] 27.64693 27.25095 27.12465 27.64693 28.06727 27.61998 27.60859
[386] 27.35197 27.54965 27.99992 27.23706 27.22193 27.18951 27.25436
[393] 27.64693 26.69124 27.08470 27.54124 27.74330 27.99992 27.87242
[400] 27.60859 28.20197 28.25534 27.83034 27.77697 27.96624 27.87242
[407] 28.20197 27.78827 27.18359 26.99494 27.64693 27.57492 28.16830
[414] 28.29742 28.12913 28.21327 27.66206 27.99992 28.32753 28.51206
[421] 27.76240 28.20197 27.61998 28.03359 28.00291 27.48949 27.54965
[428] 28.63827 28.72895 28.65125 28.18740 27.66206 27.57492 27.35197
[435] 27.02737 27.51722 28.17120 27.15291 27.58208 28.06727 28.12913
[442] 28.21327 28.33949 28.00291 27.83034 28.13462 27.78827 28.03359
[449] 28.00291 27.66206 28.13462 27.91449 27.67595 28.20197 27.61998
[456] 28.03359 27.57791 27.18359 27.67936 28.03359 28.33949 27.83034
[463] 27.64227 28.20197 28.46998 28.38165 28.00291 27.61998 27.25095
[470] 27.51722 27.67595 28.03359 28.18740 28.04498 28.04498 28.12913
[477] 27.91449 28.20197 27.87242 27.14992 27.58208 27.70413 28.03359
[484] 27.91449 27.28462 27.25436 27.58208 27.96624 27.95665 27.99992
[491] 27.74620 27.25095 27.12465 27.02737 27.22193 27.64693 27.35197
[498] 27.67936 28.46998 28.14855 28.25534 28.25534 28.59620 28.22625
[505] 28.51206 28.22625 28.17120 28.51206 28.14855 28.08706 28.04498
[512] 28.72242 28.14855 28.72242 28.26510 28.72242 28.18740 27.74620
[519] 28.10095 26.87355 27.12018 27.64693 28.00291 26.98034 27.10244
[526] 27.18359 27.12465 26.69965 27.12018 27.64227 27.99992 26.76998
[533] 27.12018 26.33951 26.06546 26.51353 27.18359 27.61451 27.28462
[540] 27.51722 27.60859 27.11624 27.51722 27.31830 27.58208 27.67595
[547] 27.18359 26.24222 26.51353 26.99602 27.74330 27.25095 27.58208
[554] 27.96624 28.25534 28.04498 28.33949 28.21327 28.12913 28.25534
[561] 28.29742 27.87242 27.70962 28.13462 27.78827 27.74330 27.70962
[568] 28.13462 28.17120 27.66206 27.74330 27.64227 27.99992 28.04498
[575] 28.76449 28.26510 28.17120 28.29742 28.21327 28.00291 28.17120
[582] 28.21327 27.83034 27.99992 27.44742 27.51722 27.45395 26.14494
[589] 26.45584 27.22193 27.58208 27.60859 27.14992 27.67936 27.67595
[596] 28.03359 29.70496 28.20944 28.16830 28.12913 28.21327 27.70413
[603] 27.64227 27.31830 27.25436 27.02737 27.09222 27.58208 28.25534
[610] 28.21327 28.25534 28.08706 27.66206 27.67595 28.20197 28.08706
[617] 28.46998 28.30395 28.04498 28.00291 28.59620 28.22625 28.04498
[624] 28.76449 28.99855 28.68424 28.65125 28.65125 28.38165 28.29742
[631] 27.83034 27.35197 27.25436 27.51722 28.29339 28.12913 28.17120
[638] 27.19498 27.58208 28.84550 29.19280 28.88910 28.38165 27.74620
[645] 28.13462 28.12913 28.63827 27.76240 28.13462 29.11510 28.75253
[652] 29.23165 29.31410 29.10924 28.46410 28.21327 28.68034 28.69010
[659] 28.42050 28.55413 28.76780 28.72895 28.72895 28.69010 28.80665
[666] 29.11510 28.88910 28.61240 28.80665 28.65125 29.19280 29.21167
[673] 28.68424 28.57355 28.57355 28.80665 28.65125 29.23165 29.27996
[680] 28.88910 29.27050 28.36167 28.68034 28.29339 28.12913 28.42791
[687] 29.34824 29.14339 28.78667 28.38165 28.33949 28.76449 29.19280
[694] 29.05944 28.30395 28.17120 28.17120 28.33949 28.55413 28.14855
[701] 28.12913 28.25534 27.91449 28.20197 28.55413 28.84550 29.23165
[708] 28.71839 28.22625 27.23706 27.54965 27.78827 28.16830 27.23706
[715] 27.41994 28.69010 28.34280 28.12913 27.83034 28.16830 29.88500
[722] 29.18166 28.22625 28.04498 28.42791 28.57355 28.22625 28.33949
[729] 28.12913 28.68034 26.87355 27.10244 28.55413 28.14855 28.72242
[736] 28.22625 28.68034 28.14855 28.72242 29.23165 28.71839 28.99855
[743] 28.85496 28.80665 29.11510 28.71839 28.14855 27.66206 27.60859
[750] 28.10095 28.26510 28.25534 27.19498 27.67936 28.25534 27.27913
[757] 27.02737 27.58208 27.57492 27.99992 27.74620 28.16830 28.17120
[764] 28.29742 27.48949 27.58208 28.65125 27.87895 27.18359 27.61451
[771] 27.35197 27.58208 28.03359 28.42050 28.21327 27.91449 27.74330
[778] 27.74330 27.77697 28.20197 28.26510 28.29742 28.72242 28.26510
[785] 28.12913 28.72242 28.14855 28.51206 28.22625 27.61998 28.10095
[792] 28.72895 27.95665 27.57492 28.16830 28.33949 28.25534 27.70413
[799] 28.03359 28.04498 28.25534 27.66206 27.64227 27.25095 27.61451
[806] 27.74330 27.70962 28.16830 28.12913 28.00291 27.78827 26.85962
[813] 27.03150 28.68034 28.22625 27.74620 27.64227 26.79227 27.03150
