Syntax Literate: Jurnal Ilmiah
Indonesia p�ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 7, No. 4, April 2022
COMPARISON OF INFORMATION GAIN AND CHI-SQUARE
SELECTION FEATURES FOR PERFORMANCE IMPROVEMENT OF NAIVE BAYES ALGORITHM ON
DETERMINING STUDENTS WITH NO PIP RECIPIENTS AT SMKN 1 BREBES
Magus
Sarasnomo, Muljono, M. Arief Soeleman
Master of Informatics
Engineering, Faculty of Computer Science, Dian Nuswantoro
University, Semarang, Indonesia
Email: [email protected],
[email protected], [email protected]
Abstract
All policies of the Smart
Indonesia Program (PIP) through the form of the Smart Indonesia Card (KIP) are
issued by the government under the auspices of the Ministry of Education and
Culture (Kemendikbud) through the National Team for
the Acceleration of Poverty Reduction (TNP2K). Helping to alleviate the poor
category of students in order to obtain a proper education, prevent children
dropping out of school, and fulfill their school needs are the goals of the
program. This assistance can be used by students to meet all school needs such
as transportation costs to go to school, the cost of buying school supplies,
and school pocket money. This study aims to compare the Information Gain and
Chi-Square selection features to improve the performance of the Naive Bayes
algorithm in determining poor students who are recipients of the Smart
Indonesia Program (PIP) at SMKN 1 Brebes, to
determine the accuracy of the Naive Bayes, Information Gain and Chi-Square
algorithms. and compare the level of accuracy and determine the attributes that
affect the accuracy. At this stage, collecting relevant and useful research
data, which is collected in the form of literature and data, and processed as
research material. Sources of data used in this study in the form of primary
data collection and secondary data. The primary data collection technique used
in this study was a questionnaire or questionnaire, while the secondary data
obtained in this study was through document files. At this stage, preliminary
data processing is carried out, the data used is student data of SMKN 1 Brebes in 2021. The initial data collection obtained was
703 data, but not all records were used because they had to go through several
stages of initial data processing (data preparation). The results of the Naive
Bayes algorithm accuracy of 90.31% with an AUC of 0.967, after the addition of
the Information Gain selection feature the accuracy becomes 90.88% with an AUC
value of 0.970. The addition of the Information Gain selection feature can help
improve the classification performance of the Naive Bayes algorithm even though
the accuracy is not maximized. The accuracy of the Naive Bayes algorithm is
90.31% with an AUC of 0.967, after the addition of the Chi-Square selection
feature the accuracy becomes 90.88% with an AUC value of 0.970. The accuracy
results are not maximized but the addition of the Chi-Square selection feature
can also improve the classification performance of the Naive Bayes algorithm.
The accuracy of the Naive Bayes algorithm is 90.31% with an AUC of 0.967, after
the addition of the Information Gain selection feature and the Chi-Square
selection feature the accuracy becomes 90.88% with an AUC value of 0.970. The
results of the same accuracy in the use of the Information Gain and Chi-Square
selection features to increase the performance of the Naive Bayes algorithm by
0.57% although the accuracy results are still less than optimal.
Keywords: Information Gain; Chi Square; Algoritma Na�ve
Bayes; PIP
Introduction
It is hoped that this Smart Indonesia
Program (PIP) fund will not happen again for students dropping out of school
due to lack of funds. Providing funds for the Smart Indonesia Program (PIP) to
underprivileged students from elementary school to high school (Rohaeni & Saryono, 2018).
At the Vocational High School (SMK) students will increase every year who
come from rich and poor families. Changes in the condition of the community's
economic income which sometimes cannot be monitored regularly by the parties
concerned will have an impact on the existence of students from wealthy
families who are registered as recipients of the Smart Indonesia Card (KIP) and
students are not registered as recipients of the Smart Indonesia Card (KIP).
from poor families, the policy of the Smart Indonesia Program (PIP) through the
Smart Indonesia Card (KIP) has not been fully targeted in equalizing education.
The unavailability of sufficient data
and information related to students' family income causes the characteristics
of the families of students who are able and cannot be found to be found. Based
on this, the authors conducted research on the family income data of students
to find the characteristics or characteristics of the family groups of students
who were able and unable, as well as proposed new parameters that were more
suitable for determining poor students who received PIP at SMKN 1 Brebes,
namely orphans or students (Setyawati, 2018).
orphans, or orphaned students, where this study aims to increase the level of
accuracy.
