Bogor agricultural university student’s characteristics based on their national examination score and gpa

BOGOR AGRICULTURAL UNIVERSITY STUDENT’S
CHARACTERISTICS BASED ON THEIR NATIONAL
EXAMINATION SCORE AND GPA

NOOR HIDAYATUZZAKIAH

DEPARTMENT OF STATISTICS
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014

PERNYATAAN MENGENAI SKRIPSI DAN
SUMBER INFORMASI SERTA PELIMPAHAN HAK CIPTA*
Dengan ini saya menyatakan bahwa skripsi berjudul Bogor Agricultural
University Student’s Characteristics Based on Their National Examination Score
and GPA adalah benar karya saya dengan arahan dari komisi pembimbing dan
belum diajukan dalam bentuk apa pun kepada perguruan tinggi mana pun. Sumber
informasi yang berasal atau dikutip dari karya yang diterbitkan maupun tidak
diterbitkan dari penulis lain telah disebutkan dalam teks dan dicantumkan dalam
Daftar Pustaka di bagian akhir skripsi ini.

Dengan ini saya melimpahkan hak cipta dari karya tulis saya kepada Institut
Pertanian Bogor.
Bogor, July 2014
Noor Hidayatuzzakiah
NIM G14100105

ABSTRACT
NOOR HIDAYATUZZAKIAH. Bogor Agricultural University Student’s
Characteristics Based on Their National Examination Score and GPA. Supervised
by ASEP SAEFUDDIN dan FARIT M AFENDI.
National Examination (UN) has been established as a standard to pass high
school since 2002 with a purpose to improve the quality of education in Indonesia
and measure student’s compentency during high school. It indicated an
assumption that good UN score would provide good achievement in college as
well. Student’s achievement at university is indicated by GPA (Grade Point
Average). Theoretically, UN score and GPA should have high positive
correlation. However, this is not automatically true. In 2010, more than 30%
student who had high UN score got GPA TPB less than 2.50. This percentage
tends to decrease in 2011 and 2012. By using pearson correlation test, there was a
significance correlation between UN score and GPA TPB even the coefficient was

weak and positif. Based on Multiple correspondence analysis in the last three
years, characteristics of student who had high UN score and high GPA TPB were
from faculty FATETA, FMIPA, FEM, and FEMA, from department with high
popularity level such as ITP, STK, AGB, ILKOM, GIZI, TIN, SIL, and
EKSYAR, and came from Banten, DKI Jakarta, and West Java. Whereas the
characteristics students who had high UN score but got low GPA TPB were from
veterinary faculty, enrolled IPB through UTMI, and had higher education and
higher income family background.
Keywords: GPA, multiple correspondence analysis, UN score

BOGOR AGRICULTURAL UNIVERSITY STUDENT’S
CHARACTERISTICS BASED ON THEIR NATIONAL
EXAMINATION SCORE AND GPA

NOOR HIDAYATUZZAKIAH

Scientific Paper
to complete the requirement for graduation of
Bachelor Degree in Statistics
at

Department of Statistics

DEPARTMENT OF STATISTICS
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014

Title
Name
NIM

: Bogor Agricultural University Student’s Characteristics Based on
Their National Examination Score and GPA
: Noor Hidayatuzzakiah
: G14100105

Approved by

Prof Dr Asep Saefuddin, MSc

Advisor I

Dr Farit M Afendi, MSi
Advisor II

Acknowledge by

Dr Anang Kurnia, MSi
Head of Department

Graduation Date:

ACKNOWLEDGEMENTS
Alhamdulillahi rabbil ‘alamin, many thanks to Allah subhanahu wa ta’ala
for His bless so I can finish my research with the title “Bogor Agricultural
University Student’s Characteristics Based on Their National Examination Score
and GPA”.
The author realize that this paper would not have been complete without
support from many people, that’s why the author would like to express her sincere
thank to those who have helped:

1. Prof Dr Ir Asep Saefuddin,MSc and Dr Farit M Afendi,MSi as advisory
committee for their kindness, warm advices, and helpful guidence during
writing this paper.
2. Noormansyah and Maryam as her parent, and her sisters, Noor Indah
Ekawati, Noor Ni’mati Khairunnisa, and Salma Noor Khalifatuzzahra,
who always give unstoppable love, affection, spirit, and pray to the author
all this time.
3. STK47 who always cheering her up with their spirit, support, jokes, and
unimportant chat in group
4. The author’s closest friends, Gita as the roommate for four years in IPB,
and Defika as the roommate in the last 3 month in IPB, for their support
and everything.
5. The author’s high school friends, #istseleb, for being available anytime.
Hopefully this paper can be useful for those who read it.

Bogor, July 2014
Noor Hidayatuzzakiah

CONTENT
LIST OF TABLE

ix

LIST OF FIGURE

ix

LIST OF APPENDIX

ix

INTRODUCTION

1

Background

1

Objective

2

METHODOLOGY

2

Data Source

2

Methods

2

RESULT AND DISCUSSION

3

Student’s Profile

3

Data Exploration

4

Correlation and Association Between UN Score and GPA TPB

7

Characteristics Student Based on UN Score and GPA TPB

8

CONCLUSION

12

REFERENCES

13

APPENDIX

14

BIOGRAPHY

23

LIST OF TABLE
1
2
3
4
5
6
7
8

Descriptive of UN score and GPA TPB
Range for UN score and GPA TPB
The list of subjects in TPB IPB
Percentage of UN score’s classes to GPA TPB’s classes
Correlation between UN score and GPA TPB
Chi-square test between classes of UN score and GPA TPB
Charactristics of students year 2010
Characteristics of students year 2011 and 2012

4
4
6
7
8
8
9
11

LIST OF FIGURE

1
2
3
4
5

Classification scheme of UN score and GPA TPB
Percentage of UN score and GPA TPB in 2010, 2011, 2012
Percentage of group (Y). (a) 2010. (b) 2011&2012
Plot of multiple correspondence analysis for student year 2010
Plot of multiple correpondence analysis for students year 2011&2012

3
5
7
10
12

LIST OF APPENDIX
1 List of variables and the categories
2 Percentage of each variables
3 Non-trivial eigenvalues for students year 2010
4 Column contribution of multiple correspondence analysis from
student year 2010
5 Non-trivial eigenvalues for students year 2011&2012
6 Column contribution of multiple correspondence analysis from
student year 2011&2012