[820] 27.64693 28.21327 28.25534 28.12913 28.08706 28.63827 28.84550
[827] 28.22625 27.87242 27.57492 28.03359 27.78827 28.16830 28.42791
[834] 28.26510 28.21327 27.66206 28.13462 28.42791 29.23165 27.86839
[841] 27.96624 28.72242 28.14855 28.55413 28.26510 27.40534 26.73208
[848] 27.08470 27.14992 27.67936 27.21727 27.22193 27.61451 27.61998
[855] 27.70962 28.20197 27.84010 26.89330 27.10244 28.10095 27.66206
[862] 27.99992 27.23706 27.45237 28.10095 28.04498 28.21327 28.29742
[869] 28.51206 28.22625 28.55413 28.57355 28.18740 28.72242 27.87895
[876] 28.10095 28.18740 28.17120 28.21327 28.51206 28.38165 28.72242
[883] 29.14339 29.10924 28.25924 27.74620 28.06727 28.33949 28.55413
[890] 27.33740 27.61451 27.36327 27.51722 27.64227 27.18359 27.41994
[897] 28.29742 28.33949 27.40534 27.18951 27.22193 27.58208 27.35197
[904] 27.18951 27.25436 27.05980 27.58208 28.16830 27.40534 27.18951
[911] 27.61451 27.25095 27.12465 27.61451 27.99992 27.57791 26.26624
[918] 26.03084 26.04238 26.08853 26.46738 27.67936 28.16830 27.19498
[925] 27.25436 27.18951 27.64693 26.79227 27.08470 27.51722 26.79227
[932] 26.21697 26.46738 26.24222 26.46738 27.10244 27.61451 27.57492
[939] 28.06727 28.21327 26.85413 27.04923 27.54965 27.60859 26.92697
[946] 27.04923 27.18359 27.64693 28.10095 27.84010 28.20197 28.04498
[953] 27.66206 27.96624 27.19498 27.67936 27.23706 27.48480 27.67595
[960] 27.57492 27.57492 28.20197 27.19498 27.09222 27.61451 26.89330
[967] 27.12018 27.74330 28.03359 27.57791 27.25095 27.45237 28.10095
[974] 28.55413 28.25924 28.29742 28.29742 28.25534 27.70413 27.70962
[981] 28.16830 27.72355 27.31830 27.67936 27.74620 27.74330 27.60859
[988] 27.35197 27.54965 27.57492 27.67595 27.60859 27.57492 28.20197
[995] 28.51206 28.57355 27.72355 28.16830 28.80665 28.65125
[ reached getOption("max.print") -- omitted 7587 entries ]Tabel Pembanding dengan Hasil Penyesuaian Kedua
tabel2 = cbind(datapakai,ramal,adj.forecast,adj.forecast2)
tabel2 datapakai ramal adj.forecast adj.forecast2
[1,] 27.70 28.08706 27.66206 27.66206
[2,] 27.70 27.77697 27.77697 27.77697
[3,] 27.70 27.77697 27.77697 27.77697
[4,] 27.20 27.77697 27.77697 27.77697
[5,] 27.10 27.60859 27.60859 27.60859
[6,] 28.30 27.57492 28.42492 27.99992
[7,] 28.50 28.21327 28.21327 28.21327
[8,] 28.60 28.29742 28.29742 28.29742
[9,] 28.70 28.33949 29.18949 28.76449
[10,] 27.20 28.57355 27.72355 27.72355
[11,] 28.50 27.60859 28.45859 28.03359
[12,] 28.20 28.29742 28.29742 28.29742
[13,] 28.60 28.17120 28.17120 28.17120
[14,] 27.90 28.33949 28.33949 28.33949
[15,] 27.00 28.04498 27.61998 27.61998
[16,] 28.40 27.54124 28.39124 27.96624
[17,] 28.50 28.25534 28.25534 28.25534
[18,] 27.30 28.29742 27.87242 27.87242
[19,] 27.80 27.64227 28.49227 28.06727
[20,] 27.90 28.00291 28.00291 28.00291
[21,] 27.20 28.04498 27.61998 27.61998
[22,] 27.40 27.60859 27.60859 27.60859
[23,] 26.00 27.67595 26.82595 26.82595
[24,] 25.70 26.69518 26.69518 26.69518
[25,] 26.60 26.64197 27.49197 27.06697
[26,] 27.90 27.15708 28.00708 27.58208
[27,] 27.90 28.04498 28.04498 28.04498
[28,] 28.20 28.04498 28.04498 28.04498
[29,] 27.00 28.17120 27.74620 27.74620
[30,] 27.30 27.54124 27.54124 27.54124
[31,] 26.60 27.64227 27.21727 27.21727
[32,] 27.60 27.15708 28.00708 27.58208
[33,] 27.20 27.74330 27.74330 27.74330
[34,] 27.90 27.60859 28.45859 28.03359
[35,] 28.60 28.04498 28.04498 28.04498
[36,] 27.70 28.33949 27.91449 27.91449
[37,] 28.80 27.77697 28.62697 28.20197
[38,] 28.90 28.61240 28.61240 28.61240
[39,] 29.40 28.65125 28.65125 28.65125
[40,] 28.00 28.84550 28.42050 28.42050
[41,] 27.00 28.08706 27.66206 27.66206
[42,] 28.10 27.54124 28.39124 27.96624
[43,] 28.80 28.12913 28.97913 28.55413
[44,] 28.50 28.61240 28.18740 28.18740
[45,] 29.00 28.29742 29.14742 28.72242
[46,] 29.30 28.69010 28.69010 28.69010
[47,] 29.30 28.80665 28.80665 28.80665
[48,] 29.40 28.80665 28.80665 28.80665
[49,] 27.70 28.84550 27.99550 27.99550
[50,] 29.60 27.77697 29.05197 28.20197
[51,] 28.90 29.14339 28.71839 28.71839
[52,] 27.60 28.65125 27.80125 27.80125
[53,] 27.30 27.74330 27.74330 27.74330
[54,] 27.80 27.64227 28.49227 28.06727
[55,] 27.00 28.00291 27.57791 27.57791
[56,] 28.20 27.54124 28.39124 27.96624
[57,] 28.20 28.17120 28.17120 28.17120
[58,] 28.70 28.17120 29.02120 28.59620
[59,] 29.70 28.57355 29.42355 28.99855
[60,] 27.70 29.17753 27.90253 27.90253
