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coursera-statistics-making-sense-of-data (video pembelajaran)
Data Processing Using Python (video pembelajaran)
Applied Data Science with Python Specialization (video pembelajaran) Unsupervised Machine Learning Project with R (video pembelajaran)
https://www.coursera.org/courses?query=free (video pembelajaran)
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(diakses pada Juli 2020)
https://medium.com/@gifadelyaninursyafitri/k-means-clustering-menggunakan-python-deeb0881333c (diakses pada Juli 2020)
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LAMPIRAN A: PROGRAM UNTUK CAPAIAN NILAI SECARA NASIONAL import numpy as np
import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
plt.title('Box Plot Capaian Nilai Rata-Rata Nasional') plt.show()
from pandas import Series, DataFrame import seaborn as sns
plt.title('Box Plot Capaian Nilai Rata-Rata Nasional Mata Pelajaran') plt.show()
LAMPIRAN B: PROGRAM UNTUK CAPAIAN NILAI RATA-RATA PROVINSI import numpy as np
import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet1")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.tahun_2016,data2.tahun_2017,data2.tahun_2018,data2.tahun_2019]) data2.describe()
print(data2.describe())
plt.title('Box Plot Capaian Nilai Rata-Rata Nasional 2016-2019')
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.show()
from pandas import Series, DataFrame import seaborn as sns
LAMPIRAN C: PROGRAM UNTUK CAPAIAN NILAI RATA-RATA PROVINSI PER MATA PELAJARAN import numpy as np
import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet5")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.bindo_2016,data2.bindo_2017,data2.bindo_2018,data2.bindo_2019]) data2.describe()
print (data2.describe())
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.title('Box Plot Capaian Nilai Rata-Rata Provinsi Mapel Bahasa Indonesia 2016-2019') plt.show()
Boxplot mapel Bindo
import numpy as np import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
plt.title('Histogram Capaian Bahasa Indonesia 2016')
#plt.title('Histogram Capaian Bahasa Indonesia 2017')
#plt.title('Histogram Capaian Bahasa Indonesia 2018')
#plt.title('Histogram Capaian Bahasa Indonesia 2019') plt.show()
from pandas import Series, DataFrame import seaborn as sns
plt.xlabel=('tahun 2016','tahun 2017','tahun 2018','tahun 2019')
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"])
Boxplot mapel Bing
plt.title('Box Plot Capaian Nilai Rata-Rata Provinsi Mapel Bahasa Inggris 2016-2019') plt.show()
import numpy as np import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
plt.title('Histogram Capaian Bahasa Inggris 2016')
#plt.title('Histogram Capaian Bahasa Inggris 2017')
#plt.title('Histogram Capaian Bahasa Inggris 2018')
#plt.title('Histogram Capaian Bahasa Inggris 2019') plt.show()
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet7")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.matek_2016,data2.matek_2017,data2.matek_2018,data2.matek_2019]) data2.describe()
print(data2.describe())
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.title('Box Plot Capaian Nilai Rata-Rata Provinsi Mapel Matematika 2016-2019') plt.show()
from pandas import Series, DataFrame import seaborn as sns
#_=plt.xlabel("Matematika 2018")
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet8")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.ipa_2016,data2.ipa_2017,data2.ipa_2018,data2.ipa_2019]) data2.describe()
print(data2.describe())
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.title('Box Plot Capaian NilaiRata-Rata Provinsi Mapel IPA 2016-2019') plt.show()
Boxplot mapel IPA
import numpy as np import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
LAMPIRAN D: PROGRAM UNTUK CAPAIAN NILAI RATA-RATA PER ZONA WAKTU import numpy as np
import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet2")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.tahun_2016,data2.tahun_2017,data2.tahun_2018,data2.tahun_2019]) data2.describe()
print (data2.describe())
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.title('Box Plot Capaian Nilai Rata-Rata Nasional WIT 2016-2019')
plt.show()
Boxplot WIT
import numpy as np import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet3")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.tahun_2016,data2.tahun_2017,data2.tahun_2018,data2.tahun_2019]) data2.describe()