It is hoped that the research conducted
can provide results in the form of useful information in making school
decisions, especially those relating to the determination of poor students who
receive PIP at SMKN 1 Brebes. Many studies discuss the prediction and
determination of student PIP with various data mining algorithm models. In
previous studies, prediction techniques and students' PIP determination have
been carried out, such as:
Joy Nashar UtamajaYes, Andi Mentari A.P,
Siti Masnunah (Utamajaya, Putri, & Masnunah, 2020) in 2020 conducted an
analysis of the determination technique to determine prospective PIP
scholarship recipients at SDN 023 Penajam by using the Naive Bayes algorithm
application model. Some of the criteria used in determining scholarship
recipients include: parents' income, parent's occupation, number of dependents,
report cards, rank, distance from home to school as well as academic and
non-academic achievements. The application of the Naive Bayes method in
determining the eligibility of prospective bidikmisi scholarship recipients.
The choice of this method is because it is able to study previous case data
that used as
test data. This research has produced a decision support system application
with an accuracy rate of 97.2%.
(RAINI, 2020) in 2020
also examined the evaluation of the implementation of the Smart Indonesia
Program at SMA Negeri 1 Sembawa. The use of qualitative descriptive research
methods by researchers as a source of primary data and secondary data by collecting
data, interviews, documents and library sources. The results of the study can
be concluded that the implementation of the Smart Indonesia Program (PIP) at
SMAN 1 Sembawa has not run optimally as it should. This can be seen from the
number of students who receive PIP funds are students who are not on target,
this is partly because the data used in determining candidates PIP receiver is
still less accurate.
Several research problems on the
characteristics of the family groups of students who are able and unable as a
determinant of scholarship recipients or PIP using data mining that have been
stated can be carried out an analysis, among others: a. Naive Bayes
is a method used to classify a data set (Annur, 2018).
b. Feature selection is a technique that is often used in pre-processing
data mining by reducing the number of features involved in determining a target
class value and reducing irrelevant features (Djatna & Morimoto, 2008).
Seeing the ability of data mining with
the Naive Bayes algorithm in classifying as well as Information Gain and
Chi-Square in feature selection capabilities, the author wants to do
comparative research of the two feature selection methods with the Naive Bayes
algorithm for determining poor students who are PIP recipients at SMKN 1 Brebes (Betesda, 2020).
In previous studies using Naive Bayes
performance in conducting an analysis of determination techniques to determine
prospective PIP recipients. In this research on determining poor students who
are PIP recipients at SMKN 1 Brebes, we will use an approach with a comparison
of Information Gain and Chi-Square selection features to improve the
performance of Naive Bayes, while the parameters used are 14 parameters.
The data obtained through the mining
process is used to model the algorithm to be used. Result of model This is used to determine the
characteristics of the family group of students who are able and unable so that
it can determine students who cannot afford PIP recipients at SMKN 1 Brebes (Setiawan, 2017). This study aims
to compare the Information Gain and Chi-Square selection features to improve
the performance of the Naive Bayes algorithm in determining poor students who
are recipients of the Smart Indonesia Program (PIP) at SMKN 1 Brebes and determine the accuracy of the Naive Bayes
Algorithm, Information Gain and Chi-Square as well as compare their accuracy.
This research can be useful to minimize the ability of students to get PIP,
help vocational high schools to pay more attention to the characteristics of
the family groups of students who are able and unable so that students who
cannot afford to get PIP and help vocational high schools to determine what
method should be applied so that students unable to get PIP (Uriyalita & Syahrodi, 2020).
Research
Method
Research stages are the steps that
will be taken by researchers in providing an overview and ease of conducting a research. Systematically, the steps in this study are
presented in Figure 1.
Formulation of the
problem Study of literature Data
collection Initial
data processing Data
training and data testing Proposed
method Testing method Evaluation
and accuracy results
Sources of data used in this study in
the form of primary data collection and secondary data. Primary data in the form
of data about the attributes of student data biodata and their families.
Secondary data in the form of class division data taken from school archives (Maulidi, 2016).