[61,] 29.00 27.77697 28.62697 28.20197
[62,] 27.60 28.69010 27.84010 27.84010
[63,] 28.40 27.74330 28.59330 28.16830
[64,] 28.20 28.25534 28.25534 28.25534
[65,] 27.30 28.17120 27.74620 27.74620
[66,] 27.30 27.64227 27.64227 27.64227
[67,] 27.20 27.64227 27.64227 27.64227
[68,] 27.40 27.60859 27.60859 27.60859
[69,] 28.50 27.67595 28.52595 28.10095
[70,] 28.90 28.29742 29.14742 28.72242
[71,] 29.60 28.65125 29.50125 29.07625
[72,] 27.30 29.14339 27.86839 27.86839
[73,] 27.00 27.64227 27.64227 27.64227
[74,] 27.80 27.54124 28.39124 27.96624
[75,] 27.90 28.00291 28.00291 28.00291
[76,] 28.40 28.04498 28.04498 28.04498
[77,] 27.50 28.25534 27.83034 27.83034
[78,] 27.60 27.70962 27.70962 27.70962
[79,] 27.50 27.74330 27.74330 27.74330
[80,] 27.70 27.70962 27.70962 27.70962
[81,] 28.00 27.77697 28.62697 28.20197
[82,] 28.30 28.08706 28.08706 28.08706
[83,] 27.50 28.21327 27.78827 27.78827
[84,] 28.90 27.70962 28.55962 28.13462
[85,] 27.10 28.65125 27.80125 27.80125
[86,] 26.80 27.57492 27.14992 27.14992
[87,] 27.10 27.22193 28.07193 27.64693
[88,] 26.10 27.57492 27.14992 27.14992
[89,] 27.70 26.99494 27.84494 27.41994
[90,] 27.60 27.77697 27.77697 27.77697
[91,] 27.50 27.74330 27.74330 27.74330
[92,] 27.90 27.70962 28.55962 28.13462
[93,] 27.40 28.04498 27.61998 27.61998
[94,] 28.10 27.67595 28.52595 28.10095
[95,] 27.60 28.12913 27.70413 27.70413
[96,] 28.60 27.74330 28.59330 28.16830
[97,] 27.80 28.33949 28.33949 28.33949
[98,] 27.40 28.00291 27.57791 27.57791
[99,] 26.80 27.67595 27.25095 27.25095
[100,] 26.80 27.22193 27.22193 27.22193
[101,] 26.80 27.22193 27.22193 27.22193
[102,] 27.80 27.22193 28.07193 27.64693
[103,] 27.50 28.00291 27.57791 27.57791
[104,] 27.10 27.70962 27.70962 27.70962
[105,] 26.80 27.57492 27.14992 27.14992
[106,] 27.50 27.22193 28.07193 27.64693
[107,] 27.60 27.70962 27.70962 27.70962
[108,] 26.70 27.74330 27.31830 27.31830
[109,] 27.60 27.18951 28.03951 27.61451
[110,] 28.70 27.74330 28.59330 28.16830
[111,] 26.70 28.57355 27.29855 27.29855
[112,] 28.30 27.18951 28.03951 27.61451
[113,] 27.20 28.21327 27.78827 27.78827
[114,] 27.20 27.60859 27.60859 27.60859
[115,] 26.60 27.60859 27.18359 27.18359
[116,] 27.00 27.15708 28.00708 27.58208
[117,] 27.40 27.54124 27.54124 27.54124
[118,] 26.80 27.67595 27.25095 27.25095
[119,] 26.90 27.22193 27.22193 27.22193
[120,] 27.20 27.25436 28.10436 27.67936
[121,] 28.10 27.60859 28.45859 28.03359
[122,] 27.90 28.12913 28.12913 28.12913
[123,] 27.70 28.04498 27.61998 27.61998
[124,] 27.40 27.77697 27.77697 27.77697
[125,] 29.00 27.67595 28.52595 28.10095
[126,] 27.30 28.69010 27.84010 27.84010
[127,] 28.50 27.64227 28.49227 28.06727
[128,] 27.70 28.29742 27.87242 27.87242
[129,] 28.20 27.77697 28.62697 28.20197
[130,] 28.70 28.17120 29.02120 28.59620
[131,] 28.10 28.57355 28.14855 28.14855
[132,] 28.50 28.12913 28.12913 28.12913
[133,] 27.80 28.29742 28.29742 28.29742
[134,] 28.60 28.00291 28.00291 28.00291
[135,] 27.50 28.33949 27.91449 27.91449
[136,] 28.70 27.70962 28.55962 28.13462
[137,] 27.00 28.57355 27.72355 27.72355
[138,] 27.40 27.54124 27.54124 27.54124
[139,] 29.20 27.67595 28.52595 28.10095
[140,] 28.80 28.76780 28.76780 28.76780
[141,] 28.40 28.61240 28.18740 28.18740
[142,] 28.70 28.25534 29.10534 28.68034
[143,] 27.10 28.57355 27.72355 27.72355
[144,] 26.60 27.57492 27.14992 27.14992
[145,] 28.10 27.15708 28.00708 27.58208
[146,] 28.60 28.12913 28.12913 28.12913
[147,] 28.20 28.33949 28.33949 28.33949
[148,] 26.20 28.17120 27.32120 27.32120
[149,] 26.40 27.02737 27.02737 27.02737
[150,] 26.50 27.09222 27.09222 27.09222
[151,] 27.30 27.12465 27.97465 27.54965
[152,] 28.00 27.64227 28.49227 28.06727
[153,] 27.20 28.08706 27.66206 27.66206
[154,] 27.30 27.60859 27.60859 27.60859
[155,] 27.20 27.64227 27.64227 27.64227
[156,] 25.90 27.60859 26.75859 26.75859
[157,] 27.00 26.67744 27.52744 27.10244
[158,] 25.80 27.54124 26.69124 26.69124
[159,] 26.40 26.65970 27.50970 27.08470
[160,] 26.50 27.09222 27.09222 27.09222
[161,] 26.50 27.12465 27.12465 27.12465
[162,] 27.70 27.12465 27.97465 27.54965
[163,] 27.40 27.77697 27.77697 27.77697
[164,] 26.50 27.67595 27.25095 27.25095
[165,] 24.70 27.12465 26.27465 26.27465
[166,] 26.60 26.03084 26.88084 26.45584
[167,] 26.80 27.15708 27.15708 27.15708
[168,] 25.50 27.22193 26.79693 26.79693
[169,] 26.70 26.60650 27.45650 27.03150
[170,] 26.30 27.18951 27.18951 27.18951
