





































Vol. 6, No.1, January 2025 | 39 
 

 

Implementation of the C4.5 Algorithm in Predicting the Interest 

of Prospective Students in Choosing Higher Education 
 

Bambang Triraharjo1*, Prilian Ayu Minarni2, Baskoro3 
1,3Universitas Muhammadiyah Pringsewu/Fakultas Ilmu Komputer, Pringsewu,Lampung, Indonesia 

2Universitas Muhammadiyah Pringsewu/Fakultas Kesehatan, Pringsewu,Lampung, Indonesia 

E-mail: bambangtriraharjo@umpri.ac.id, prilianayuminarni@umpri.ac.id, baskoro@umpri.ac.id 

 
Received: 2024-07-02 | Revised: 2024-10-30 | Accepted: 2025-01-31 

 

Abstract  

 

Lampung Province has many diverse private universities and offers a variety of majors. Due to the 

large number of private universities that exist, competition between universities to attract prospective 

new students is very tight. So, to be able to compete with these universities, the campus needs to predict 

the interests of prospective new students by knowing what factors motivate prospective new students to 

choose a university. The aim of this research is to predict prospective students' interest in choosing a 

university. In this research, the data processed are the results of a survey conducted on prospective new 

students in the Information Systems and Technology Undergraduate Study Program. Data from 

prospective students will be processed using a Data Mining process with the Classification Method. 

Furthermore, using the C4.5 algorithm in the Classification method, literacy was obtained up to node 

4 with 3 assessment data which became a factor in determining prospective students' interest in the 

study program. Furthermore, the results of data processing using the classification method with the 

C4.5 algorithm were tested using Rapid Software. Miner, where an accuracy rate of 100% is obtained. 

The final result of using Data Mining with the C4.5 algorithm is that it is able to predict the interests 

of prospective new students based on assessment factors in choosing a university. 

 
Keywords: Prediction, Classification, Data Mining, C4.5 Algorithm, Decision Tree 

 

 

I. Introduction 

The development of a university, one of which is seen based on the number of students obtained 

every year. In universities, especially private, the acquisition of students every year is the main factor 

in the development of the university. Because, students are the main resource for private universities to 

get funds in carrying out operations in universities. The more students, the greater the income for the 

university, so that it is easy for the university to run campus operations without limited funds. Currently, 

the Information Systems and Technology Study Program, University of Muhammadiyah Pringsewu is 

a new program majoring in Computer Science. In 2021-2022, the Information Systems and Technology 

Study Program will only start opening registration. So currently the Study Program is trying its best to 

get prospective new students to be able to continue their education at the Information Systems and 

Technology Study Program, University of Muhammadiyah Pringsewu. 

In choosing a university, a student will usually look for information about the university they are 

going to [8]. Apart from that, there are also factors that affect prospective students in choosing a 

university, including factors that influence parents, friends, relatives, scholarships, job opportunities 

and others. With these factors, universities can predict what factors are the main drivers for prospective 

students to choose universities, so that universities can make the best decisions to be able to recruit as 

many prospective students as possible [3]. Interest can be interpreted as a high tendency and passion or 

a great desire for something. The purpose of this study is to find out the interest of prospective new 

P-ISSN: 2715-2448 | E-ISSN: 2715-7199 
Vol.6 No.1 January 2025 
Buana Information Technology and Computer Sciences (BIT and CS) 

mailto:prilianayuminarni@umpri.ac.id


 

Vol. 6, No.1, January 2025 | 40 
 

students in a university and how to apply the Data Mining process to predict the request of prospective 

students based on the assessment factors they use [6]. 

Data Mining can also be interpreted as the extraction of new information extracted from chunks of 

big data that helps in decision-making [15]. Data mining is part of Knowledge Discovery in Database 

(KDD) which consists of several stages such as data selection, data pre-processing, transformation, data 

mining and evaluation of results [11]. By using data mining, valuable information in the data set can be 

mined. By using data mining, data on the interests of prospective new students can be processed with 

an algorithm [10]. In data mining there are algorithms that are able to analyze data. In this study, the 

algorithm used is the C4.5 algorithm. The C4.5 algorithm is a classification and prediction method used 

to form a Decission Tree based on training data [9]. A decission tree is a flowchart structure that has a 

Tree, where each internal node indicates an attribute test, each branch represents the test result and the 

leaf node represents the class or class distribution [7]. 