14
15
16
18
19
21

0

1

INTRODUCTION
In the history of education in Indonesia, national exam has been held since
1965 namely Ujian Negara and then changed into Ujian Sekolah in 1972, and then
renamed to EBTANAS (Evaluasi Belajar Tahap Akhir Nasional) in 1980, then
changed its name again to UAN (Ujian Akhir Nasional) in 2002, and renamed
again to UN (Ujian Nasional) since 2005 until now. EBTANAS is the national
exam’s format that has the longest period, which is 20 years. Cut-off score to pass
EBTANAS is using formulations with involving grades odd and even semesters
and pure UN score (Nilai Ebtanas Murni). Since 2002, when renamed to UAN,
cut-off score to pass UAN is change into only pure UAN score. Then, in 2005 up
to now, when UAN changed into UN, there is few changes formula to pass UN. In
2013, cut-off score was based on the proportion of student’s UN score and School
exam score, which is 60:40.
Background
Mohammad Nuh as the minister of Kemendikbud said that National Exam
(UN) is an effort to control the quality of education. The aim of quality control is
to ensure continuous quality improvement. So UN is used for mapping as well
coaching and improvement of education quality (Sidiknas 2013). Besides, some
education observers also stated that UN is a test to prove the ability of students
nationally. Therefore, UN is not only about material education, but also mental
education for students.
Sudharto P Hadi as the head of Diponegoro University said that UN score
is more proper than report score for SNMPTN Undangan selection because report
score from each high school have different standards (Himawan 2012). However,
the issues of ‘cheating’ in UN have been heard since few years ago in almost all
over Indonesia. It cause doubt on the UN score, whether UN score still can
consider as a standard to measure student’s competence or not, whether UN score
can represent student’s performance at university level or not.
First year study in IPB, namely TPB (Tingkat Persiapan Bersama), is the
starting point for all new students from various high schools. Subjects in TPB are
a repetition from high school’s subjects, so that students first performance can be
describe through GPA in TPB. Theoretically, good UN score will be followed by
good GPA in TPB, but in fact, there are many students who have high UN score
but low in GPA, or students who have low UN score but high in GPA.
By classifying students into nine clusters, which nine clusters are the
combination of high, middle, and low between UN score and GPA, it will be
easier to know the characteristics of students who have similarity. To see the
characteristics profile of each cluster is using Multiple Correspondence Analysis
(MCA). MCA is a visual picture in a two dimensions plot that can explain
characteristics data which have categorical variables. The closer each category to
the clusters, the more category describe the cluster. This could be useful to
evaluate students with high or middle UN score but low in GPA.

2

Objective
The objective of this research are analysing the relationship between UN
score and GPA and analysing the characteristics of student in IPB based on their
UN score and GPA according to their demographics, high school status, and
major at IPB.

METHODOLOGY
Data Source
This study used data from student affair directorate (Dit. AP) Bogor
Agricultural University. Population is all students year 2010, 2011, and 2012 at
TPB. The response variables are UN score and GPA, and the explanatory
variables are :
6. Father’sformer
8. Mother’sformer
1. Gender
education
education
2. High school status
7. Mother’s
9. Faculty
3. Enrollment scheme
4. Parental income
occupation
10. Department.
5. Father’s occupation

Methods
The method used in this research are:
1. Cleaning the data by checking data’s completeness. Observation with
incomplete data was excluded in this process.
2. Explore the data using descriptive statistical analysis to determine
respondent’s descriptions.
3. Check correlation between UN score and GPA TPB.
4. Classify UN score and GPA TPB into three classes
a. UN score divide into three classes, which are high, middle, and low.
GPA also divide into three classes, which are high, middle, and low.
1
To divide the range is using formula �̅ ± �. High if the score is
2

1

1

more than �̅ + �, low if the score less than �̅ − �, and middle is
1

2

1

2

between �̅ + � and �̅ − �. Half of standard deviation is ideal to
2
2
divide the range for this data, because the width of each range is not
too large or small so the total observations of each range is almost
balance.
b. Combine the three classes of UN score and GPA TPB, so there will
be nine clusters as the Figure 1 below,

3

UN
Score

High

GPA High

GPA Middle

Middle

GPA Low

GPA High

GPA Middle

Low

GPA Low

GPA High

GPA Middle

Figure 1 Classification scheme of UN score and GPA TPB
5. Check association between classes in UN score and classes in GPA TPB
using chi-square test.
6. Implement Multiple correspondence analysis to describe the
characteristics of each clusters.
a. Define indicator matrix (Z). Let Q be the number of variables. The
dimension of matrix Z is nxp, where n is the number of observations,
p is the number of total categories. Each variable contain pq
Q
categories, so the total of categories is p (p= ∑q=1 pq ). Matrix Z
contain binary number, where 1 if the observation belongs to the
corresponding category of each variable, and otherwise is 0.
b. Define burt matrix (�)�� . Burt matrix is defined by ZTZ.
c. Calculate mass column total m,where m=

1

n×Q

ZT 1 .

d. Define diagonal matrix ��� where the main diagonal is the element
of m.
e. The solution of multiple correspondence analysis according to
Kaciak and Louviere (1990) is calculating the eigenvalues (ei) and
eigenvectors (wi) of S, � = ��−�/� �� ��−�/� , then calculating the
�/�
principal coordinate, �� = �� �� , where � = [�� �� ] = �−�/� �.

RESULT AND DISCUSSION
Student’s Profile
Overall, students year 2010, 2011, 2012 characteristic’s was almost the
same. In terms of demographics, the number of female students in IPB exceeds
the male with percentage over 55%. More than 40% of IPB students came from
West Java, more than 8% from DKI Jakarta, more than 7% from Banten, around
18% came from Central Java, Yogyakarta and East Java, about 17% came from
Sumatra, and the rest came from Kalimantan, Bali, Sulawesi, and around Eastern
Indonesia. In term of family background, more than 40% of students had a father
who reached college level, more than 30% at high school level, and the rest was
below high school or blank. While mother's education level, about 35% at college
level, 35% at high school level, and the rest was below high school or blank.
Father’s occupation was dominated by civil servants/state/military/etc that was

GPA Low

4

more than 50%, as a private employee/entrepreneur/professional was around 16%,
as a farmer/fisherman/laborer was around 17%, and the rest was work in other
professions or blank. Most of parents income of IPB student’s were in range
Rp2.500.000 until Rp5.000.000 per month, followed by range from 1,000,000 to
Rp2.500.000 per month. The remaining are about 17% under Rp1,000,000 per
month and about 28% higher than Rp5.000.000 per month.
IPB provided 5 schemes of enrollment, which were SNMPTN Undangan
(USMI), SNMPTN Tulis (SBMPTN), International Achievement (PIN), local
scholarship (BUD), and UTMI. The largest quota for new students was USMI, so
that more than 60% of students IPB enrolled through USMI, then followed by
SBMPTN about 17%, UTMI about 9%, and the rest was through BUD and PIN.
The percentage of students who enrolled through USMI was reduced from 2010 to
2011 and 2012, so the percentage of students who enrolled through other schemes
was increasing. The exact percentage was listed in Appendix 2.
Data Exploration
The average of GPA TPB in the last three years (2010, 2011, and 2012)
increased every year. As seen in Table 1, in 2010 the average of GPA TPB was
2.71, in 2011 the average of GPA TPB was 2.93 and in 2012 the average of GPA
TPB was 3.075. In contrast to UN score, year 2011 had the largest UN score than
2010 and 2012. In brief, the average and the standard deviation of UN score from
2010 to 2012 were 50.13 and 3.89 respectively, while the average and the standard
deviation of GPA TPB were 2.91 and 0.63 respectively.
Table 1 Descriptive of UN score and GPA TPB