The primary data collection technique
used in this study was a questionnaire or questionnaire, while the secondary
data obtained in this study was through document files. The data collected is
703 data with attributes as shown in the following table:
Table 1
Number of Attributes.
|
Attribute Name |
Information |
|
Student's name |
Student's Full Name |
|
Gender |
Man |
|
Woman |
|
|
Class |
XI |
|
XII |
|
|
Domicile |
In the city |
|
Out of town |
|
|
Average Report Score |
Value < 80 |
|
Value > = 80 |
|
|
Report Ratings |
between 1 - 5 |
|
between 6 - 15 |
|
|
between 16 - 36 |
|
|
Distance to School |
Distance < 1 Km |
|
Distance > = 1 Km |
|
|
Academic achievement |
Yes |
|
Not |
|
|
Non-Academic Achievements |
Yes |
|
Not |
|
|
Parents' job |
Laborer |
|
Fisherman |
|
|
Non civil servant |
|
|
Farmer |
|
|
civil servant |
|
|
entrepreneur |
|
|
Parents' Income |
Income < 1,000,000 |
|
Income > 2,000,000 |
|
|
Income between 1,000,000 - 2,000,000 |
|
|
Number of Family Dependents |
Quantity < 2 people |
|
Quantity > = 2 people |
|
|
Student Status in Family |
Orphans |
|
Not an orphan |
|
|
Get PIP |
Yes |
|
Not |
Result and
Discussion
Experiments that
will be carried out by researchers, namely experiments with the Naive Bayes
method, experiments with the Naive Bayes and Information Gain methods,
experiments with the Naive Bayes and Chi-Square methods, and experiments with
the Naive Bayes method and combining the results of the Information Gain and
Chi-Square selection features. .
Calculation of
Conditional Posterior Probability
Calculating
using the Naive Bayes method to determine whether Selvi is in the Yes or No
category as a PIP recipient if it is known that the test data or testing from
Selvi are:
Name = Selvi
Gender = Female
Class = XI
Domicile = In
the city
The average
value of the report card = Value < 80
Report card
rating = between 16 - 36
Distance to
school = Distance > = 1 Km
Academic
achievement = No
Non-academic
achievement = Yes
Parent's
occupation = Labor
Parent's income
= Income between 1,000,000 - 2,000,000
Number of
dependents = Number > = 2 people
Student status
in the family = Not an orphan
Getting PIP =
???
Calculation stage
1:
Based on
equation (2):
P(E)=x/n
Calculation
stage 2:
The next
calculation is continued based on equation (1):
P(x│y)=(P(y│x).P(x))/(P(y))
Calculation
stage 3:
Multiply all the
results of the Yes and No variables.
P(X│Student=Yes)=
0.797 x 0.437 x 0.975 x 0.294 x 0.417 x 0.888 x 0.506 x 0.118
x 0.437 x 0.166
x 0.863 x 0.818 = 6.716
P(X│Student=No)=
0.825 x 0.578 x 0.981 x 0.297 x 0.365 x 0.677 x 0.490
�x 0.106 x 0.251 x 0.529 x 0.502 x 0.985=6.586
Calculation
stage 4:
Result (P|Yes) =
6.716
Result (P|No) =
6.586
Compare
the results of the Yes and No classes because the result (P|Yes) is greater
than (P|No) then the decision status is Selvi "Yes" to get PIP.
Table 2
Confusion Matrix
Calculation in Naive Bayes
|
Class Actual |
Positive Prediction |
Negative Prediction |
Total |
|
Positive |
TP |
FN |
P |
|
Negative |
FP |
TN |
N |
|
Total |
TP + FP |
FN + TN |
P + N |
A model that is trained to predict
whether a YES student gets PIP or Does not get PIP, assuming based on table 1
it is known that the total number of students 702 with 439 YES students getting
PIP and 263 students Not getting PIP, the Confusion Matrix after calculations
using rapidminer studio results in :
� True
Positive (TP) which predicts Yes to get PIP and it is true that the student Yes
gets PIP as many as 390 students.
� True
Negative (TN) which predicts not getting PIP and it is true that the student
does not get PIP as many as 244 students.
� False
Positive (FP) which predicts Yes to get PIP and it turns out that the
prediction is wrong, in fact 19 students don't get PIP.
