191 

 

 

THE EFFECT OF AMOUNT OF DATA ON RESULTS OF ACCURACY VALUE 
OF C4.5 ALGORITHM ON STUDENT ACHIEVEMENT INDEX DATA 

 
Anton Sunardi 1*), Sienny Rusli 2, Christina Juliane 3 

 
Information System Master's Program 

STMIK LIKMI  
https://likmi.ac.id 

antonsunardi@gmail.com1*), siennyrusli@gmail.com2, Christina.juliane@likmi.ac.id3 
(*) Corresponding Author 

 
Abstract 

Of the many academic data, data in the form of an achievement index needs to be used in-depth so that it 
does not become a display of numbers and information only. This achievement index evaluation data 
reflects the educational process students and teaching staff carries out in an educational process. This study 
aims to measure the accuracy of data mining processing based on differences in test data by analyzing the 
C4.5 algorithm using RapidMiner as a data processing tool and determining the decisions students can make 
and academic institutions in developing study strategies and educational curricula to be maximized. The 
data processing is carried out by classifying the student achievement index data at a private university using 
data analysis test equipment. The data source comes from kaggle.com, which consists of 1687 data that have 
been processed and processed. The conclusion from the results of this study is that the amount of data turns 
out to have a significant influence on the accuracy value of the C4.5 algorithm, where an accuracy rate of 
91.69% is obtained from the test results of 1687 data with four main attributes, namely IPK1, IPK2, IPK3, 
IPK4 and correctly or not as a label. 
 
Keywords: the amount of data, C4.5, achievement index, data mining 
 
 

Abstrak 
Dari sekian banyak data akademik, data berupa Indeks Prestasi perlu dimanfaatkan secara mendalam agar 
tidak menjadi tampilan deret angka dan informasi saja. Data evaluasi Indeks Prestasi ini merupakan cerminan 
dari proses pendidikan yang dilakukan pelajar, mahasiswa, dan tenaga pengajar dalam suatu proses 
pendidikan. Penelitian ini bertujuan untuk mengukur tingkat akurasi pengolahan data mining berdasarkan 
perbedaan jumlah data uji dengan menganalisa algoritma C4.5 menggunakan RapidMiner sebagai alat bantu 
olah data, dan untuk mengetahui keputusan yang dapat diambil oleh pelajar, mahasiswa dan institusi 
akademis dalam menyusun strategi studi dan kurikulum pendidikan agar lebih maksimal. Proses olah data 
dilakukan dengan mengklasifikasikan data Indeks Prestasi mahasiswa pada sebuah perguruan tinggi swasta 
menggunakan alat uji analisis data. Sumber data berasal dari kaggle.com yang terdiri dari 1687 data yang 
telah diproses dan diolah. Kesimpulan dari hasil penelitian ini adalah jumlah data ternyata memiliki pengaruh  
signifikan terhadap nilai akurasi algoritma C4.5, dimana tingkat akurasi sebesar 91.69 % didapatkan dari 
hasil uji terhadap 1687 data dengan 4 atribut utama yaitu IPK1, IPK2, IPK3, IPK4 dengan Tepat atau tidak 
kelulusan sebagai label. 
 
Kata Kunci: jumlah data, C4.5, indeks prestasi, data mining  
 
 

INTRODUCTION 
 
From time to time, along with the rapid 

development of the world of data and education, a 
person is required to improve knowledge and skills 
to have a good and quality thinking pattern to plan 
strategies in the face of future 
competition. Performance evaluations are stored 
and collected by various universities in the form of 
student achievement index data that universities 

can use as one of the supports that the management 
of education organizers can use to determine their 
educational strategies. The way to use historical 
data is to process it using data mining methods, one 
of which is by applying data classification methods 
so that patterns and rules can produce helpful 
information to support students and educational 
institutions in developing learning strategies and 
educational strategies. 

mailto:antonsunardi@gmail.com1
mailto:siennyrusli@gmail.com2
mailto:Christina.juliane@likmi.ac.id3