UN SCORE
GPA TPB

2010

2011

2012

TOTAL

Mean
St.Dev

49.36
3.58

51.12
3.89

49.99
3.99

50.13
3.89

Mean

2.71

2.93

3.08

2.91

St.Dev

0.63

0.62

0.59

0.63

The distribution both UN score and GPA TPB was skewed to the left,
means that the average of UN score and GPA TPB was less than the median of
each score. UN score was classified into three classes, which were high, middle,
and low. High if UN score more than the average plus half of standard deviation,
low if UN score less than the average minus half of standard deviation, and
middle was between them. This was applied to classified GPA TPB too. The
score’s range was listed below in Table 2.
Table 2 Range for UN score and GPA TPB
UN Score

GPA TPB

High

≥ 52.08

≥ 3.22

Middle
Low

48.19 – 52.08
< 48.19

2.59 – 3.22
< 2.59

Based on first graph on Figure 2, student in year 2010 and 2012 mostly in
middle class (UN score between 48.19 and 52.08) which were 45.9% from total

5

students in 2010, and 38.3% from total students in 2012, but in 2011 mostly in
high class which was 45.4% from total students in 2011. Student year 2010 had
the most percentage of students in low class (UN score less than 48.19) which was
30.6%, whereas students year 2011 had the least percentage of students in low
class which was 18.2%.

UN Score
50%

45.9%

GPA TPB
46.7%

50%

45.4%

40.3%
36.5%

40%

38.3%
34.7%

40%

39.7%
38.2%
35.7%

34.8%

30.6%

30%

27.0%
23.5%

30%

24.6%
21.6%
18.5%

18.2%

20%

20%

10%

10%

0%

0%
2010
HIGH

2011
MIDDLE

2012
LOW

2010
HIGH

2011
MIDDLE

2012
LOW

Figure 2 Percentage of UN score and GPA TPB in 2010, 2011, 2012
Based on the second graph on Figure 2, students year 2010 and 2011
mostly in middle class (GPA TPB between 2.59 and 3.22) which were 40.3%
from total students in 2010, and 39.7% from students in 2011. but in 2012
students mostly in high class which was 46.7%. From 2010 until 2012, the
percentage of student who had high GPA TPB was increased, and student who
had low GPA TPB was decreased.
UN score and GPA TPB was expected had a positive correlation or
association, so the percentage of students who had high UN score must be pretty
similar with the percentage of students who got high GPA TPB, and so on. Based
on Figure 2, in 2010 the percentage of students who had high UN score was quite
similar with the percentage of students who got high GPA TPB, and so did the
middle and low classes. In 2011, the percentage of students who had high UN
score (45.4%) was higher than the percentage of students who got high GPA TPB
(35.7%). The percentage of students who had low UN score (18.2%) was lower
than the percentage of students who got low GPA TPB (24.6%). While in 2012
the percentage of students who had high UN score (34.7%) was lower than the
percentage of students who got high GPA TPB (46.7%). The difference
percentage was caused by the changing system in UN and TPB.
The requirement to pass high school in 2012 was different with 2010 and
2011. To pass high school in 2010 and 2011, the average of total 6 subjects in UN
must be more than 55, the minimum score was 40 for maximal two subjects, and
the minimun score was 42.5 for other subjects. While to pass high school in 2012,
Ujian Akhir Sekolah (UAS) was included into the calculation for final score. The
proportion for final score was; UAS score 40% and UN score 60%. The minimum

6

final score to pass high school was 55, with the minimum score for each subject
was 40. The percentage of students who had high UN score was decreasing in
2011 to 2012. It did not mean that the quality of students also decreasing, but it
could be because of the system’s changing.
IPB was implement new curriculum at TPB and new scoring system
started in 2011. The new curriculum for TPB was the reduction of some subjects
for some certain major. The difference could be seen in Table 3. The new
curriculum was offering subjects that the students need as the basic
knowledgement, while the old curriculum was giving all TPB’s subjects to all
students from various major. This could be one of many reasons why the student’s
GPA TPB was increasing from 2010 to 2011 and 2012. Started in 2011, IPB not
only changing the curriculum of TPB, but also changing the scoring system from
5 range score (A, B, C, D, E) to 7 range score (A, AB, B, BC, C, D, E). The
difference of the scoring system could be possibly increasing the final score or
GPA, and it could be another reason why the percentage of students who got high
GPA TPB was ncreasing every year.
Table 3 The list of subjects in TPB IPB
2010
PM
Kalkulus
Biologi
Kimia
Fisika
Sosiologi Umum
Ekonomi Umum
Agama
B. Inggris

B. Indonesia
PKN
Olahraga
PIP

2012 & 2012
LM Sosiologi Umum
PM Ekonomi Umum
Kalkulus Agama
Biologi Dasar B. Inggris
Biologi Umum B.Indonesia
Fisika Dasar PKN
Fisika Umum Olahraga
Kimia Dasar PIP
Kimia Umum

There was a different result (the increament of GPA TPB) when the new
curriculum of TPB was applied, so the students in year 2011 and 2012 was
combined in the next analysis as an observation who had different treatment with
students in 2010.
Table 4 was explaining the percentage of GPA TPB to each classes in UN
score. Students in year 2010 who had a high UN score mostly got middle GPA
TPB which was 40.76% only 28.96% of them got high GPA TPB, and around
30% of them got low GPA TPB. While the students who had low UN score was
mostly got low GPA TPB, and there was 14.06% who got high GPA TPB. In year
2011 and 2012, the students who had high UN score mostly got high GPA TPB
around 48.90% and only 16.78% of them got low GPA TPB. The students who
had low UN score was mostly got middle GPA TPB and almost 30% of them got
high GPA TPB. This was proving that the new curriculum and new scoring
system could increase the GPA in TPB.
Figure 3 was explaining the percentage of students based on the
classification scheme in Figure 1. Y1 was a group of students who had high UN
score and high GPA TPB, Y2 was a group of students who had high UN score and
middle GPA TPB, Y3 was a group of students who had high UN score and low
GPA TPB, and so on. Based on Figure 3, only 6.8% from student year 2010
belongs to Y1, students who had high UN score and GPA TPB. This percentage

7

was increasing in 2011&2012 became 19.43%. Students who had high UN score
but got low GPA TPB was decreased 0.41% from 2010 to 2011&2012. Overall,
the quality of students in IPB gets better every year based on their achievement in
high school and first year in college.
Table 4 Percentage of UN score’s classes to GPA TPB’s classes
UN SCORE