[171,] 24.50 27.05980 26.20980 26.20980
[172,] 26.10 26.00777 26.85777 26.43277
[173,] 28.10 26.99494 27.84494 27.41994
[174,] 27.40 28.12913 27.70413 27.70413
[175,] 27.70 27.67595 27.67595 27.67595
[176,] 27.00 27.77697 27.77697 27.77697
[177,] 28.20 27.54124 28.39124 27.96624
[178,] 27.90 28.17120 28.17120 28.17120
[179,] 26.60 28.04498 27.19498 27.19498
[180,] 27.40 27.15708 28.00708 27.58208
[181,] 26.20 27.67595 27.25095 27.25095
[182,] 25.60 27.02737 26.60237 26.60237
[183,] 26.40 26.62423 27.47423 27.04923
[184,] 25.10 27.09222 26.24222 26.24222
[185,] 24.70 26.07700 26.07700 26.07700
[186,] 28.10 26.03084 27.73084 26.45584
[187,] 28.10 28.12913 28.12913 28.12913
[188,] 28.50 28.12913 28.12913 28.12913
[189,] 27.90 28.29742 28.29742 28.29742
[190,] 27.10 28.04498 27.61998 27.61998
[191,] 27.50 27.57492 27.57492 27.57492
[192,] 27.60 27.70962 27.70962 27.70962
[193,] 28.80 27.74330 28.59330 28.16830
[194,] 28.70 28.61240 28.61240 28.61240
[195,] 28.00 28.57355 28.14855 28.14855
[196,] 28.10 28.08706 28.08706 28.08706
[197,] 26.90 28.12913 27.27913 27.27913
[198,] 27.30 27.25436 28.10436 27.67936
[199,] 26.60 27.64227 27.21727 27.21727
[200,] 28.30 27.15708 28.00708 27.58208
[201,] 28.30 28.21327 28.21327 28.21327
[202,] 26.60 28.21327 27.36327 27.36327
[203,] 25.30 27.15708 26.73208 26.73208
[204,] 27.00 26.57102 27.42102 26.99602
[205,] 27.50 27.54124 27.54124 27.54124
[206,] 26.90 27.70962 27.28462 27.28462
[207,] 26.20 27.25436 27.25436 27.25436
[208,] 27.20 27.02737 27.87737 27.45237
[209,] 27.30 27.60859 27.60859 27.60859
[210,] 26.80 27.64227 27.21727 27.21727
[211,] 27.40 27.22193 28.07193 27.64693
[212,] 26.70 27.67595 27.25095 27.25095
[213,] 26.60 27.18951 27.18951 27.18951
[214,] 26.40 27.15708 27.15708 27.15708
[215,] 26.30 27.09222 27.09222 27.09222
[216,] 27.40 27.05980 27.90980 27.48480
[217,] 27.50 27.67595 27.67595 27.67595
[218,] 27.30 27.70962 27.70962 27.70962
[219,] 28.00 27.64227 28.49227 28.06727
[220,] 27.30 28.08706 27.66206 27.66206
[221,] 27.50 27.64227 27.64227 27.64227
[222,] 26.60 27.70962 27.28462 27.28462
[223,] 25.10 27.15708 26.30708 26.30708
[224,] 25.90 26.07700 26.92700 26.50200
[225,] 27.80 26.67744 27.95244 27.10244
[226,] 27.50 28.00291 27.57791 27.57791
[227,] 27.10 27.70962 27.70962 27.70962
[228,] 27.50 27.57492 27.57492 27.57492
[229,] 27.40 27.70962 27.70962 27.70962
[230,] 27.50 27.67595 27.67595 27.67595
[231,] 27.80 27.70962 28.55962 28.13462
[232,] 26.60 28.00291 27.15291 27.15291
[233,] 26.90 27.15708 27.15708 27.15708
[234,] 27.40 27.25436 28.10436 27.67936
[235,] 27.50 27.67595 27.67595 27.67595
[236,] 27.60 27.70962 27.70962 27.70962
[237,] 27.60 27.74330 27.74330 27.74330
[238,] 27.90 27.74330 28.59330 28.16830
[239,] 27.80 28.04498 28.04498 28.04498
[240,] 29.40 28.00291 28.85291 28.42791
[241,] 28.60 28.84550 28.42050 28.42050
[242,] 28.00 28.33949 28.33949 28.33949
[243,] 26.70 28.08706 27.23706 27.23706
[244,] 27.70 27.18951 28.03951 27.61451
[245,] 28.20 27.77697 28.62697 28.20197
[246,] 27.40 28.17120 27.74620 27.74620
[247,] 29.00 27.67595 28.52595 28.10095
[248,] 28.00 28.69010 28.26510 28.26510
[249,] 28.20 28.08706 28.08706 28.08706
[250,] 27.70 28.17120 27.74620 27.74620
[ reached getOption("max.print") -- omitted 8337 rows ]tail(tabel2, 15) datapakai ramal adj.forecast adj.forecast2
[8573,] 27.2 28.65125 27.80125 27.80125
[8574,] 27.1 27.60859 27.60859 27.60859
[8575,] 26.9 27.57492 27.14992 27.14992
[8576,] 28.8 27.25436 28.52936 27.67936
[8577,] 29.0 28.61240 28.61240 28.61240
[8578,] 31.0 28.69010 29.54010 29.11510
[8579,] 29.6 30.03166 29.60666 29.60666
[8580,] 29.0 29.14339 28.71839 28.71839
[8581,] 27.2 28.69010 27.84010 27.84010
[8582,] 27.1 27.60859 27.60859 27.60859
[8583,] 29.0 27.57492 28.42492 27.99992
[8584,] 26.7 28.69010 27.41510 27.41510
[8585,] 26.6 27.18951 27.18951 27.18951
[8586,] 28.7 27.15708 28.43208 27.58208
[8587,] 30.0 28.57355 29.42355 28.99855datauji_excluded <- datauji[-1,]
tanggal = datauji_excluded$date
tabel_prediksi <- data.frame(tanggal, datapakai, ramal, adj.forecast, adj.forecast2)
tabel_prediksiPeramalan 7 hari kedepan sejak 6 Juli 2024
# Membuat fungsi untuk melakukan peramalan menggunakan model FTSMC
forecast_ftsmc <- function(data, n.tengah, bobot, L, box1, periode) {
# Fuzifikasi data
fuzifikasi <- rep(NA, length(data))