 

 
192 

 

 

In previous studies, the level of accuracy in 
processing student graduation data using several 
methods can also be tested using machine 
learning or deep learning techniques, as has been 
done by (Maryanto, 2017). From the explanation in 
the research, data processing requires a tool that 
can measure the level of accuracy. According to 
2020 college statistics (Handini et al., 2020) The 
amount of student data recorded nationally 
compared to the number of students enrolled in the 
Ministry of Education and Culture has a significant 
difference. Therefore, research is needed to 
apply extensive data collection to support the 
industrial revolution as a breakthrough in rapid 
technological progress. 

Research that has been done previously by 
(Budiman & Ramadina, 2015) regarding predictions 
using the data mining classification algorithm 
conducted by (Windarti & Suradi, 2019) applies 
large amounts of data to data analysis concepts that 
can help readers, especially students and teaching 
staff and related agencies. Their field determines 
educational strategies by considering various 
aspects, especially the influence of GPA and the 
accuracy of the Student Achievement Index data 
results. 

 
This research is presupposed to provide 

benefits for students as an early reminder about the 
potential for untimely graduation so that students 
can develop a more effective study plan strategy. 
For academic institutions to foreknown provide 
information in the form of patterns and images that 
can be used to determine policies in minimizing the 
student's potential untimely graduation, which is 
not timely in the scientific field, it is hoped that this 
research can provide changes in data mining testing 
techniques. The classification method uses the C4.5 
algorithm for varying amounts of data so that this 
research can be used as a reference for parties in 
need. 

 
RESEARCH METHODS 

  
The method used in data collection and 

reference consists of analytical techniques used to 
classify data and is done by selecting large amounts 
of data sourced from Kaggle.com, then sorting and 
cleaning steps to be arranged according to research 
needs, namely 1687 data divided into five research 
attributes. Data processing is carried out using the 
RapidMiner data processing tool to find C4.5 
accuracy through careful calculations to find the 
best level of precision to produce data references to 
be implemented. The data is applied through the 
process of extracting patterns from data using 
training data of 80% and testing data of 20%, then 

conducted a comparison test of the accuracy rate of 
the C4.5 algorithm, with the number of data 100, 
400, 900, 1250, 1450, and 1687 from a private 
university in Indonesia, referring to the Student 
Achievement Index. The research flow can be 
presented in figure 1 below:   
 

 
Figure 1. Research Flow 

 
Research Type 

According to (Chapman et al., 2000), CRISP-
DM is a method that connects the background to be 
achieved to data usage and provides an overview of 
the data cycle. Research using similar techniques 
was conducted by (Sabna & Muhardi, 2016) 
regarding the data cycle of the data wealth of college 
students. This study uses six stages of the data 
processing process, namely:  

 
1. Business Understanding 

The focus of this stage is more on 
understanding the objectives and requirements 
than turning this knowledge into the purpose of 
extracting data. 
2. Data Understanding 

Data understanding begins with data collection, 
then getting to know the data, identifying data 
quality, looking for insights, and detecting groups of 
data parts that can generate hypotheses on 
confidential information. 
3. Data Preparation 

(Amir & Abijono, 2018) It is posited that the 
stages in the preparation of data are processed in 
several locations and can be non-sequential as 
modelling tools are performed labelling, attribute 
selection, transformation, and data cleaning to build 
the final dataset. RapidMiner is a data processing 
tool used to find patterns, designs, knowledge, and 
evaluation of large amounts of data. It is an open-
source learning machine that contains data tools for 
pre-processing, classification, rule, and association 



 

 
193 

 