print(data2.describe())
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.title('Box Plot Capaian Nilai Rata-Rata Nasional WITA 2016-2019')
plt.show()
Boxplot WITA
import numpy as np import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\margaretha.xlsx") data2=data1.parse("Sheet4")
print(data2)
plt.label=['tahun 2016','tahun 2017','tahun 2018','tahun 2019']
plt.boxplot([data2.tahun_2016,data2.tahun_2017,data2.tahun_2018,data2.tahun_2019]) data2.describe()
print(data2.describe())
plt.legend(["1=tahun 2016","2=tahun 2017","3=tahun 2018","4=tahun 2019"]) plt.title('Box Plot Capaian Nilai Rata-Rata Nasional WIB 2016-2019')
plt.show()
Boxplot WIB
LAMPIRAN E: PROGRAM UNTUK CAPAIAN NILAI RATA-RATA ANTAR ZONA WAKTU import numpy as np
import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
import matplotlib.pyplot as plt
data1=pd.ExcelFile("D:\Thonny\marto.xlsx") data2=data1.parse("Sheet6")
data3=data1.parse("Sheet7") data4=data1.parse("Sheet8") ax=plt.gca()
ax.set_ylim([40,68]) ax.set_xlim([0,4])
plt.boxplot([data2.wit_2016,data3.wita_2016,data4.wib_2016])
#plt.boxplot([data2.wit_2017,data3.wita_2017,data4.wib_2017])
#plt.boxplot([data2.wit_2018,data3.wita_2018,data4.wib_2018])
#plt.boxplot([data2.wit_2019,data3.wita_2019,data4.wib_2019]) plt.legend(["1=WIT","2=WITA","3=WIB"])
plt.title('Box Plot Perbandingan Nilai Per Satuan Waktu 2016') data2.describe()
data3.describe() data4.describe() print(data2.describe()) print(data3.describe()) print(data4.describe()) plt.show()
Boxplot zona waktu
LAMPIRAN F: HASIL LISTING PROGRAM UNTUK PCA (PERANGAKAT LUNAK R) 10 68.02 54.55 45.81 51.33 11 66.88 51.00 44.17 50.26 12 70.30 52.90 44.51 52.33 13 70.34 50.40 43.19 47.31 14 70.78 57.16 52.51 55.83 15 70.21 57.96 47.83 59.51 16 70.49 55.62 52.57 57.07 17 66.41 60.00 57.93 60.74 18 63.64 53.76 49.46 56.29 19 66.87 57.48 54.06 59.04 20 65.50 54.47 51.18 56.98 21 66.78 59.68 53.68 57.66 22 68.89 54.22 46.55 58.11 23 61.64 54.73 47.62 49.95 24 63.37 50.03 42.51 52.04 25 62.93 54.43 48.98 53.05 26 67.95 44.30 35.51 46.18 27 65.61 62.71 57.81 59.27 28 73.03 46.90 37.20 47.93 29 64.34 53.49 49.21 55.38 30 65.30 49.25 38.86 45.67 31 73.97 55.56 43.12 51.57 32 60.29 46.59 40.62 49.31 33 69.55 63.69 61.46 63.12 34 66.57 47.59 38.05 47.91
>attach(data)
> data[]=lapply(data,function(x) if(is.numeric(x)){scale(x,center=TRUE,scale=TRUE)}else x)
>eigen(cov(data)) eigen() decomposition
$values
[1] 2.90585095 0.91390082 0.12582050 0.05442774
$vectors
[,1] [,2] [,3] [,4]
[1,] -0.2332135 0.95895776 -0.04032885 -0.1561572 [2,] -0.5647950 -0.08281114 -0.64924283 0.5026258 [3,] -0.5568060 -0.26640223 -0.08699446 -0.7819392 [4,] -0.5626590 -0.05071671 0.75451262 0.3339960
>data_pca=prcomp(data,center=TRUE,scale=TRUE)
>data_pca
Standard deviations (1, .., p=4):
[1] 1.7046557 0.9559816 0.3547119 0.2332975
Rotation (n x k) = (4 x 4):
PC1 PC2 PC3 PC4
y1 -0.2332135 -0.95895776 0.04032885 0.1561572 y2 -0.5647950 0.08281114 0.64924283 -0.5026258 y3 -0.5568060 0.26640223 0.08699446 0.7819392 y4 -0.5626590 0.05071671 -0.75451262 -0.3339960
>install.packages('psych')
--- Please select a CRAN mirror for use in this session ---
Warning: failed to download mirrors file (cannot open URL 'https://cran.r-project.org/CRAN_mirrors.csv');
using local file 'C:/PROGRA~1/R/R-36~1.0/doc/CRAN_mirrors.csv'
Warning: unable to access index for repository https://repo.bppt.go.id/cran/src/contrib:
cannot open URL 'https://repo.bppt.go.id/cran/src/contrib/PACKAGES'
Warning: unable to access index for repository https://repo.bppt.go.id/cran/bin/windows/contrib/3.6:
cannot open URL 'https://repo.bppt.go.id/cran/bin/windows/contrib/3.6/PACKAGES' Warning messages:
1: In download.file(url, destfile = f, quiet = TRUE) :
InternetOpenUrl failed: 'The server name or address could not be resolved' 2: package ‘psych’ is not available (for R version 3.6.0)
>library(psych) Warning message:
package ‘psych’ was built under R version 3.6.2
> fit <- principal(data, nfactors=1, rotate="varimax")
> fit
Principal Components Analysis
Call: principal(r = data, nfactors = 1, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix PC1 h2 u2 com
Test of the hypothesis that 1 component is sufficient.