High

Middle

Low

YEAR

GPA

2010

2011&2012

High

28.96%

48.90%

Middle

40.76%

34.23%

Low

30.27%

16.87%

Total

100.00%

100.00%

High

22.80%

41.48%

Middle

38.84%

37.75%

Low

38.35%

20.78%

Total

100.00%

100.00%

High

14.06%

28.53%

Middle

42.01%

41.16%

Low

43.93%

30.31%

Total

100.00%

100.00%

GPA TPB

GPA TPB
Y7
4.31%

Y4
10.46%

Y1
6.80%

Y7
6.52%

Y4
15.52%

Y1
19.43%

Y8
9.41%

Y5
14.12%

Y2
13.60%

Y9
6.93%

Y6
7.77%

3.22

3.22
Y8
12.87%

Y5
17.82%

Y2
9.57%

Y9
13.46%

Y6
17.60%

Y3
7.11%

2.59

2.59

48.19

52.08

UN
Score

48.19

Y3
6.70%
52.08

UN
Score

Figure 3 Percentage of group (Y). (a) 2010. (b) 2011&2012

Correlation and Association Between UN Score and GPA TPB
UN score, as a measurement of student’s ability and quality in high school,
was expected had a positive correlation with GPA TPB because the subjects in
TPB was similar as the subjects in high school. Correlation between UN score and
GPA TPB could be seen using Pearson Correlation and the result was shown in
Table 5.

8

Table 5 Correlation between UN score and GPA TPB
2010

2011 & 2012

Pearson Correlation

0.173

0.206

Sig. (2-tailed)

.000

.000

N

3574

7102

Coefficient correlation between UN score and GPA TPB was low and
positive, this mean that the correlation between UN score and GPA TPB was
weak, and increament of UN score was followed by the increament of GPA in
TPB. Significance (2-tailed) test of each year were less than alpha (0.05), so the
null hypothesis was rejected, and it means that there was a correlation between
UN score and GPA TPB, even the cofficient correlation was low. This was
because the size of observation (n) was big, so the t-statistics became bigger and
tend to reject null hypothesis.
After classifying UN score and GPA TPB into high, middle, and low as
explained before, the association between classes in UN score and classes in GPA
TPB could be seen by using Chi-Square test, with the null hypothesis was no
association.
Table 6 Chi-square test between classes of UN score and GPA TPB

Pearson Chi-Square
Asymp. Sig. (2-sided)

2010

2011 & 2012

75.949

204.733

.000

.000

Significance (2-sided) test of each year were less than alpha (0.05), so the
null hypothesis was rejected, and it means that there was an association between
classes in UN score and classes in GPA TPB. The association between UN score
and GPA could be seen in Table 4. Students who got high GPA TPB tends to had
high UN score rather than had middle or low UN score, while students who got
low GPA TPB tends to had low UN score rather than had high or middle GPA
TPB.

Characteristics Student Based on UN Score and GPA TPB
Student Year 2010
Chi-square test was used to knew the association between group (Y1-Y9)
to other variables. The result was all variable had association with group except
mother’s occupation because the significance value was more than alpha (0.05).
Because mother’s occupation did not have association with group, so it was
excluded from multiple correspondence analysis.
Multiple correspondence analysis to students year 2010 resulted 45 nontrivial eigenvalues with a total variance was 4.09. Based on the picture above, the
first two principal axes could explain 11.05% of the total variance. The largest
contributor to the first principal axis and the second principal axis was father’s
former education with the absolute contribution was 26.9% and 31.6%.

9

Categories that located far from the origin such as FE4, ME4, FO4, and,
S3 indicated that only a few students in 2010 who did not fill father’s and
mother’s former education (FE4 and ME4), and father’s occupation (FO4) on the
registration form, and only slightly of those coming from foreign high school (S3).
The first axis or component 1 was like classified groups based on GPA
TPB. Above component 1 was for groups with low GPA TPB while below
component 1 was for groups with high and middle GPA TPB. The second axis or
component 2 was like classified groups based on UN score. In the right
component 2 was for groups with low UN score, while in the left component 2
was for groups with high and middle UN score.
Variable group (Y1-Y9) was best described by the first and second
principal axis with the same absolute contribution, 1.7%. Y1, Y2, Y4, and Y5
were located adjacent as shown in Figure 4, on the left component 2 and below
component 1. Y1, Y2, Y4, and Y5 were a group who had high and middle UN
score and got high and middle GPA TPB. Y3 and Y6 were located adjacent in the
left of component 2 and above component 1. Y7 and Y8 were located adjacent in
the right component 2 and below component 1. Y9 was located at the right
component 2 and above component 1.
The characteristics of each group could be seen in Table 7. Y9 was located
far from other categories, so group Y9 did not have tendency to a certain
categories.
Table 7 Charactristics of students year 2010
Variable
Gender
Enrollment
Faculty

Y1, Y2, Y4, Y5
G2 (Female)
J2 (SBMPTN)
F (FATETA), H (FEM),
I (FEMA)

Group
Y3, Y6
J5 (UTMI)
B (FKH)

Department

D1 (ITP, STK, AGB,
ILKOM, GIZI, TIN, SIL,
EKSYAR)

-

Origin

P2 (Banten), P3 (DKI
Jakarta)

-

Parent Income

-

Father
Occupation

FO1 (PNS/BUMN/etc),
FO2 (private
employee/entrepeneur/etc)

Father Education
Mother
Education

ME2 (High school)

I5 (5jt – 7.5jt), I6
(> 7jt)
-

FE3 (College)
ME3 (College)

Y7, Y8
A (FAPERTA),
D(FAPET)
-

P5 (Central Java,
DIY, East Java)
I3 (1jt – 2.5jt)
-

FE2 (High school)
-

10

Column Plot
0.4
I5

G

I6

0.3

Y6

Y3
J5

B

0.2

Y9

ME3

Component 2

I4

0.1

FE3
P4
D2
P1
S1

0.0
H

E

Y2

D1

G1

J1

P3

D3

G2
J2

S2

Y4

F

FO2

-0.1

P6

C

P2

Y1
I

FO1

Y5

P5

I3

ME2

-0.2

Y7
Y8

A
FE2

-0.3
-1.00

-0.75

-0.50

-0.25

0.00

D

0.25

0.50

Component 1

Figure 4 Plot of multiple correspondence analysis for student year 2010
Student Year 2011 and 2012
The result of chi-square test was all variable had association with group
except mother’s occupation because the significance value was more than alpha
(0.05). Because the significance value of mother’s occupation did not have
association with group, so it excluded from multiple correspondence analysis.
The results of multiple correspondence analysis in students year
2011&2012 generated 45 non-trivial eigenvalues with a total of variance was 4.09.
Based on the plot of Figure 5, the first two principal axes were able to explain
10.11% of total variance. The biggest contributor to the first principal axis was
father's education variable with the absolute contribution was 24.6%. Meanwhile,
the biggest contributor to the second principal axis was faculty with the absolute
contribution was 22.8%.