 

so that it is easy to visualize stated. (Muis & 
Affandes, 2015) 
4. Modelling 

This research refers to previous research 
by  (Romadhona, Suprapedi, & Himawan, 2017). In 
his discussion of data modelling using the C4.5 
algorithm decision tree, he explains that there is a 
higher level of accuracy when compared to ID3 and 
Chaid algorithms. Therefore, modelling is 
determined and adjusted to achieve optimal values 
at the modelling stage. At this stage, the authors 
tested the amount of data against the level of 
accuracy by comparing data < 1000, namely 100, 
400, and 900, and data >1000 data, namely 1250, 
1400, and 1687. (Hermawanti, Asriyanik, & Sunarto, 
2019) found an accuracy rate of 68.42% using 145 
test data, comparing < 1000 data i.e. 100, 400, 900 
and data > 1000 data namely 1250, 1400 and 
1687. This study used training data of 80% test data 
of 20% to produce optimal accuracy values 
referring to discussions done previously by (Musu, 
Ibrahim, & Heriadi, 2021) 

 
5. Evaluation 

At the evaluation stage, an assessment of the 
data that has been generated from accuracy values 
based on Confusion Matrix, under curve area values 
(AUC) and execution time (ET). (Olson & Shi, 2007) 
his book entitled Introduction to the Science of 
Business Data Excavation explains that a confusion 
matrix produces four types of classifications, 
namely true Positive (TP), true negative (TN), false 
positive (FP), and false-negative (FN). The 
formulation of the confusion matrix in table form is 
described in table 1 below:  

 
Table 1. Confusion Matrix Table (Olson and Shi 

2007) 
 True Value 

TRUE FALSE 
Predicted 

Value 
True TP (True 

Positive) 
Correct 
result 

FP (False 
Positive) 

Unexpected 
result 

False FN (False 
Negative) 
Missing 
result 

TN (True 
Negative) 

Correct 
absence of 

result 
 
Information: 
True Positive (TP): The amount of positive and 
predicted data to be accurate as Positive. 
False Positive (FP): The amount of harmful data but 
predicted as Positive. 

False Negative (FN): The amount of data that is 
positive but predicted as Negative. 
True Negative (TN): The amount of negative and 
predicted data to be accurate as Negative. 

Referring to research conducted by 
(Azhari, Situmorang, & Rosnelly, 2021), it is 
mentioned that precision is the accuracy of getting 
information from accurate positive and negative 
class data. In addition to the accuracy value, there is 
also a value to recall specific information obtained 
from the recall value. The comparison value in 
testing against research data is obtained from the 
system's predictive value and the tester's prediction 
value called accuracy. Once the results of the 
Confusion Matrix calculation are obtained, 
estimations for precision, recall, and accuracy 
values can be calculated as in the measures in 
Formula 1 below: 

 

Precision = 
𝑇𝑃

𝑇𝑃+𝐹𝑃
  ............................................................... (1) 

 

Recall   = 
𝑇𝑃

𝑇𝑃+𝐹𝑁
  .................................................................... (2) 

 

Accuracy =    
𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
  .............................................. (3) 

 
Formula1. Table Precision, Recall, and Accuracy 
(Olson & Shi, 2007) Formula with the following 
description: 
TP: True Positive 
FP: False Positive 
TN: True Negative 
FN: False Negative 
 
6. Deployment 
 According to (Saefulloh & Moedjiono, 
2013), after the formation of the model, further 
analysis and measurements are carried out at the 
previous stage. At this stage, the most accurate 
model or rule is applied to predict timely graduation 
and can then be used to evaluate new data. The 
concept of data deployment refers to the 
application of a model to predict the accuracy 
results of 91.69% of 1687 data that students, 
educators, and readers can use to monitor data 
processing strategy plans with the implementation 
of data mining if the purpose of the model is to 
increase knowledge about data, knowledge. What is 
obtained needs to be arranged and presented so 
that students, educators, and related institutions 
can use it? The implementation stage produces 
research reports that can be used for repetitive data 
mining. Students or students, educators, and 
educational institutions that carry out deployment 
steps need to be understood to redevelop necessary 
knowledge so that the results have use value. 