for (i in 1:length(data)) {
for (j in 1:nrow(box1)) {
if (data[i] >= (box1[j,1]) & data[i] < (box1[j,2])) {
fuzifikasi[i] = j
break
} else if (i == length(data) && data[i] == box1[j,2]) {
fuzifikasi[i] = j
break
}
}
}
# Memastikan tidak ada nilai NA dalam fuzifikasi
if (any(is.na(fuzifikasi))) {
stop("Fuzifikasi menghasilkan nilai NA. Pastikan semua data berada dalam rentang interval yang benar.")
}
# Inisialisasi variabel ramalan
ramal <- numeric(length(data) + periode)
ramal[1:length(data)] <- data
# Melakukan peramalan untuk periode ke depan
for (i in (length(data) + 1):(length(data) + periode)) {
for (j in 1:(nrow(bobot))) {
if (!is.na(fuzifikasi[i-1]) && fuzifikasi[i-1] == j) {
ramal[i] = (diagonal[j] * ramal[i-1]) + sum(pinggir[j,] * n.tengah[,1])
}
}
# Fuzifikasi nilai ramalan untuk periode berikutnya
for (j in 1:nrow(box1)) {
if (ramal[i] >= (box1[j,1]) & ramal[i] < (box1[j,2])) {
fuzifikasi[i] = j
break
} else if (i == length(ramal) && ramal[i] == box1[j,2]) {
fuzifikasi[i] = j
break
}
}
}
# Mengembalikan hasil ramalan
return(ramal[(length(data) + 1):(length(data) + periode)])
}
# Menentukan periode peramalan
periode_peramalan <- 7
# Menentukan nilai diagonal dan pinggir
diagonal <- diag(bobot)
m.diagonal <- diag(diagonal)
pinggir <- bobot - m.diagonal
# Menghitung nilai ramalan 7 hari ke depan
ramalan_7_hari <- forecast_ftsmc(data = adj.forecast2, n.tengah = n.tengah, bobot = bobot, L = L, box1 = box1, periode = periode_peramalan)
# Menampilkan hasil ramalan
ramalan_7_hari[1] 28.68954 28.56949 28.32665 28.22448 28.18150 28.16342 28.15581# Gabungkan tabel2 dan data perkiraan baru, dan sertakan kolom tanggal
tabel2_new <- rbind(tabel2, data.frame(datapakai=NA, ramal=NA, adj.forecast=NA, adj.forecast2=ramalan_7_hari))
# Membuat urutan tanggal untuk baris baru
tanggal_terakhir <- max(tanggal)
tanggal_lanjut <- seq(tanggal_terakhir + 1, by = "day", length.out = nrow(tabel2_new) - length(tanggal))
# Tambahkan kolom tanggal ke tabel baru
tabel2_new$tanggal <- c(tanggal, tanggal_lanjut)
# Mengganti nama kolom
colnames(tabel2_new) <- c("Actual", "Initial Forecast", "Adjusted Forecast", "Final Adjusted Forecast", "Date")
# Hitung kolom kesalahan (error)
tabel2_new$Error <- abs(tabel2_new$Actual - tabel2_new$`Final Adjusted Forecast`)
# Susun ulang kolom untuk menempatkan 'tanggal' sebelum 'Aktual'
tabel2_new <- tabel2_new[, c("Date", "Actual", "Initial Forecast", "Adjusted Forecast", "Final Adjusted Forecast", "Error")]
# Menampilkan tabel
tabel2_newNAtail(tabel2_new, 10)Uji Ketepatan
galat2 = abs(datapakai-adj.forecast2)
MSE = mean(galat2^2, na.rm = TRUE)
MAE = mean(abs(galat2), na.rm = TRUE)
MAPE = mean(abs(galat2/datapakai*100), na.rm=TRUE)
ketepatan = cbind(MSE,MAE,MAPE)
ketepatan MSE MAE MAPE
[1,] 0.3440946 0.448634 1.599491Plot Data Aktual dan Data Prediksi
# Ubah kolom tanggal ke format Tanggal
datauji_excluded <- datauji[-1,]
datauji_excluded$date <- as.Date(datauji_excluded$date, format = "%d-%m-%Y")
tabel2_new$Date <- as.Date(tabel2_new$Date, format = "%Y-%m-%d")
# Mengagregasi data berdasarkan bulan agar lebih mudah dibaca
monthly_data_actual <- datauji_excluded %>%
mutate(month = floor_date(date, "month")) %>%
group_by(month) %>%
summarize(monthly_avg_temp = mean(tavg, na.rm = TRUE))
monthly_data_predicted <- tabel2_new %>%
mutate(month = floor_date(Date, "month")) %>%
group_by(month) %>%
summarize(monthly_avg_temp = mean(`Final Adjusted Forecast`, na.rm = TRUE))
# Menggabungkan data aktual dan prediksi
combined_data <- bind_rows(
monthly_data_actual %>% mutate(type = "Actual"),
monthly_data_predicted %>% mutate(type = "Predicted")
)
# Plot data time series menggunakan ggplot2
ggplot(combined_data, aes(x = month, y = monthly_avg_temp, color = type)) +
geom_line(aes(linetype = type)) +
scale_color_manual(values = c("Actual" = "blue", "Predicted" = "orange")) +
labs(title = "Time Series Plot of Average Monthly Temperature",
x = "Period",
y = "Average Temperature (°C)",
color = "Type") +
theme_minimal() +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
---
title: "A FUZZY TIME SERIES MARKOV CHAIN (FTSMC) MODEL WITH AN IMPLEMENTATION OF WEB APPLICATION TO FORECAST THE AVERAGE TEMPERATURE IN KOLAKA REGENCY, SOUTHEAST SULAWESI BASED ON DAILY DATA FROM THE METEOROLOGICAL, CLIMATOLOGICAL, AND GEOPHYSICAL AGENCY (BMKG) IN 2001-2024"
output: html_notebook
---