11

The formation of plot MCA graph in Figure 5 was like classifying groups
based on GPA TPB only. The first axis or component 1 was like divided groups
into two, above component 1 and below component 1. Above component 1 was
for groups who had high GPA TPB, and below component 1 was for groups who
had middle and low GPA TPB.
Group Y1-Y9 was well explained by the second principal axis. The result
of ortoghonal projection in Figure 5, Y1, Y4, and Y7 were in the same quadran,
Y2, Y5, Y8, Y3, Y6, and Y9 were in the same quadran. But if we see closely at
Figure 6, Y3, Y6, Y9 were located far away from Y2, Y5, and Y8, means that Y3,
Y6, Y9 had a different characteristics with Y2, Y5, Y8. The characteristics of
each group was explained in Table 8.
Table 8 Characteristics of students year 2011 and 2012
Variable
Gender
Enrollment

Group
Y1, Y4, Y7
G2 (Female)
J1 (USMI), J2 (SBMPTN)

Y2, Y5, Y8
-

High school
status

S1 (Public school)

Faculty

F (FATETA), G
(FMIPA), H (FEM), I
(FEMA)

A (FAPERTA),
E(FAHUTAN)

Department

D1 (ITP, STK, AGB,
ILKOM, GIZI, TIN, SIL,
EKSYAR)

Origin

P2 (Banten), P3 (DKI
Jakarta), P4 (West Java)

D2, D3 (all
department
except
department in
D1)
-

Parent Income

-

-

I1 (< 500rb)
-

Y3, Y6, Y9
J4 (BUD), J5 (
UTMI)
S2 (Private school)
B (FKH), C
(FPIK), D
(FAPET)
-

P1 (Sumatra), P6
(Other)
I5 (5jt - 7.5jt), I6
(>7.5jt)

Father
Occupation

FO1 (PNS/BUMN/etc),
FO2 (Private
employee/entrepeneur/etc)

-

Father Education

FE2 (High school)

FE1 (below High
school)

FE3 (college)

Mother
Education

ME2 (High school)

ME1 (below
High school)

ME3 (college)

Especially for Y9, this group dominated by students who came from outer
Java and Sumatra (P6), enrolled IPB through BUD (J4), and had high parent’s
income (I7). Most student in Y9 did not fill parent’s former education in
registration form.

12

Column Plot
S5

3

2

Component 2

J3

D1
F

1

Y1
H
FO2

P3
P2

G2
ME2
P4 S1
I4

J2

0
FE3

ME3
I5

Y4
Y7 G

I

FO1

ME1

Y6
B

FE1
I1

D3
A

S2

Y3

I2

FO3

Y8

P1
J5

I3

Y2
Y5

G1
D2

I6

P5

FE2 FO4
J1

E
C

D

-1
P6

I7
FE4

Y9
ME4
J4

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Component 1

Figure 5 Plot of multiple correpondence analysis for students year 2011&2012

CONCLUSION
UN score and GPA TPB had a low positive correlation and had association
after calssified into several classes. From the last three years, the average of GPA
TPB’s students in IPB had increase every year. It could be seen by the percentage
of students who had high UN score and high GPA TPB (Y1), students who had
middle UN score and high GPA TPB (Y4), and students who had low UN score
and high GPA TPB (Y7) also increase every year. Students who had high UN
score but low GPA TPB, and had low UN score and low GPA TPB was decrease
every year. This means that students in IPB gets better every year. Characteristics
of students who had high both UN score and GPA TPB were from faculty
FATETA, FMIPA, FEM, and FEMA, from department which had high popularity
level such as ITP, STK, AGB, ILKOM, GIZI, TIN, SIL, and EKSYAR, and

13

enrolled IPB through SNMPTN Tulis. While characteristics of students who had
high UN score but got low GPA TPB were from faculty of veterinary, enrolled
IPB through UTM and local scholarship, and had high family background.

REFERENCES
Greenacre M. 2006. Multiple Correspondence Analysis. Los Angeles (US): SAGE
Publications, Inc.
Greenacre M. 2007. Correspondence Analysis in Practice Second Edition. Boca
Raton (US): Taylor and Francis Group, LLC.
Himawan, Susilo. 2012. 2013, UN jadi syarat SNMPTN Undangan [Internet].
[May 14 2012]; [downloaded 2014 March 4].
http://kampus.okezone.com/read/2012/05/14/373/628964/2013-un-jadisyarat-snmptn-undangan/large
Hwang H, Tomiuk MA, Takane Y. 2008. Correspondence Analysis, Multiple
Correspondence Analysis and Recent Developments [Internet]. [February 10
2008]. hlm: 15-20; [downloaded 2014 Februari 19].
Kaciak E, Louviere J. 1990. Multiple Correspondence Analysis of Multiple
Choice Experiment Data. Journal of Marketing Research. 27(1): 455-465.
Lebart L, Morineau A, Warwick KM. 1984.Multiple Descriptive Statistical
Analysis. New York (US): John Willey & Sons.
Roux LB, Rouanet H. Multiple Correspondence Analysis. Los Angeles (US):
SAGE Publications, Inc.
Sidiknas. 2013. UN, Upaya Pengendalian Mutu Pendidikan [Internet]. [30
Oktober 2013]; [downloaded 2014 March 4].
www.kemdiknas.go.id/kemdikbud/node/1828
Sumertajaya IM, Mattjik AA. 2011. Sidik Peubah Ganda dengan Menggunakan
SAS. Bogor (ID): Departemen Statistika IPB.

14

APPENDIX
Appendix 1 List of variables and the categories
NO
1

VARIABLE
GROUP

2

GENDER

3

FACULTY

4

DEPARTMENT (Based on
popularity level. Total
registrant per quota)

5

ENROLLMENT SCHEME

6

SCHOOL STATUS

7

STUDENT'S ORIGIN

8

PARENT INCOME

CODE
Y1
Y2
Y3
Y4
Y5
Y6
Y7
Y8
Y9
G1
G2
A
B
C
D
E
F
G
H
I

CATEGORY
UN high GPA high
UN high GPA middle
UN high GPA low
UN middle GPA high
UN middle GPA middle
UN middle GPA low
UN low GPA high
UN low GPA middle
UN low GPA low
Male
Female
FAPERTA
FKH
FPIK
FAPET
FAHUTAN
FATETA
FMIPA
FEM
FEMA