 

 
194 

 

 

 
Procedure 

This research was conducted by referring 
to various literature studies in the form of research 
results in journals and books seized according to 
related research needs as a reference for 
writing. The steps of the data excavation and 
processing process procedure using RapidMiner 
that we do can be presented in Figure 2 below: 
 

 
Figure 2. Process of processing data, methods, 
knowledge, evaluation using RapidMiner (author 
source) 

RESULTS AND DISCUSSIONS 
 
(Megna, 2021) In his research in 2021, he 

believed that large amounts of data are enormous 
data sets in volume but grow exponentially with the 
time that traditional data does not have, so it 
requires management tools that can store or 
process it to be efficient. Based on this, the diversity 
of data formats that are increasingly complex and 
grow over time requires data governance using 
techniques and technology. 

Here are the results obtained based on the 
crisp-dm methodology theory 
1. Business Understanding 

The purpose of the study was to determine the 
amount of data in the dataset that affects the degree 
of accuracy in the decision tree classification 
method with the C4.5 algorithm. 
2. Data Understanding 

The student achievement index dataset is in the 
form of public data taken from kaggle.com, with 
1687 data and four attributes containing student 
achievement index values and 1 point of 
appropriate or inappropriate graduation used as a 
label. The initial data is presented in table 2 and 
table 3 below: 
 

Table 2. Initial dataset table 
No SAI1 SAI2 SAI3 SAI4 Yes / 

No 
1 2.30 1.97 1.80 1.56 No 
2 1.81 1.68 1.57 1.86 No 
3 3.07 3.00 2.75 3.21 No 
4 2.71 2.33 2.61 1.98 No 
5 3.17 3.02 3.28 2.96 No 
6 3.16 3.45 3.02 3.06 No 
7 2.72 2.50 2.92 3.00 No 
. . . . . . 

1687 3.18 3.05 3.05 3.27 Yes 
 

Table 3. Dataset description table 
Attribute Name Information 

SAI1 Student Achievement 
Index 1 

SAI2 Student Achievement 
Index 2 

SAI3 Student Achievement 
Index 3 

SAI4 Student Achievement 
Index 4 

Yes / No Right or Not 
Graduation 

 

 

3. Data Preparation 
In the preparatory stage, the dataset was 

adjusted to the data mining processing process 
using RapidMiner, and the decision tree 
classification method was carried out with the C4.5 
algorithm and then obtained 1687 data with four 
attributes. Student Achievement Index and one 
attribute are appropriate or not timely as labels as 
presented in table 4 below: 

 
Table 4. Pre Processing data table 

No SAI1 SAI2 SAI3 SAI4 Yes / 
No 

1 2.30 1.97 1.80 1.56 No 
2 1.81 1.68 1.57 1.86 No 
3 3.07 3.00 2.75 3.21 No 
4 2.71 2.33 2.61 1.98 No 
5 3.17 3.02 3.28 2.96 No 
6 3.16 3.45 3.02 3.06 No 
7 2.72 2.50 2.92 3.00 No 
. . . . . . 

1687 3.18 3.05 3.05 3.27 Yes 



 

 
195 

 

 

 
According to (Dengen, Kusrini, & Luthfi, 2020) The 
calculation steps in the C4.5 algorithm decision tree 
using manual calculation methods are as follows: 
The calculation steps in the C4.5 algorithm decision 
tree using manual calculation methods are as 
follows: 
1. Prepare dataset samples 
2. Calculate the entropy value using the formula:: 

            
 Entropy (S) = ∑ − 𝑝𝑖 ∗ log 2 𝑝𝑖𝑛𝑖=1   ............................ (4) 

 
Information: 
S: set 
N: number of partitions S 
Pi: proportion of Si to SS 
3. Calculate the gain value of each attribute, 

followed by selecting the highest gain value 
 

Gain (S,A) = Entropy(S) -  ∑  ∗ Entropy(Si)ni=1   ...... (5) 
           
Information: 
S: Set 
A: Attributes 
n: Number of attribute partitions A 
| Si |: The amount of data on the ith partition 
| S |: Number of cases in S 
4. Modelling 
 

This stage progressed by testing <1000 data 
with 100, 400, and 900 samples and testing data 
>1000 with examples taken from 1250, 1400, and 
1687.  This study processed the composition of 
training data by 80% and testing data by 20%. Using 
Rapid Miner, data testing was executed to get 
performance vector results through the 
presentation in Table 5 and Table 6. 