� False
Negative (FN) which predicts Not getting PIP and it turns out that the
prediction is wrong, it turns out Yes to get PIP a total of 49 students.
So that after
being entered into the table it will look like in table 3.
Table 3
Confusion Matrix results table using Rapidminer Studio
|
|
true No |
true Yes |
|
pred.
No |
244 |
49 |
|
pred.
Yes |
19 |
390 |
Order of information gain value results The
information gain value for each attribute is then sorted from the largest value
to the lowest value, so that the results are as in table 4 as follows:
Table 4
Table of
Results Ranking of Information Gain Value Values
|
Attribute |
Information Gain
Value |
Rangking |
|
Parents' Income |
0,29562 |
1 |
|
Number of Family Dependents |
0,10992 |
2 |
|
Student Status in Family |
0,05748 |
3 |
|
Parent's Job |
0,05177 |
4 |
|
Distance to School |
0,04754 |
5 |
|
Class |
0,01341 |
6 |
|
Report Ratings |
0,00191 |
7 |
|
Gender |
0,00085 |
8 |
|
Domicile |
0,00028 |
9 |
|
Non-Academic Achievements |
0,00024 |
10 |
|
Academic achievement |
0,00016 |
11 |
|
Average Report Score |
0,00001 |
12 |
Confusion Matrix calculation on
Naive Bayes and information gain In table 4 the
attributes ranked 1 to 6 are taken for the Confusion Matrix calculation, while
the other attributes are not used. It is known that the total number of
students 702 with 439 YES students getting PIP and 263 students Not getting
PIP, After calculating with rapidminer
studio it resulted:
�
True Positive (TP) which predicts
Yes to get PIP and it is true that the student Yes gets PIP as many as 393
students.
�
True Negative (TN) which predicts
not getting PIP and it is true that the student does not get PIP as many as 245
students.
�
False Positive (FP) which predicts
Yes to get PIP and it turns out that the prediction is wrong, in fact 19
students don't get PIP.
�
False Negative (FN) which predicts
Not getting PIP and it turns out that the prediction is wrong, it turns out Yes
to get PIP a total of 49 students.
So that
after being entered into the table it will look like in table 5.
Table
5
Confusion
Matrix results using rapidminer studio
|
|
true
No |
true Yes |
|
pred. No |
245 |
49 |
|
pred. Yes |
19 |
393 |
The
following are contingency tables of the attributes:
Gender
Contingency Table
Based on
table 1, it is known:
Number of
male gender = 135, yes = 89, no = 46
Total
female gender = 567, yes = 350, no = 217
Total
column Oi (male) = 135, Oi (yes) = 89 and Oi (no) = 46
Total
column Oi (female) = 567, Oi (yes) = 350 and Oi (no) = 217
Total row
gender Oi (yes) = yes male + yes female = 89+350 = 439
Total row
gender Oi (no) = not male + not female = 46+217 = 263
Total
gender Oi (yes and no) = 439 + 263 = 702
Using the
formula, the value for Ei is calculated for each
attribute
Ei=
(total row x total column)/(total total)
Ei(Yes,Male)= (439 x
135)/702=84,423
Ei(No,Male)= (263 x
135)/702=50,577
Ei(Yes,Female)= (439 x
567)/702=354,577
Ei(No,Female)= (263 x
567)/702=212.423
Total Ei gender (yes male and yes female) = 84,423 + 354,577 =
439,000
Total Ei gender (not male and not female) = 50,577 + 212,423 =
263,000
Total all
gender Ei (yes and no) = 439,000 + 263,000 = 702
Then these
values are entered into the sex contingency table as follows:
Table
6
�Gender Contingency
|
Contingency Table |
||||||
|
Get PIP |
Gender |
Total |
||||
|
Male |
Female |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
89 |
84,423 |
350 |
354,577 |
439 |
439,000 |
|
No |
46 |
50,577 |
217 |
212,423 |
263 |
263,000 |
|
TOTAL |
135 |
135 |
567 |
567 |
702 |
702 |
Using the formula, the Value for calculated �X2 hitung
gender
Value X2 count =
Table 7
�Class Contingency
|
Contingency
Table |
||||||
|
Get
PIP |
Class |
Total |
||||
|
XI |
XII |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
192 |
215,123 |
247 |
223,877 |
439 |
439,000 |
|
No |
152 |
128,877 |
111 |
134,123 |
263 |
263,000 |
|
TOTAL |
344 |
344 |
358 |
358 |
702 |
702 |
Using the formula, the Value for calculated
Value
13,008 > 3,841, then H0 is rejected. There is an effect of the
relationship between class and Get PIP.