D1

AGB, EKSYAR, GIZI, ILKOM, ITP, SIL,
STK, TIN

D2

AGH, ARL, BDP, BIK, BIO, FKH, IE,
INTP, ITK, KIM, KPM, MAN, MNH

D3

ESL, FIS, GFM, IKK, IPTP, KSHE, MSL,
MSP, MTK, PSP, PTN, SVK, THH, THP,
TMB

J1
J2
J3
J4
J5
S1
S2
S3
P1
P2
P3
P4
P5
P6
I1
I2
I3
I4
I5
I6
I7

USMI
SPMB/UMPTN/SNMPTN
PIN/Prestasi
BUD/Beasiswa/Kemitraan
UTM
Public school
Private school
Foreign school
Sumatra
Banten
DKI Jakarta
West Java
Central Java, DIY, East Java
Other
Rp 0,- s/d Rp 500.000,Rp 500.000,- s/d Rp 1.000.000,Rp 1.000.000,- s/d Rp 2.500.000,Rp 2.500.000,- s/d Rp 5.000.000,Rp 5.000.000,- s/d Rp 7.500.000,Di atas Rp 7.500.000,Blank

15

Appendix 1 List of variables and the categories
NO
9

VARIABLE
FATHER OCCUPATION

CODE
FO1
FO2

10

MOTHER OCCUPATION

11

FATHER EDUCATION

12

MOTHER EDUCATION

FO3
FO4
MO1
MO2
FE1
FE2
FE3
F34
ME1
ME2
ME3
M34

CATEGORY
Civil servant
Private employee, Self-employed,
Professional
Labor, Farmer, Fisherman
Other and Blank
Working
Not Working
Under Senior High School
Senior High School
College
Blank
Under Senior High School
Senior High School
College
Blank

Appendix 2 Percentage of each variables
VARIABLE
GENDER
FACULTY

DEPARTMENT

ENROLLMENT SCHEME

SCHOOL STATUS

STUDENT'S ORIGIN

CATEGORY
G1
G2
A
B
C
D
E
F
G
H
I
D1
D2
D3
J1
J2
J3
J4
J5
S1
S2
S3
P1
P2
P3
P4
P5
P6

2010
40.60%
59.40%
12.28%
4.90%
10.30%
4.64%
11.70%
11.50%
19.95%
15.53%
9.21%
22.36%
41.55%
36.09%
70.48%
14.16%
0.03%
5.68%
9.65%
86.54%
13.21%
0.25%
18.35%
7.13%
8.76%
41.66%
18.83%
5.26%

YEAR
2011
41.13%
58.87%
11.88%
4.65%
11.26%
4.76%
11.32%
12.95%
18.58%
14.65%
9.95%
23.50%
40.14%
36.36%
68.49%
17.60%
0.12%
5.54%
8.25%
87.16%
12.84%
0.00%
16.71%
7.15%
8.67%
45.83%
17.66%
3.99%

2012
38.38%
61.62%
11.43%
5.18%
11.35%
5.48%
10.60%
12.50%
18.75%
14.58%
10.12%
24.44%
39.40%
36.16%
63.57%
18.35%
0.03%
6.81%
11.24%
85.55%
14.40%
0.05%
17.47%
7.51%
8.49%
44.63%
16.99%
4.91%

16

Appendix 2 Percentage of each variables
VARIABLE

CATEGORY

PARENT INCOME

FATHER EDUCATION

FATHER OCCUPATION

MOTHER EDUCATION

MOTHER OCCUPATION

I1
I2
I3
I4
I5
I6
I7
FO1
FO2
FO3
FO4
FE1
FE2
FE3
FE4
ME1
ME2
ME3
M34
MO1
MO2

2010
5.09%
11.05%
18.44%
36.51%
16.14%
11.95%
0.81%
13.51%
31.90%
47.79%
6.80%
54.67%
15.39%
14.91%
15.03%
18.91%
36.51%
37.86%
6.72%
42.11%
57.89%

YEAR
2011
7.33%
11.41%
20.58%
31.36%
16.20%
11.14%
2.00%
17.24%
35.50%
45.83%
1.43%
53.13%
17.30%
17.84%
11.73%
23.67%
38.98%
35.85%
1.49%
40.95%
59.05%

Appendix 3 Non-trivial eigenvalues for students year 2010
No.

Inertia

Proportion

Cumulative

Histogram

1

0.2331

0.057

0.057

2

0.2191

0.0536

0.1105

****************************

3

0.1679

0.041

0.1516

*********************

4

0.1581

0.0386

0.1902

********************

5

0.1453

0.0355

0.2257

******************

6

0.1321

0.0323

0.258

*****************

7

0.1278

0.0312

0.2893

****************

8

0.1183

0.0289

0.3182

***************

9

0.1112

0.0272

0.3454

**************

10

0.1051

0.0257

0.3711

*************

11

0.1013

0.0248

0.3959

*************

12

0.0999

0.0244

0.4203

************

13

0.0991

0.0242

0.4445

************

14

0.0967

0.0236

0.4681

************

15

0.0953

0.0233

0.4914

************

16

0.0945

0.0231

0.5145

************

17

0.0935

0.0229

0.5374

************

18

0.092

0.0225

0.5599

***********

19

0.0906

0.0221

0.582

***********

20

0.0902

0.022

0.6041

***********

******************************

2012
7.16%
13.73%
20.91%
26.82%
15.01%
16.37%
0.00%
20.30%
35.66%
42.55%
1.50%
48.48%
17.44%
20.49%
13.60%
27.64%
36.51%
34.43%
1.42%
39.56%
60.44%

17

Appendix 3 Non-trivial eigenvalues for students year 2010
No.

Inertia

Proportion

Cumulative

Histogram

21

0.0891

0.0218

0.6259

***********

22

0.0886

0.0217

0.6475

***********

23

0.0874

0.0214

0.6689

***********

24

0.0863

0.0211

0.69

***********

25

0.0844

0.0206

0.7106

**********

26

0.084

0.0205

0.7311

**********

27

0.0829

0.0203

0.7514

**********

28

0.079

0.0193

0.7707

**********

29

0.0779

0.019

0.7898

**********

30

0.0769

0.0188

0.8086

*********

31

0.0733

0.0179

0.8265

*********

32

0.0697

0.017

0.8435

********

33

0.0664

0.0162

0.8597

********

34

0.0652

0.0159

0.8757

********

35

0.0627

0.0153

0.891

********

36

0.0602

0.0147

0.9057

*******

37

0.0582

0.0142

0.9199

*******

38

0.056

0.0137

0.9336

*******

39

0.0538

0.0132

0.9468

******

40

0.0529

0.0129

0.9597

******

41

0.0482

0.0118

0.9715

******

42

0.0385

0.0094

0.9809

****

43

0.0371

0.0091

0.99

****

44

0.0318

0.0078

0.9977

****

45

0.0092

0.0023

1

Total

4.0909

*

18

Appendix 4 Column contribution of multiple correspondence analysis from
student year 2010
Component 1
Mass