Table 5. In performance, Vector data < 1000 
obtained calculation results for 100 data with an 
accuracy result of 70% with confusion matrix on 
prediction "Yes" and prediction result "Yes" of 1, 
and on prediction "Yes" and prediction result "No" 
by 5. The results of the Performance Vector 400 
data calculation were obtained with an accuracy 
result of 85% with confusion matrix on prediction 
"Yes" and prediction result "Yes" of 0, and on 
projection "Yes" and prediction result "No" of 
5. Performance Vector calculation results from 900 
data obtained an accuracy result of 86.11% 
with Confusion Matrix on prediction "Yes" and 
prediction result "Yes" of 1, and on prediction "Yes" 
and prediction result "No" of 12. 
 
 
 
 

 
Table 5. RapidMiner Vector Performance Table 

(Data < 1000) 
Data  < 1000 
Amount of Data Performance Vector 
100 Accuracy 70.00 % 

Confusion Matrix 
True No Yes 
No 1 5 
Yes 1 13 

 

400 Accuracy 85.00 % 
Confusion Matrix 
True No Yes 
No 0 5 
Yes 7 68 

 

900 Accuracy 86.11 % 
Confusion Matrix 
True No Yes 
No 1 12 
Yes 13 154 

 

 
Table 6. RapidMiner Vector Performance Table 

(Data > 1000) 
Data > 1000 
Amount of Data Performance Vector 
1250 Accuracy 86.60 % 

Confusion Matrix 
True No Yes 
No 0 6 
Yes 20 224 

 

1400 Accuracy 90.36 % 
Confusion Matrix 
True No Yes 
No 0 3 
Yes 24 253 

 

1687 Accuracy 91.69 % 
Confusion Matrix 
True No Yes 
No 2 3 
Yes 25 307 

 

 
Table 6. In performance, Vector data >1000 

obtained calculation results for 1250 data with an 
accuracy result of 86.6% with Confusion Matrix on 
prediction "Yes" and prediction result "Yes" of 0, 
and on prediction "Yes" and prediction result "No" 
of 6. The results of the Performance Vector 1400 
data calculation were obtained an accuracy result of 
90.36% with confusion matrix on prediction "Yes" 
and prediction result "Yes" of 0, and on prediction 
"Yes" and prediction result "No" of 
3. Performance Vector 1687 data obtained an 
accuracy result of 91.69% with Confusion Matrix on 
the forecast "Yes" and the prediction result "Yes" of  



 

 
196 

 

 

2, and on the prediction "Yes" and the development 
of the prophecy. "No" of 3. 

 
5. Evaluation 

The evaluation is seen from the results of 
calculating accuracy values based on confusion 

matrix using RapidMiner obtained results as table 7 
below: 

 

 

 
 

Table 7. C4.5 algorithm data evaluation table 
Amount of 

data 
Accuracy Algorithm of C4.5 

100 70.00 %  True no True Yes Class Precision 
Prediction No 1 5 16.67 % 
Prediction Yes 1 13 92.86 % 

Class Recall 50.00 % 72.22 %  
 

400 85.00 %  True no True Yes Class Precision 
Prediction No 0 5 0.00 % 
Prediction Yes 7 68 90.67 % 

Class Recall 0.00 % 93.15 %  
 

900 86.11 %  True no True Yes Class Precision 
Prediction No 1 12 7.69 % 
Prediction Yes 13 154 92.22 % 

Class Recall 7.14 % 92.77 %  
 

1250 89.60 %  True no True Yes Class Precision 
Prediction No 0 6 0.00 % 
Prediction Yes 20 224 91.80 % 

Class Recall 0.00 % 97.39 %  
 

1400 90.36 %  True no True Yes Class Precision 
Prediction No 0 3 0.00 % 
Prediction Yes 24 253 91.34 % 