## Input Data

```{r}
datauji <- read.csv("/cloud/project/avg_temp_sulteng.csv", header=T, sep =",") 
head(datauji)
data = datauji$tavg
n=length(data)
n
```

## Plot Data Aktual

```{r}
## Plot Data Aktual
library(ggplot2)
library(dplyr)
library(lubridate)

# Pastikan kolom tanggal bertipe Date (Tanggal)
datauji$date <- as.Date(datauji$date, format = "%d-%m-%Y")

# Mengagregasi data berdasarkan bulan agar lebih mudah dibaca
monthly_data <- datauji %>%
  mutate(month = floor_date(date, "month")) %>%
  group_by(month) %>%
  summarize(monthly_avg_temp = mean(tavg, na.rm = TRUE))

# Plot data time series menggunakan ggplot2
ggplot(monthly_data, aes(x = month, y = monthly_avg_temp)) +
  geom_line(color = 'blue') +
  geom_smooth(method = "loess", span = 0.2, color = 'red', se = FALSE) + 
  labs(title = "Time Series Plot of Average Monthly Temperature\nin Kolaka Regency Southeast Sulawesi from 2001-2024",
       x = "Period",
       y = "Average Temperature (°C)") +
  theme_minimal() +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") + 
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1), 
    plot.title = element_text(size = 14, hjust = 0.5, margin = margin(t = 20, b = 20))  
  )