Table 8
�Domicile Contingency
|
Contingency
Table |
||||||
|
Get
PIP |
Domicile |
Total |
||||
|
In the city |
Out of town |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
428 |
428,994 |
11 |
10,006 |
439 |
439,000 |
|
No |
258 |
257,006 |
5 |
5,994 |
263 |
263,000 |
|
TOTAL |
686 |
686 |
16 |
16 |
702 |
702 |
Using the formula, the Value for calculated
Value
0,270 ≤ 3,841, then H0 is accepted. No, there is a relationship
between domicile and Get PIP.
Table 9
Contingency
of Average Value of Report Cards
|
Contingency Table |
||||||
|
Get
PIP |
Rapor Average Value |
Total |
||||
|
Value < 80 |
Value > = 80 |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
129 |
129,449 |
310 |
309,551 |
439 |
439,000 |
|
No |
78 |
77,551 |
185 |
185,449 |
263 |
263,000 |
|
TOTAL |
207 |
207 |
495 |
495 |
702 |
702 |
Using the formula, the Value for calculated
Value
0,006 ≤ 3,841, then H0 is accepted. No, there is a relationship
between the average value of the report card and Get PIP.
Table 10
Contingency
of Reporting Ratings
|
Contingency Table |
|
||||||
|
Get PIP |
Rapor Ranking |
|
|||||
|
between
1 - 5 |
between
6 - 15 |
between
16 - 36 |
|
||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
|
Yes |
62 |
63,786 |
194 |
200,739 |
183 |
174,474 |
|
|
No |
40 |
38,214 |
127 |
120,261 |
96 |
104,526 |
|
|
TOTAL |
102 |
102 |
321 |
321 |
279 |
279 |
|
|
TOTAL |
|||||||
|
Oi |
Ei |
|
|||||
|
439 |
439,000 |
|
|||||
|
263 |
263,000 |
|
|||||
|
702 |
702 |
|
|||||
Using the formula, the Value for calculated
Value
Table 11
Distance to School Contingency.
|
Contingency Table |
||||||
|
Get PIP |
Distance to School |
Total |
||||
|
Distance <
1 Km |
Distance >
= 1 Km |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
49 |
83,798 |
390 |
355,202 |
439 |
439,000 |
|
No |
85 |
50,202 |
178 |
212,798 |
263 |
263,000 |
|
TOTAL |
134 |
134 |
568 |
568 |
702 |
702 |
Using the formula, the Value
for calculated
Value
47,669 > 3,841, then H0
is rejected. There is a relationship between distance to school and Get PIP.
Table 12
Contingency of Academic Achievement.
|
Contingency
Table |
||||||
|
Get
PIP |
Academic
Achievement. |
Total |
||||
|
Yes |
No |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
217 |
219,500 |
222 |
219,500 |
439 |
439,000 |
|
No |
134 |
131,500 |
129 |
131,500 |
263 |
263,000 |
|
TOTAL |
351 |
351 |
351 |
351 |
702 |
702 |
Using the formula, the Value for calculated
Value
0,152 ≤ 3,841, then
H0 is accepted. No, there is a relationship between academic achievement and
Get PIP.
Table 13
Contingency of Non-Academic Achievements
|
Contingency
Table |
||||||
|
Get
PIP |
Non-Academic
Achievements |
Total |
||||
|
Yes |
No |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
52 |
50,028 |
387 |
388,972 |
439 |
439,000 |
|
No |
28 |
29,972 |
235 |
233,028 |
263 |
263,000 |
|
TOTAL |
80 |
80 |
622 |
622 |
702 |
702 |
Using the formula, the Value
for calculated
Df (Non-Academic
Achievements) = (2-1) x (2-1) = 1
Value
Value
Table 14
Parents' Occupational Contingency.