In the previous study, the Theory of Planned Behavior approach was used in predicting student 

interest[5]. This approach measures students' interest in entrepreneurship by prioritizing 3 determinants 

of desire to be entrepreneurial, namely Attitudes Towards Behavior, Subjective Norms, and Behavior 

Control. The result of this approach is a conclusion where attitudes towards behavior do not affect 

students' interest while 2 other factors do [6]. Another algorithm used in previous research to predict 

the interest of vocational school students to enter college is the Naive Bayes algorithm. The data used 

is graduate data in 2018 and 2019 where there are 158 data. Furthermore, the data was processed using 

the Naive Bayes algorithm with 9 attributes, namely expertise, report card scores, UN scores, parental 

work, and others. The data was tested using the RapidMinner application [2]. The results of the 

processor were obtained from the prediction of parental income attributes, report card scores, and 

student desires, which affected students' interest in continuing their education to higher education with 

an accuracy rate of 92.96% [12]. 

In previous research, the C4.5 algorithm has been used to predict the factors that cause students to 

repeat a course. In the study, variables were determined in the form of Number of Semesters, GPA, 

Grades, Economic Condition and Status. Furthermore, data processing is carried out with the C4.5 

algorithm as many as 141 data records as training data. After data processing using the C4.5 algorithm, 

tests were then carried out using the WEKA application. The result of this study is information in the 

form of rules that can predict students who repeat a course [1]. Furthermore, the C4.5 algorithm is also 

used for the classification of student success rate predictions at AMIK Tunas Bangsa. In this study, 

several attribute variables were determined such as Gender, Attendance, Lecture Session, Average 

Score and School Origin. Furthermore, data processing was carried out using the C4.5 algorithm. after 

data processing using the C4.5 algorithm, the results were tested using Rapid Minner. The results of the 

processing of the RapidMinner application found that the results of decisions obtained based on manual 

calculations and using Rapid Minner resulted in an accuracy of 92% against predictions [14]. From the 

previous research that has been explained, the use of the Theory of Planned Behavior approach has not 

been optimal in helping decision-makers. This is because the prediction accuracy level is not accurate, 

while the Naive Bayes algorithm compared to the C4.5 algorithm has a low accuracy level, and also 

does not describe the shape of a decision with a Decession Tree. With the use of the C4.5 algorithm, 

the data will be tested with a high degree of accuracy to predict the interest of prospective new students 

in choosing a university so that the university can make the best decision in attracting prospective new 

students. 

 

II. Methods 

In this study, the stages in the process of getting the best decision using the C4.5 algorithm based 

on the data that have been obtained are explained. The stages carried out can be seen in Figure 1. 



Vol. 6, No.1, January 2025 | 41 
 

 
Figure 1. Research Stages 

A. Data Needs Analysis 

The data needed in this study is data in the form of survey results data on students and criteria data 

that are factors in determining prospective students interested in studying at the campus. There are 5 

criteria and subcriteria that are the determining factors that can be seen in Table 1. 

 

Table 1. Criteria for Determining Factors of Interest in Prospective Students Choosing a Campus  

It Criterion  Subkriteria  

1 Tuition Cheap Affordable Expensive 

2 Facilities Excellent Adequate Enough 

3 Accreditation Excellent Good Enough 

4 Service Excellent Good Enough 

5 Location Near Affordable Far 

 

The reason for choosing the criteria for Accreditation, Services and Facilities is seen in terms of 

student needs for higher education. In terms of tuition fees, because many families in Lampung have a 

lower middle monthly income and in terms of location, many students come from outside the Lampung 

area. Based on the criteria of the above determining factors, a survey was conducted on several students 

based on the criteria of these determining factors so that data on the interest survey of students who 

chose and did not choose to study at the campus were obtained. Furthermore, the data from the survey 

results was carried out Data Transformation for processing. 