The root mean square of the residuals (RMSR) is 0.1 with the empirical chi square 4.2 with prob< 0.12 Fit based upon off diagonal values = 0.98>
>data<-read.csv(file.choose(),header=T,sep=",") 10 58.40 44.95 43.20 47.63 11 57.47 46.24 45.18 45.88 12 61.35 46.00 46.91 49.25
13 61.23 41.68 44.78 46.16 14 64.75 50.41 52.82 52.49 15 67.77 48.81 45.67 49.02 16 68.14 47.86 42.80 46.71 17 61.53 56.09 56.30 54.13 18 61.14 50.39 47.87 48.93 19 63.41 51.62 51.65 52.16 20 63.45 52.76 52.78 53.54 21 61.19 58.17 55.38 51.29 22 67.54 50.41 43.63 50.45 23 59.20 48.19 46.02 48.59 24 61.21 49.63 47.88 48.48 25 57.48 50.91 48.12 47.40 26 60.07 41.26 41.32 44.85 27 58.12 57.00 53.56 50.11 28 66.93 42.99 42.97 47.64 29 61.12 48.67 48.24 50.30 30 58.76 44.27 42.16 44.50 31 66.93 49.20 47.34 49.48 32 56.91 44.48 42.28 44.97 33 64.02 58.08 58.41 54.99 34 64.90 45.64 40.69 45.36
>attach(data)
The following objects are masked from data (pos = 4):
y1, y2, y3, y4
>data=read.csv(file.choose(),header=T) Error in file.choose() : file choice cancelled
> data[]=lapply(data,function(x) if(is.numeric(x)){scale(x,center=TRUE,scale=TRUE)}else x)
>eigen(cov(data)) eigen() decomposition
$values
[1] 2.80002588 0.96637104 0.19316564 0.04043744
$vectors
[,1] [,2] [,3] [,4]
[1,] -0.3070382 0.8569975 -0.3560571 0.2109649 [2,] -0.5025958 -0.4433962 -0.7257534 -0.1551747 [3,] -0.5690559 -0.2181653 0.3784050 0.6966987 [4,] -0.5738470 0.1461475 0.4509046 -0.6678515
>data_pca=prcomp(data,center=TRUE,scale=TRUE)
>>data_pca
Error: unexpected '>' in ">"
>data_pca
Standard deviations (1, .., p=4):
[1] 1.6733278 0.9830417 0.4395061 0.2010906
Rotation (n x k) = (4 x 4):
PC1 PC2 PC3 PC4
y1 -0.3070382 -0.8569975 -0.3560571 -0.2109649 y2 -0.5025958 0.4433962 -0.7257534 0.1551747 y3 -0.5690559 0.2181653 0.3784050 -0.6966987 y4 -0.5738470 -0.1461475 0.4509046 0.6678515
> fit <- principal(data, nfactors=1, rotate="varimax")
> fit
Principal Components Analysis
Call: principal(r = data, nfactors = 1, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix PC1 h2 u2 com
Test of the hypothesis that 1 component is sufficient.
The root mean square of the residuals (RMSR) is 0.17 with the empirical chi square 11.24 with prob< 0.0036
Fit based upon off diagonal values = 0.93>
>data=read.csv(file.choose(),header=T) 10 60.61 45.20 38.71 42.81 11 59.20 44.74 38.62 41.90 12 62.56 44.58 37.31 43.03 13 62.36 44.74 36.86 42.12 14 65.09 50.54 45.48 47.48 15 65.47 48.65 41.00 47.22 16 66.87 51.16 41.85 47.38 17 57.11 49.11 41.44 45.05 18 58.39 45.85 39.38 44.97 19 59.12 47.60 41.49 46.01 20 61.99 50.26 45.19 49.16 21 62.21 58.18 51.53 50.77 22 67.76 53.82 41.62 50.21 23 56.16 42.95 36.32 42.14 24 61.80 51.63 43.47 47.72 25 59.27 52.46 46.91 48.65 26 62.13 43.67 35.88 41.94 27 60.16 57.80 49.86 48.58 28 67.88 47.67 40.95 46.99 29 56.81 44.91 38.24 44.05 30 59.74 46.47 37.47 41.98 31 69.98 54.07 43.86 47.24 32 56.11 43.12 37.37 42.69 33 61.29 52.53 48.13 49.37 34 64.78 47.19 39.48 45.69
>attach(data)
The following objects are masked from data (pos = 3):
y1, y2, y3, y4
The following objects are masked from data (pos = 5):
y1, y2, y3, y4
> data[]=lapply(data,function(x) if(is.numeric(x)){scale(x,center=TRUE,scale=TRUE)}else x)
>eigen(cov(data)) eigen() decomposition
$values
[1] 3.32822563 0.51812326 0.10365996 0.04999115
$vectors
[,1] [,2] [,3] [,4]
[1,] -0.4300257 0.85538110 -0.1537894 -0.2444378 [2,] -0.5146113 -0.33010198 -0.7580451 0.2271024 [3,] -0.5151896 -0.39894199 0.3170072 -0.6891527 [4,] -0.5336972 0.01418239 0.5488378 0.6432288
> fit <- principal(data, nfactors=1, rotate="varimax")
> fit
Principal Components Analysis
Call: principal(r = data, nfactors = 1, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix PC1 h2 u2 com
Test of the hypothesis that 1 component is sufficient.