```

## Mencari Data Maksimum dan Minimum

```{r}
minimum = min(data)
maximum = max(data)
minimum
maximum
```

## Mencari Data Minimum Baru dan Data Maksimum Baru untuk Dijadikan sebagai Batas Bawah dan Batas Atas Interval Semesta Pembicaraan U

```{r}
new.min = minimum-0.2
new.max = maximum+0.3
new.min
new.max
```

## Menentukan Banyak Interval (N) dan Panjang Interval (L)

```{r}
n = round(1 +(3.3 *logb(length(data), base = 10)))
n
```

```{r}
L = (new.max - new.min)/n
L
```

## Menentukan Batas-batas Interval

```{r}
intrv.1 = seq(new.min,new.max,len = n+1)
intrv.1
```

## Pembagian Interval

```{r}
box1 = data.frame(NA,nrow=length(intrv.1)-1,ncol=3)
names(box1) = c("bawah","atas","kel")

for (i in 1:length(intrv.1)-1) {
  box1[i,1]=intrv.1[i]
  box1[i,2]=intrv.1[i+1]
  box1[i,3]=i
  
}
box1
```

## Menentukan Nilai Tengah Interval

```{r}
n.tengah = data.frame(tengah=(box1[,1]+box1[,2])/2,kel=box1[,3])
n.tengah
```

## Menentukan Bilangan Fuzzy

```{r}
fuzifikasi=c() 
for (i in 1:length(data)){
  for (j in 1:nrow(box1)){
    if (i!=which.max(data)){
      if (data[i]>=(box1[j,1])&data[i]<(box1[j,2])){
        fuzifikasi[i]=j
        break
      }
    }
    else {
      if (data[i]>=(box1[j,1])&data[i]<=(box1[j,2])){
        fuzifikasi[i]=j
        break
      }
    }
  }
}
fuzifikasi
```

## Fuzifikasi ke Data Asal

```{r}
fuzzyfy = cbind(data,fuzifikasi)
fuzzyfy
```

## Membuat Fuzzy Logical Relation (FLR)

```{r}
FLR = data.frame(fuzzifikasi=0,left=NA,right =NA)
for (i in 1:length(fuzifikasi)) {
  FLR[i,1] = fuzifikasi[i]
  FLR[i+1,2] = fuzifikasi[i]
  FLR[i,3] = fuzifikasi[i]
}
FLR = FLR[-nrow(FLR),]
FLR = FLR[-1,]
FLR
```

## Membuat Fuzyy Logical Relation Group (FLRG)

```{r}
FLRG = table(FLR[,2:3])
FLRG
```

## Membuat Matriks Transisi

```{r}
bobot = round(prop.table(table(FLR[,2:3]),1),5)
bobot
```

## Peramalan Awal

```{r}
diagonal = diag(bobot)
m.diagonal = diag(diagonal)
#mengambil nilai selain diagonal
pinggir = bobot-m.diagonal

ramal=NULL
for (i in 1:(length(fuzifikasi))){
  for (j in 1:(nrow(bobot))){
    if (fuzifikasi[i]==j)
    {ramal[i+1]=(diagonal[j]*data[i])+sum(pinggir[j,]*n.tengah[,1]) }else
      if (fuzifikasi[i]==j)
      {ramal[i]=0}
  }
}
ramal = ramal[-length(ramal)]
ramal
```

## Adjusted Forecasting Value (Peramalan Tahap Kedua)

```{r}
adjusted = rep(0,nrow(FLR)) 
selisih = FLR[,3]-FLR[,2] 
for(i in 1:nrow(FLR))
{
  if (FLR[i,2]!=FLR[i,3] && diagonal[FLR[i,2]]==0)
  {adjusted[i]=selisih[i]*(L/2)} else   #Untuk tidak komunicate
    if (selisih[i]==1 && diagonal[FLR[i,2]]>0)
    {adjusted[i]=(L)} else #Untuk  komunicate
      if (FLR[i,2]!=FLR[i,3] && diagonal[FLR[i,2]]>0)
      {adjusted[i]=selisih[i]*L/2} #Untuk komunicate
}
adjusted
```

## Peramalan Tahap Terakhir

```{r}
ramal=ramal[c(2:length(ramal))]
adj.forecast=adjusted + ramal
adj.forecast
```

## Tabel Pembanding dengan Hasil Penyesuaian Kedua

```{r}
datapakai = data[c(2:length(data))]
tanggal = data[c(1:length(data))]
galat = abs(datapakai-adj.forecast)
tabel = cbind(datapakai,ramal,adjusted,adj.forecast,galat)
tabel
```

## Penyesuaian Overestimate Ramalan

```{r}
adj.forecast2 = rep(0,length(datapakai)) 
for(i in 1:length(datapakai)){
  if((ramal[i]-datapakai[i])<(adj.forecast[i]-datapakai[i])) {
    adj.forecast2[i]=ramal[i]+(L/2)
  } else {
    adj.forecast2[i]=adj.forecast[i]}
}
adj.forecast2
```