Class Recall 0.00 % 98.83 %  
 

1687 91.69 %  True no True Yes Class Precision 
Prediction No 2 3 40.00 % 
Prediction Yes 25 307 92.47 % 

Class Recall 7.41 % 99.03 %  
 

 
 
Table 7 shows the percentage value of C4.5 
algorithm calculation accuracy against the number 
of data > 1000, i.e., 1250, 1400, and 1687 and the 
amount of information < 1000, i.e., 100, 400, and 
900. The C4.5 algorithm showed higher accuracy in 
> 1000 data, namely 1250, 1400, and 1687. It 
indicates that data processing 
using RapidMiner from several data samples has a 
high degree of accuracy on data amounting to > 
1000. The highest accuracy obtained is 91.69%, 
with 1687 data. 

 
6. Deployment 

 
 The results of this research are enclosed 
conceive writing comprise information on 
differences in the results of calculating the accuracy 
value of the C4.5 algorithm, which is affected by the 

amount of data. This paper can be functioned as an 
information and reference for research by using the 
following data classification methods. 
 
 

CONCLUSIONS AND SUGGESTIONS 
  

Conclusion 
The study concluded that the Achievement 

Index data processing test 
using RapidMiner against <1000 data showed a 
lower graduation accuracy rate than the graduation 
accuracy rate with the number of data > 1000. The 
difference in the amount of data tested causes a 
difference in the results of the accuracy value. The 
highest accuracy percentage is shown in data 1687 
at 91.69%, while the test data amounting to 100 
produced the lowest accuracy value of 70%. 



 

 
197 

 

 

 
Suggestion 

To obtain the outcome of the achievement index 
grouping, appropriate data support is needed. To 
aim for a more accurate potential value in the C4.5 
algorithm technique, the suitability of the type of 
data and the amount of data is essential because it 
has a significant effect. The more data tested, the 
accuracy level will be determined, therefore it is 
concluded that the amount of data influences the 
result of processing the accuracy value.  

 

 
REFERENCE 

 
Amir, S., & Abijono, H. (2018). Penerapan Data 

Mining untuk Mendukung Pemasaran 
Produk. CAHAYAtech, 7(2), 161–182. 
Retrieved from 
https://ojs.cahayasurya.ac.id/index.php/CT/
article/view/102 

Azhari, M., Situmorang, Z., & Rosnelly, R. (2021). 
Perbandingan Akurasi, Recall, dan Presisi 
Klasifikasi pada Algoritma C4. 5, Random 
Forest, SVM dan Naive Bayes. JURNAL MEDIA 
INFORMATIKA BUDIDARMA, 5(2), 640–651. 
https://doi.org/10.30865/mib.v5i2.2937 

Budiman, I., & Ramadina, R. (2015). Penerapan 
Fungsi Data Mining Klasifikasi untuk Prediksi 
Masa Studi Mahasiswa Tepat Waktu pada 
Sistem Informasi Akademik Perguruan 
Tinggi. JUPITER (Jurnal Penelitian Ilmu Dan 
Teknologi Komputer), 7(1), 39–50. Retrieved 
from 
https://jurnal.polsri.ac.id/index.php/jupiter/
article/view/709 

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., 
Reinartz, T. P., Shearer, C., & Wirth, R. (2000). 
CRISP-DM 1.0: Step-by-step data mining 
guide. In SPSS inc. CRISP-DM consortium. 
Retrieved from https://www.kde.cs.uni-
kassel.de/wp-
content/uploads/lehre/ws2012-
13/kdd/files/CRISPWP-0800.pdf 

Dengen, C. N., Kusrini, K., & Luthfi, E. T. (2020). 
Implementasi Decision Tree Untuk Prediksi 
Kelulusan Mahasiswa Tepat Waktu. 
SISFOTENIKA, 10(1), 1–11. Retrieved from 
https://www.stmikpontianak.ac.id/ojs/index
.php/ST/article/view/484 