|
Contingency
Table |
||||||
|
Get
PIP |
Parents'
job |
|||||
|
Workers |
Fisherman |
Non PNS |
||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
192 |
161,342 |
7 |
9,380 |
9 |
10,006 |
|
No |
66 |
96,658 |
8 |
5,620 |
7 |
5,994 |
|
TOTAL |
258 |
258 |
15 |
15 |
16 |
16 |
|
Contingency
Table |
||||||
|
Get
PIP |
Parents'
job |
|||||
|
Farmer |
PNS |
entrepreneur |
||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
55 |
55,031 |
1 |
11,882 |
175 |
191,359 |
|
No |
33 |
32,969 |
18 |
7,118 |
131 |
114,641 |
|
TOTAL |
88 |
88 |
19 |
19 |
306 |
306 |
|
TOTAL |
|
|
Oi |
Ei |
|
439 |
439,000 |
|
263 |
263,000 |
|
702 |
702 |
Using the formula, the Value for calculated
47,766 > 11,070, then H0 is rejected. There is an effect of the
relationship between parents' work and Get PIP.
|
Value |
Table 15
Contingency of Parents' Income
|
Contingency
Table |
||||||
|
Get
PIP |
Parents'
Income |
|||||
|
Income < 1.000.000 |
Income > 2.000.000 |
Income between
1.000.000 - 2.000.000 |
||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
365 |
267,652 |
1 |
38,772 |
73 |
132,575 |
|
No |
63 |
160,348 |
61 |
23,228 |
139 |
79,425 |
|
TOTAL |
428 |
428 |
62 |
62 |
212 |
212 |
|
TOTAL |
|
|
Oi |
Ei |
|
439 |
439,000 |
|
263 |
263,000 |
|
702 |
702 |
Using the formula, the Value for calculated
Value
264,186 > 5,991, then H0 is rejected. There
is an effect of the relationship between parents' income and getting PIP.
Table 16
Contingency of Number of Family Dependents
|
Contingency
Table |
||||||
|
Get
PIP |
Number
of Family Dependents |
Total |
||||
|
Number of Family Dependents < 2 people |
Number of Family Dependents > = 2 people |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
60 |
119,443 |
379 |
319,557 |
439 |
439,000 |
|
No |
131 |
71,557 |
132 |
191,443 |
263 |
263,000 |
|
TOTAL |
191 |
191 |
511 |
511 |
702 |
702 |
Using the formula, the Value is calculated for the number of family
dependents
Value
108,477 > 3,841, then H0 is rejected. There is a relationship
between the number of dependents in the family and getting PIP.
Table 17
Contingency of Student Status in Families
|
Contingency
Table |
||||||
|
Get
PIP |
Student
Status in Families |
Total |
||||
|
Orphans/Orphans |
Not Orphans |
|||||
|
Oi |
Ei |
Oi |
Ei |
Oi |
Ei |
|
|
Yes |
80 |
52,530 |
359 |
386,470 |
439 |
439,000 |
|
No |
4 |
31,470 |
259 |
231,530 |
263 |
263,000 |
|
TOTAL |
84 |
84 |
618 |
618 |
702 |
702 |
Using the formula, Value is
calculated for the status of students in the family
Value
Order
of results Value Chi Square
The
chi-square value for each attribute is then sorted from the largest Yesng Value
to the lowest value, so that the results are as shown in table 4.5 as follows:
Table
18
�Table of results for the ranking of Value
chi-square
|
Attribute |
Value Chi-Square |
Rangking |
|
Parents' Income |
264,186 |
1 |
|
Number of Family
Dependents |
108,477 |
2 |
|
Parents' job |
47,766 |
3 |
|
Distance to
School |
47,669 |
4 |
|
Student Status
in Family |
43,556 |
5 |
|
Class |
13,008 |
6 |
|
Report Ratings |
1,849 |
7 |
|
Gender |
0,820 |
8 |
|
Domicile |
0,270 |
9 |
|
Non-Academic
Achievements |
0,234 |
10 |
|
Academic
achievement |
0,152 |
11 |
|
Report Average
Value |
0,006 |
12 |
�
Confusion
Matrix calculations on Naive Bayes and chi-square
In table 18 the attributes ranked 1 to 6 are taken
to calculate the Confusion Matrix, while the other attributes Yes No are used.