Inertia

Coord

Corr

Component 2
Contr

Coord

Corr

Contr

Gender
G1

0.037

0.013

0.121

0.01

0.002

0.044

0.001

0

G2

0.054

0.009

-0.083

0.01

0.002

-0.03

0.001

0

A

0.011

0.019

0.157

0.003

0.001

-0.277

0.011

0.004

B

0.004

0.021

-0.167

0.001

0.001

0.232

0.003

0.001

C

0.009

0.02

0.07

0.001

0

-0.017

0

0

D

0.004

0.021

0.185

0.002

0.001

-0.29

0.004

0.002

E

0.011

0.02

0.005

0

0

-0.001

0

0

F

0.01

0.02

-0.312

0.013

0.004

-0.083

0.001

0

G

0.018

0.018

0.416

0.043

0.013

0.32

0.026

0.008

H

0.014

0.019

-0.448

0.037

0.012

-0.025

0

0

I

0.008

0.02

-0.054

0

0

-0.136

0.002

0.001

Faculty

Department
D1

0.02

0.017

-0.329

0.031

0.009

-0.021

0

0

D2

0.038

0.013

0.025

0

0

0.035

0.001

0

D3

0.033

0.014

0.175

0.017

0.004

-0.028

0

0

Enrollment Scheme
J1

0.064

0.007

0.036

0.003

0

0.031

0.002

0

J2

0.013

0.019

-0.473

0.037

0.012

-0.078

0.001

0

J3

0

0.022

-0.951

0

0

-0.585

0

0

J4

0.005

0.021

1.334

0.107

0.039

-0.599

0.022

0.008

J5

0.009

0.02

-0.351

0.013

0.005

0.243

0.006

0.002

School Status
S1

0.079

0.003

-0.04

0.01

0.001

-0.001

0

0

S2

0.012

0.019

0.255

0.01

0.003

-0.031

0

0

S3

0

0.022

0.492

0.001

0

1.869

0.009

0.004

Y1

0.006

0.021

-0.159

0.002

0.001

-0.104

0.001

0

Y2

0.009

0.02

-0.057

0

0

-0.009

0

0

Y3

0.006

0.021

-0.343

0.009

0.003

0.266

0.005

0.002

Y4

0.01

0.02

-0.106

0.001

0

-0.089

0.001

0

Y5

0.016

0.018

-0.083

0.001

0

-0.153

0.005

0.002

Y6

0.016

0.018

-0.15

0.005

0.002

0.276

0.016

0.006

Y7

0.004

0.021

0.449

0.009

0.003

-0.224

0.002

0.001

Y8

0.012

0.019

0.246

0.009

0.003

-0.283

0.012

0.004

Y9

0.012

0.019

0.312

0.015

0.005

0.173

0.005

0.002

Group

19

Appendix 4 Column contribution of multiple correspondence analysis from
student year 2010
Component 1
Mass

Inertia

Coord

Corr

Component 2
Contr

Coord

Corr

Contr

Origin
P1

0.017

0.018

-0.02

0

0

0.025

0

0

P2

0.006

0.021

-0.05

0

0

-0.092

0.001

0

P3

0.008

0.02

-0.023

0

0

-0.015

0

0

P4

0.038

0.013

-0.116

0.01

0.002

0.079

0.004

0.001

P5

0.017

0.018

0.172

0.007

0.002

-0.155

0.006

0.002

P6

0.005

0.021

0.478

0.013

0.005

-0.009

0

0

Parent Income
I1

0.005

0.021

1.84

0.182

0.067

-0.848

0.039

0.015

I2

0.01

0.02

0.969

0.117

0.041

-0.698

0.061

0.022

I3

0.017

0.018

0.345

0.027

0.009

-0.151

0.005

0.002

I4

0.033

0.014

-0.335

0.065

0.016

0.149

0.013

0.003

I5

0.015

0.019

-0.536

0.055

0.018

0.369

0.026

0.009

I6

0.011

0.02

-0.626

0.053

0.018

0.316

0.014

0.005

I7

0.001

0.022

2.369

0.046

0.018

-0.446

0.002

0.001

Father Education
FE1

0.012

0.019

1.404

0.308

0.104

-1.193

0.223

0.08

FE2

0.029

0.015

0.055

0.001

0

-0.281

0.037

0.01

FE3

0.043

0.012

-0.679

0.422

0.086

0.098

0.009

0.002

FE4

0.006

0.021

1.725

0.217

0.079

2.999

0.656

0.254

Father Occupation
FO1

0.05

0.01

-0.376

0.171

0.03

-0.132

0.021

0.004

FO2

0.014

0.019

-0.514

0.048

0.016

-0.084

0.001

0

FO3

0.014

0.019

0.946

0.157

0.052

-0.96

0.161

0.057

FO4

0.014

0.019

0.956

0.161

0.054

1.519

0.408

0.144

Mother Education
ME1

0.017

0.018

1.164

0.316

0.1

-1.015

0.24

0.081

ME2

0.033

0.014

-0.179

0.018

0.005

-0.191

0.021

0.006

ME3

0.034

0.014

-0.71

0.307

0.074

0.162

0.016

0.004

ME4

0.006

0.021

1.697

0.207

0.075

2.987

0.642

0.249

Appendix 5 Non-trivial eigenvalues for students year 2011&2012
No

Inertia

Proportion

Cumulative

Histogram

1

0.2492

0.0609

0.0609

******************************

2

0.1642

0.0401

0.1011

*******************

3

0.1485

0.0363

0.1374

*****************

4

0.1433

0.035

0.1724

*****************

20

Appendix 5 Non-trivial eigenvalues for students year 2011&2012
No

Inertia

Proportion

Cumulative

Histogram

5

0.1322

0.0323

0.2047

***************

6

0.1292

0.0316

0.2363

***************

7

0.1219

0.0298

0.2661

**************

8

0.1079

0.0264

0.2924

************

9

0.1075

0.0263

0.3187

************

10

0.1012

0.0247

0.3434

************

11

0.1003

0.0245

0.368

************

12

0.0986

0.0241

0.392

***********

13

0.0973

0.0238

0.4158

***********

14

0.0966

0.0236

0.4394

***********

15

0.0951

0.0232

0.4627

***********

16

0.0947

0.0232

0.4858

***********

17

0.0943

0.0231

0.5089

***********

18

0.0934

0.0228

0.5317

***********

19

0.0932

0.0228

0.5545

***********

20

0.0913

0.0223

0.5768

**********

21

0.0908

0.0222

0.599

**********

22

0.0903

0.0221

0.6211

**********

23

0.0893

0.0218

0.643

**********

24

0.0884

0.0216

0.6646

**********

25

0.0878

0.0215

0.686

**********

26

0.0855

0.0209

0.7069

**********

27

0.0852

0.0208

0.7278

**********

28

0.084

0.0205

0.7483

**********

29

0.0815

0.0199

0.7682

*********

30

0.0811

0.0198

0.788

*********

31

0.079

0.0193

0.8073

*********

32

0.0787

0.0192

0.8266

*********

33

0.0753

0.0184

0.845

*********

34

0.071

0.0174

0.8623

********

35

0.0668

0.0163

0.8787

********

36

0.065

0.0159

0.8946

*******

37

0.0616

0.0151

0.9096

*******

38

0.0591

0.0144

0.9241

*******

39

0.0579

0.0142

0.9382

******

40

0.0539

0.0132

0.9514

******

41

0.051

0.0125

0.9638

******

42

0.0471

0.0115

0.9754

*****

43

0.0371

0.0091

0.9844

****

44

0.0348

0.0085

0.9929

****

45

0.0289

0.0071

1

Total

4.0909

***

21

Appendix 6 Column contribution of multiple correspondence analysis from
student year 2011&2012
Component 1
Name