Handini, D., Hidayat, F., Attamimi, A. N. R., Putri, D. 
A. V., Rouf, M. F., & Anjani, N. R. (2020). 
Statistik Pendidikan Tinggi Tahun 2020. 
Jakarta: Sekretaris Direktorat Jenderal 
Pendidikan Tinggi. Retrieved from Sekretaris 
Direktorat Jenderal Pendidikan Tinggi 

website: 
https://pddikti.kemdikbud.go.id/asset/data/
publikasi/Statistik Pendidikan Tinggi 
2020.pdf 

Hermawanti, S. N., Asriyanik, A., & Sunarto, A. A. 
(2019). Implementasi Algoritma C4.5 untuk 
Prediksi Kelulusan Tepat Waktu ( Studi 
Kasus : Program Studi Teknik Informatika ). 
Jurnal Ilmiah SANTIKA, 9(1), 853–864. 
https://doi.org/10.37150/jsa.v9i1.552 

Maryanto, B. (2017). Big Data dan Pemanfaatannya 
dalam Berbagai Sektor. Media Informatika, 
16(2), 14-19. Retrieved from 
https://jurnal.likmi.ac.id/Jurnal/7_2017/071
7_02_BudiMaryanto.pdf 

Megna, A. A. K. (2021). Big Data: Development of 
Revolutionary technologies in Business. 
Istanbul. 

Muis, I. A., & Affandes, M. (2015). Penerapan 
Metode Support Vector Machine (SVM) 
Menggunakan Kernel Radial Basis Function 
(RBF) Pada Klasifikasi Tweet. Jurnal Sains, 
Teknologi Dan Industri, 12(2), 189–197. 
Retrieved from http://ejournal.uin-
suska.ac.id/index.php/sitekin/article/view/1
010 

Musu, W., Ibrahim, A., & Heriadi, H. (2021). 
Pengaruh Komposisi Data Training dan 
Testing terhadap Akurasi Algoritma C4.5. 
Seminar Sistem Informasi Dan Teknologi 
Informasi (SISITI), 186–195. Makasar: STMIK 
Dipanegara Makassar. Retrieved from 
https://www.ejurnal.dipanegara.ac.id/index.
php/sisiti/article/view/802 

Olson, D., & Shi, Y. (2007). Pengantar Ilmu 
Penggalian Data Bisnis - Introduction to 
Business Data Mining. Jakarta: Salemba 
Empat. 

Romadhona, A., Suprapedi, S., & Himawan, H. 
(2017). Prediksi Kelulusan Mahasiswa Tepat 
Waktu Berdasarkan Usia, Jenis Kelamin, Dan 
Indeks Prestasi Menggunakan Algoritma 
Decision Tree. Jurnal Teknologi Informasi 
CyberKU, 13(1), 69–83. Retrieved from 
http://research.pps.dinus.ac.id/index.php/Cy
berku/article/view/10 

Sabna, E., & Muhardi, M. (2016). Penerapan Data 
Mining Untuk Memprediksi Prestasi 
Akademik Mahasiswa Berdasarkan Dosen, 
Motivasi, Kedisiplinan, Ekonomi, dan Hasil 
Belajar. Jurnal CoreIT: Jurnal Hasil Penelitian 
Ilmu Komputer Dan Teknologi Informasi, 2(2), 
41. 
https://doi.org/10.24014/coreit.v2i2.2392 

Saefulloh, A., & Moedjiono, M. (2013). Penerapan 
Metode Klasifikasi Data Mining Untuk 
Prediksi Kelulusan Tepat Waktu. InfoSys 



 

 
198 

 

 

Journal, 2(1), 41–54. 
Windarti, M., & Suradi, A. (2019). Perbandingan 

Kinerja 6 Algoritme Klasifikasi Data Mining 
untuk Prediksi Masa Studi Mahasiswa. 

Telematika, 12(1), 14–30. Retrieved from 
https://ejournal.amikompurwokerto.ac.id/in
dex.php/telematika/article/view/778