It is known that the total number of students 702 with 439 YES students getting
PIP and 263 students No getting PIP, After calculating with rapidminer studio
it resulted:
�
True Positive
(TP) Yesitu predicts Yes to get PIP and it is true that the student Yes gets
PIP as many as 393 students.
�
True Negative
(TN) Yesitu predicts No to get PIP and it is true that the student No. gets PIP
as many as 245 students.
�
False Positive
(FP) Yes, it predicts Yes to get PIP and yes, it predicts wrongly, Yesta No
gets PIP for 18 students.
�
False Negative
(FN) Yesitu predicts No to get PIP and yesta predicts wrongly, Yesta turns to
get PIP as many as 46 students.
So that after being entered into the table it will
look like in table 19 as follows:
Table 19
Confusion
Matrix results using rapidminer studio
|
|
true No |
true Yes |
|
pred. No |
245 |
46 |
|
pred. Yes |
18 |
393 |
Based on
the confusion matrix table above, the performance of using the Naive Bayes and
chi-square classification method can be measured by calculating the value of
accuracy, precision and recall.
Confusion Matrix calculation in
Naive Bayes and the results of the information gain and chi-square selection
features
The results
of combining the Yesng attribute are used in the
results of the information gain and chi-square selection features. It is known
that the total number of students 702 with 439 YES students getting PIP and 263
students No getting PIP, After calculating with rapidminer studio it resulted:
�
True Positive (TP) Yesitu predicts Yes to get PIP and it is true that the
student Yes gets PIP as many as 393 students.
�
True Negative (TN) Yesitu predicts No to get PIP and it is true that the
student No. gets PIP as many as 245 students.
�
False Positive (FP) Yes, it
predicts Yes to get PIP and yes, it predicts wrongly, Yesta
No gets PIP for 18 students.
�
False Negative (FN) Yesitu predicts No to get PIP and yes, Yesta
predicts wrongly, turns Yesta Yes gets 46 students
PIP.
So that
after being entered into the table it will look like in table 20 as follows:
Table 20
Confusion Matrix results
using rapidminer studio
|
|
true No |
true Yes |
|
pred. No |
245 |
46 |
|
pred. Yes |
18 |
393 |
Based on this research can discuss the
evaluation of the accuracy of the results of the experimental method to be compared
as follows:
Table
21
Attributes
of the Naive Bayes Algorithm
|
Naive Bayes
Algorithm |
|
Attribute |
|
Student's name |
|
Getting PIP
(Label) |
|
Gender |
|
Class |
|
Domicile |
|
Report Average
Value |
|
Report Ratings |
|
Distance to
School |
|
Academic
achievement |
|
Non-Academic
Achievements |
|
Parents' Jobs |
|
Parents' Income |
|
Number of Family
Dependents |
|
Student Status
in Family |
�
In the Naive Bayes algorithm, all attributes are
used for calculations.
Table
22
Information
Gain Selection Feature Attributes
|
Information Gain |
||
|
Attribute |
Value |
Rangking |
|
Student's name |
|
|
|
Getting PIP
(Label) |
|
|
|
Parents' Income |
0,29562 |
1 |
|
Number of Family
Dependents |
0,10992 |
2 |
|
Student Status
in Family |
0,05748 |
3 |
|
Parent's Job |
0,05177 |
4 |
|
Distance to
School |
0,04754 |
5 |
|
Class |
0,01341 |
6 |
|
Report Ratings |
0,00191 |
7 |
|
Gender |
0,00085 |
8 |
|
Domicile |
0,00028 |
9 |
|
Non-Academic Achievements |
0,00024 |
10 |
|
Academic
achievement |
0,00016 |
11 |
|
Report Average
Value |
0,00001 |
12 |
Table
24
Attributes
of Information Gain Selection Feature Results Used
|
Information Gain |
||
|
Attribute |
Value |
Rangking |
|
Student's name |
|
|
|
Getting PIP
(Label) |
|
|
|
Parents' Income |
0,29562 |
1 |
|
Number of Family
Dependents |
0,10992 |
2 |
|
Student Status
in Family |
0,05748 |
3 |
|
Parent's Job |
0,05177 |
4 |
|
Distance to
School |
0,04754 |
5 |
|
Class |
0,01341 |
6 |
Attributes
of the Chi Square Selection Feature
|
Chi-Square |
||
|
Attribute |
Value |
Rangking |
|
Student's name |
|
|