The root mean square of the residuals (RMSR) is 0.09 with the empirical chi square 3.52 with prob< 0.17
Fit based upon off diagonal values = 0.99>
>data=read.csv(file.choose(),header=T) 10 60.12 45.14 41.26 43.45 11 58.12 43.86 40.34 41.85
12 62.00 44.39 40.03 42.88 13 62.32 45.05 40.52 42.78 14 63.65 48.51 45.04 46.29 15 66.59 48.85 42.05 47.08 16 68.82 51.32 44.12 47.92 17 57.71 47.46 40.84 43.72 18 60.33 44.48 41.12 43.71 19 59.97 46.33 42.47 45.26 20 61.80 45.75 44.47 45.65 21 60.30 51.69 50.47 48.98 22 69.42 52.19 43.87 49.03 23 56.87 43.26 38.76 41.79 24 64.75 45.90 46.29 45.78 25 58.76 48.46 46.23 47.63 26 62.61 43.81 39.74 42.78 27 58.49 52.60 49.15 50.07 28 67.89 48.33 44.34 47.90 29 56.04 44.69 40.74 43.51 30 60.29 48.10 41.91 43.98 31 70.19 54.25 47.50 50.18 32 55.40 42.09 39.68 40.45 33 61.11 49.51 48.28 48.96 34 64.45 47.55 42.40 45.04
>attach(data)
The following objects are masked from data (pos = 3):
y1, y2, y3, y4
The following objects are masked from data (pos = 4):
y1, y2, y3, y4
The following objects are masked from data (pos = 6):
y1, y2, y3, y4
> data[]=lapply(data,function(x) if(is.numeric(x)){scale(x,center=TRUE,scale=TRUE)}else x)
>eigen(cov(data)) eigen() decomposition
$values
[1] 3.53054718 0.32439639 0.12677534 0.01828109
$vectors
[,1] [,2] [,3] [,4]
[1,] -0.4641254 0.8545390 0.1213037 -0.1990880 [2,] -0.5051840 -0.1972462 -0.8227453 -0.1702156 [3,] -0.5021493 -0.4651447 0.5245839 -0.5062590 [4,] -0.5265196 -0.1204051 0.1821739 0.8216401
> fit <- principal(data, nfactors=1, rotate="varimax")
> fit
Principal Components Analysis
Call: principal(r = data, nfactors = 1, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix PC1 h2 u2 com
y1 0.87 0.76 0.239 1 y2 0.95 0.90 0.099 1 y3 0.94 0.89 0.110 1
y4 0.99 0.98 0.021 1 PC1 SS loadings 3.53 Proportion Var 0.88
Mean item complexity = 1
Test of the hypothesis that 1 component is sufficient.
The root mean square of the residuals (RMSR) is 0.06 with the empirical chi square 1.43 with prob< 0.49 Fit based upon off diagonal values = 1>
LAMPIRAN G : PROGRAM K MEANS import numpy as np
import pandas as pd
from pandas import Series, DataFrame import seaborn as sns
sct = ax.scatter(x_scaled[:,1], x_scaled[:,0], s = 100,c = data2.kluster, marker = "o", alpha = 0.5)
centers = kmeans.cluster_centers_
ax.scatter(centers[:,1], centers[:,0], c='blue', s=200, alpha=0.5) plt.title("Hasil Klustering K-Means")
# km=KMeans(n_clusters=k)
# km=km.fit(data2_transformed)
# Sum_of_squared_distances.append(km.inertia_)
#plt.plot(K,Sum_of_squared_distances,"bx-")
#plt.plot(range (1,15),Sum_of_squared_distances)
#plt.xlabel("k")
#plt.ylabel("Sum_of_squared_distances")
#plt.title("Elbow Method For Optimal k")
#kl=KneeLocator(
# range(1,15),Sum_of_squared_distances, curve="convex", direction="decreasing"
# )
LAMPIRAN H: PROGRAM UJI STATISTIK
print('mean tidak sama')
Uji