## Tabel Pembanding dengan Hasil Penyesuaian Kedua

```{r}
tabel2 = cbind(datapakai,ramal,adj.forecast,adj.forecast2)
tabel2
```

```{r}
tail(tabel2, 15)
```
```{r}
datauji_excluded <- datauji[-1,]
tanggal = datauji_excluded$date
tabel_prediksi <- data.frame(tanggal, datapakai, ramal, adj.forecast, adj.forecast2)
tabel_prediksi
```
## Peramalan 7 hari kedepan sejak 6 Juli 2024

```{r}
# Membuat fungsi untuk melakukan peramalan menggunakan model FTSMC
forecast_ftsmc <- function(data, n.tengah, bobot, L, box1, periode) {
  # Fuzifikasi data
  fuzifikasi <- rep(NA, length(data))
  for (i in 1:length(data)) {
    for (j in 1:nrow(box1)) {
      if (data[i] >= (box1[j,1]) & data[i] < (box1[j,2])) {
        fuzifikasi[i] = j
        break
      } else if (i == length(data) && data[i] == box1[j,2]) {
        fuzifikasi[i] = j
        break
      }
    }
  }
  
  # Memastikan tidak ada nilai NA dalam fuzifikasi
  if (any(is.na(fuzifikasi))) {
    stop("Fuzifikasi menghasilkan nilai NA. Pastikan semua data berada dalam rentang interval yang benar.")
  }

  # Inisialisasi variabel ramalan
  ramal <- numeric(length(data) + periode)
  ramal[1:length(data)] <- data
  
  # Melakukan peramalan untuk periode ke depan
  for (i in (length(data) + 1):(length(data) + periode)) {
    for (j in 1:(nrow(bobot))) {
      if (!is.na(fuzifikasi[i-1]) && fuzifikasi[i-1] == j) {
        ramal[i] = (diagonal[j] * ramal[i-1]) + sum(pinggir[j,] * n.tengah[,1])
      }
    }
    # Fuzifikasi nilai ramalan untuk periode berikutnya
    for (j in 1:nrow(box1)) {
      if (ramal[i] >= (box1[j,1]) & ramal[i] < (box1[j,2])) {
        fuzifikasi[i] = j
        break
      } else if (i == length(ramal) && ramal[i] == box1[j,2]) {
        fuzifikasi[i] = j
        break
      }
    }
  }
  
  # Mengembalikan hasil ramalan
  return(ramal[(length(data) + 1):(length(data) + periode)])
}

# Menentukan periode peramalan
periode_peramalan <- 7

# Menentukan nilai diagonal dan pinggir
diagonal <- diag(bobot)
m.diagonal <- diag(diagonal)
pinggir <- bobot - m.diagonal

# Menghitung nilai ramalan 7 hari ke depan
ramalan_7_hari <- forecast_ftsmc(data = adj.forecast2, n.tengah = n.tengah, bobot = bobot, L = L, box1 = box1, periode = periode_peramalan)

# Menampilkan hasil ramalan
ramalan_7_hari

# Gabungkan tabel2 dan data perkiraan baru, dan sertakan kolom tanggal
tabel2_new <- rbind(tabel2, data.frame(datapakai=NA, ramal=NA, adj.forecast=NA, adj.forecast2=ramalan_7_hari))

# Membuat urutan tanggal untuk baris baru
tanggal_terakhir <- max(tanggal)
tanggal_lanjut <- seq(tanggal_terakhir + 1, by = "day", length.out = nrow(tabel2_new) - length(tanggal))

# Tambahkan kolom tanggal ke tabel baru
tabel2_new$tanggal <- c(tanggal, tanggal_lanjut)

# Mengganti nama kolom
colnames(tabel2_new) <- c("Actual", "Initial Forecast", "Adjusted Forecast", "Final Adjusted Forecast", "Date")

# Hitung kolom kesalahan (error)
tabel2_new$Error <- abs(tabel2_new$Actual - tabel2_new$`Final Adjusted Forecast`)

#  Susun ulang kolom untuk menempatkan 'tanggal' sebelum 'Aktual'
tabel2_new <- tabel2_new[, c("Date", "Actual", "Initial Forecast", "Adjusted Forecast", "Final Adjusted Forecast", "Error")]

# Menampilkan tabel
tabel2_new

```
```{r}
tail(tabel2_new, 10)
```

## Uji Ketepatan

```{r}
galat2 = abs(datapakai-adj.forecast2)
MSE = mean(galat2^2, na.rm = TRUE)
MAE = mean(abs(galat2), na.rm = TRUE)
MAPE = mean(abs(galat2/datapakai*100), na.rm=TRUE)
ketepatan = cbind(MSE,MAE,MAPE)
ketepatan
```

## Plot Data Aktual dan Data Prediksi

```{r}

# Ubah kolom tanggal ke format Tanggal
datauji_excluded <- datauji[-1,]
datauji_excluded$date <- as.Date(datauji_excluded$date, format = "%d-%m-%Y")
tabel2_new$Date <- as.Date(tabel2_new$Date, format = "%Y-%m-%d")
# Mengagregasi data berdasarkan bulan agar lebih mudah dibaca
monthly_data_actual <- datauji_excluded %>%
  mutate(month = floor_date(date, "month")) %>%
  group_by(month) %>%
  summarize(monthly_avg_temp = mean(tavg, na.rm = TRUE))

monthly_data_predicted <- tabel2_new %>%
  mutate(month = floor_date(Date, "month")) %>%
  group_by(month) %>%
  summarize(monthly_avg_temp = mean(`Final Adjusted Forecast`, na.rm = TRUE))

# Menggabungkan data aktual dan prediksi
combined_data <- bind_rows(
  monthly_data_actual %>% mutate(type = "Actual"),
  monthly_data_predicted %>% mutate(type = "Predicted")
)

# Plot data time series menggunakan ggplot2
ggplot(combined_data, aes(x = month, y = monthly_avg_temp, color = type)) +
  geom_line(aes(linetype = type)) +
  scale_color_manual(values = c("Actual" = "blue", "Predicted" = "orange")) +
  labs(title = "Time Series Plot of Average Monthly Temperature",
       x = "Period",
       y = "Average Temperature (°C)",
       color = "Type") +
  theme_minimal() +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) 

```