|
Getting PIP
(Label) |
|
|
|
Parents' Income |
264,186 |
1 |
|
Number of Family
Dependents |
108,477 |
2 |
|
Parents' Jobs |
47,766 |
3 |
|
Distance to
School |
47,669 |
4 |
|
Student Status
in Family |
43,556 |
5 |
|
Class |
13,008 |
6 |
|
Report Ratings |
1,849 |
7 |
|
Gender |
0,820 |
8 |
|
Domicile |
0,270 |
9 |
|
Non-Academic
Achievements |
0,234 |
10 |
|
Academic
achievement |
0,152 |
11 |
|
Average Report
Score |
0,006 |
12 |
Table
26
Attributes
of the Chi Square Selection Feature Results used
|
Chi-Square |
||
|
Attribute |
Value |
Rangking |
|
Student's name |
|
|
|
Getting PIP
(Label) |
|
|
|
Parents' Income |
264,186 |
1 |
|
Number of Family
Dependents |
108,477 |
2 |
|
Parents' job |
47,766 |
3 |
|
Distance to
School |
47,669 |
4 |
|
Student Status
in Family |
43,556 |
5 |
|
Class |
13,008 |
6 |
Attributes
of the Merged Information Gain and
Chi-Square
Selection Feature Features used
|
Attributes |
|
|
Information Gain |
Chi-Square |
|
Student's name |
Student's name |
|
Getting PIP (Label) |
Getting PIP (Label) |
|
Parents' Income |
Parents' Income |
|
Number of Family
Dependents |
Number of Family
Dependents |
|
Student Status
in Family |
Parents' job |
|
Parents' Jobs |
Distance to
School |
|
Distance to
School |
Student Status
in Family |
|
Class |
Class |
Table
28
Results
of method comparison
|
Algorithm |
Accuracy |
AUC (optimistic) |
|
Naive Bayes |
90,31
% |
0.967 |
|
Naive Bayes and Information Gain |
90.88% |
0.970 |
|
Naive Bayes and Chi-Square |
90.88% |
0.970 |
|
Naive Bayes, Information Gain and Chi-Square |
90.88% |
0.970 |
Based on
the results of the calculations in this study, the Naive Bayes classification
with information gain and chi-square selection features resulted in higher accuracy
and AUC values than the Naive Bayes classification without any selection
features added. Naive Bayes accuracy value is 90.31% with AUC of 0.967. In this
study, the performance of naive bayes increased by 0.57% after using the
information gain and chi-square selection features. After doing the
calculations in this study, it turns out that the accuracy and AUC results show
the same value, namely the accuracy value of 90.88% and the AUC value of 0.970
in the classification of the naive bayes algorithm with information gain and
the naive bayes algorithm with chi-square and naive bayes with information gain
and chi-square.
Conclussion
This study can be concluded that the
accuracy of the Naive Bayes algorithm is 90.31% with an AUC of 0.967, after the
addition of the Information Gain selection feature the accuracy becomes 90.88%
with an AUC value of 0.970. The addition of the Information Gain selection
feature can help improve the classification performance of the Naive Bayes algorithm
even though the accuracy is not maximized. The accuracy of the Naive Bayes
algorithm is 90.31% with an AUC of 0.967, after the addition of the Chi-Square
selection feature the accuracy becomes 90.88% with an AUC value of 0.970.
Accuracy results are not maximized but the addition of the Chi-Square selection
feature can also improve the classification performance of the Naive Bayes
algorithm. The accuracy of the Naive Bayes algorithm is 90.31% with an AUC of
0.967, after the addition of the Information Gain selection feature and the
Chi-Square selection feature the accuracy becomes 90.88% with an AUC value of
0.970. The results of the same accuracy in the use of the Information Gain and
Chi-Square selection features to increase the performance of the Naive Bayes
algorithm by 0.57% although the accuracy results are still less than optimal.
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|
Copyright holder: Magus Sarasnomo, Muljono, M. Arief Soeleman (2022) |
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First publication right: Syntax Literate: Jurnal Ilmiah
Indonesia |
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