Mass

Inert

Coord

Corr

Component 2
Contr

Coord

Corr

Contr

Gender
G1

0.036

0.013

-0.087

0.005

0.001

-0.185

0.022

0.008

G2

0.055

0.009

0.057

0.005

0.001

0.122

0.022

0.005

Faculty
A

0.011

0.02

0.245

0.008

0.003

-0.47

0.029

0.014

B

0.004

0.021

-0.392

0.008

0.003

-0.92

0.044

0.023

C

0.01

0.02

0.229

0.007

0.002

-0.774

0.076

0.038

D

0.005

0.021

-0.005

0

0

-0.916

0.046

0.024

E

0.01

0.02

0.354

0.015

0.005

-0.556

0.038

0.019

F

0.012

0.019

-0.275

0.011

0.004

1.029

0.154

0.075

G

0.017

0.018

0.184

0.008

0.002

0.375

0.032

0.015

H

0.013

0.019

-0.346

0.021

0.006

0.404

0.028

0.013

I

0.009

0.02

-0.224

0.006

0.002

0.355

0.014

0.007

D1

0.022

0.017

-0.393

0.049

0.013

1.065

0.358

0.151

D2

0.036

0.013

-0.066

0.003

0.001

-0.318

0.067

0.022

D3

0.033

0.014

0.332

0.063

0.015

-0.356

0.072

0.025

Department

Enrollment Scheme
J1

0.06

0.008

0.252

0.122

0.015

0.167

0.054

0.01

J2

0.016

0.018

-0.547

0.066

0.02

0.254

0.014

0.006

J3

0

0.022

-1.156

0.001

0

1.45

0.001

0.001

J4

0.006

0.021

0.135

0.001

0

-1.491

0.147

0.076

J5

0.009

0.02

-0.763

0.063

0.021

-0.653

0.046

0.023

School Status
S1

0.078

0.003

0.052

0.017

0.001

0.107

0.072

0.005

S2

0.012

0.019

-0.324

0.017

0.005

-0.68

0.073

0.035

S5

0

0.022

-1.772

0.001

0

3.128

0.003

0.002

Y1

0.018

0.018

-0.068

0.001

0

0.817

0.161

0.072

Y2

0.012

0.019

0.043

0

0

-0.008

0

0

Y3

0.006

0.021

-0.373

0.01

0.003

-0.701

0.035

0.018

Y4

0.014

0.019

0.074

0.001

0

0.558

0.057

0.027

Y5

0.013

0.019

0.094

0.001

0

-0.229

0.009

0.004

Y6

0.007

0.02

-0.179

0.003

0.001

-0.699

0.041

0.021

Y7

0.006

0.021

0.075

0

0

0.309

0.007

0.003

Y8

0.009

0.02

0.192

0.004

0.001

-0.417

0.018

0.009

Y9

0.006

0.021

-0.021

0

0

-1.321

0.13

0.067

Group

22

Appendix 6 Column contribution of multiple correspondence analysis from
student year 2011&2012
Component 1
Name

Mass

Inert

Coord

Corr

Component 2
Contr

Coord

Corr

Contr

Origin
P1

0.016

0.018

-0.039

0

0

-0.485

0.048

0.022

P2

0.007

0.021

-0.236

0.004

0.001

0.267

0.006

0.003

P3

0.008

0.02

-0.226

0.005

0.002

0.371

0.013

0.007

P4

0.041

0.012

-0.131

0.014

0.003

0.06

0.003

0.001

P5

0.016

0.018

0.627

0.082

0.025

0.348

0.025

0.012

P6

0.004

0.021

-0.128

0.001

0

-1.251

0.073

0.039

Parent Income
I1

0.007

0.021

1.459

0.166

0.056

-0.146

0.002

0.001

I2

0.011

0.019

1.226

0.217

0.069

0.133

0.003

0.001

I3

0.019

0.018

0.451

0.053

0.015

0.262

0.018

0.008

I4

0.026

0.016

-0.336

0.046

0.012

0.044

0.001

0

I5

0.014

0.019

-0.769

0.109

0.034

-0.111

0.002

0.001

I6

0.013

0.019

-1.001

0.162

0.051

-0.318

0.016

0.008

I7

0.001

0.022

0.211

0

0

-1.268

0.015

0.008

Father Education
FE1

0.017

0.018

1.463

0.497

0.147

-0.093

0.002

0.001

FE2

0.032

0.014

0.171

0.016

0.004

0.234

0.03

0.011

FE3

0.04

0.012

-0.77

0.467

0.095

-0.105

0.009

0.003

FE4

0.001

0.022

0.188

0.001

0

-1.316

0.026

0.014

Father Occupation
FO1

0.046

0.011

-0.317

0.103

0.019

-0.115

0.014

0.004

FO2

0.016

0.018

-0.506

0.054

0.016

0.294

0.018

0.008

FO3

0.017

0.018

1.079

0.277

0.082

-0.133

0.004

0.002

FO4

0.012

0.019

0.322

0.015

0.005

0.257

0.01

0.005

Mother Education
ME1

0.023

0.016

1.25

0.542

0.147

-0.024

0

0

ME2

0.034

0.014

-0.077

0.004

0.001

0.167

0.017

0.006

ME3

0.032

0.014

-0.837

0.379

0.09

-0.102

0.006

0.002

ME4

0.001

0.022

0.044

0

0

-1.455

0.031

0.017

23

BIOGRAPHY

Noor Hidayatuzzakiah was born in Serang as the second child of four
children from Noormansyah and Maryam on April 19th 1992. She lived and grew
up in the same city before chasing her bachelor degree in IPB through SNMPTN.
She was graduated from SMPN 1 Kota Cilegon in 2007 and SMAN 1 Kota Serang
in 2010.
Statistics is her major and her minor is financial mathematics and actuary.
During her college time, she joined many events as a committe in Statistika Ria,
Pesta Sains Nasional, International Scholarship and Ecucation Expo, and etc. She
also experinced organization in Gamma Sigma Beta as a staff in Department of
Science in 2011/2012 and became the chief of Department Science in 2012/2013.
She is participating as a lecturer assistant in Elementary Statistics subjects in
academic year 2012/2013 and 2013/2014. Not only teaching as lecturer assistant,
she also teachs as a private teacher in Katalis for elementary statistics subject and
calculus.
She loves to watch movies especially genre drama-comedy and action, and
she likes to meet, play, or hang out with friends. On July 2013, she had an
internship in Media Planning Group (Havas Media).