 

B. Data Transformation 

Data Transformation is a change in data by changing the initial attribute value into an attribute 

value that is in accordance with the needs of the data in its processing [4]. Based on the results of the 

survey of prospective students' interest in choosing a campus against the criteria, then Data 

Transformation was carried out. For testing using the C4.5 algorithm, 25 sample data is used as a 

training dataset. The results of the survey Data Transformation can be seen in Table 2: 

 

Table 2. Data Transformation Based on Survey Data 
MHS Accreditat

ion 

Tuition Facilities Service Location Interest 

MHS 1 Good Affordable Excellent Good Affordable Yes 

MHS 2 Enough Cheap Enough Enough Affordable Yes 

MHS 3 Enough Affordable Enough Enough Far It 



 

Vol. 6, No.1, January 2025 | 42 
 

MHS 4 Excellent Cheap Excellent Excellent Affordable Yes 

MHS 5 Good Cheap Adequate Good Far Yes 

MHS 6 Excellent Affordable Adequate Good Affordable Yes 

MHS 7 Enough Affordable Enough Enough Far It 

MHS 8 Good Affordable Enough Good Near Yes 

MHS 9 Enough Expensive Enough Enough Affordable It 

MHS 10 Enough Affordable Enough Enough Far It 

MHS 11 Good Cheap Excellent Excellent Near Yes 

MHS 12 Enough Affordable Excellent Enough Far Yes 

MHS 13 Enough Affordable Adequate Good Affordable Yes 

MHS 14 Enough Expensive Adequate Good Near It 

 

The data contained in the Data Transformation is sample data that will be used for testing using the 

C4.5 Algorithm. 

 

C.  C4.5 Algorithm Process 

Based on the results of the Data Transformation that has been obtained, the C4.5 algorithm process 

is then carried out. The C4.5 algorithm has stages or steps in the problem-solving process so that it 

reaches the stage of results. The stages in the C4.5 Algorithm process can be seen as follows [13]: 

1. Calculate the number of "Yes" and "No" cases based on Transformation data. 

2. Determine the Entropy value of each criterion on a case-by-case basis. To calculate the Entropy 

value can use the formula: 

Entropy (S) = ∑𝑛 i=1    −  𝑝𝑖 ∗ 𝑙𝑜𝑔2𝑝𝑖                                                                 (1) 

Where: 

S = case set 

n = number of partitions S 

pi = proportion of Si to S 

3. Determine the highest Gain value based on the Entropy score that has been obtained. To 

calculate the Gain value you can use the formula: 

Gain(S,A) = Entropy(S) – ∑𝑛 i=1   |Si/S| * Entropy(Si)                 (2) 

a. Where: 

b. S = case set 

c. A  = features 

d. n= number of partitions Attribute A 

|Si| = the proportion of Si to S 

|S|= number of cases in S 

4. Generate a Decission Tree To generate the first node, go through processes 1 to 4 until there 

are no records in the empty Decission Tree branch. 

III. Results and Discussions 

A. Data Processing 

Based on the algorithm process described above, then testing is carried out using Data Mining 

software, namely RapidMiner. For testing using the RapidMiner application, 592 data testing data was 

used. Testing is carried out by importing test data in excel format into Rapid Miner software as shown 

in Figure 2. 



Vol. 6, No.1, January 2025 | 43 
 

 
Figure 2. Dataset Snippets 

 

Decision Tree Based Algorithm on the imported data, drag and drop the learning dataset table into the 

process view and set the decession tree operator which can be seen in Figure 3:  

Figure 3. Process View in RapidMiner Software 

 

Based on the process view that has been made, the acquisition of Entropy and Gain values from 

Node 4, then to obtain the Gain value of the entire category is 0, then for the Service and Location 

categories are not a factor in determining student interest in choosing a campus. The shape of the final 

Decession Tree can be seen in Figure 4: 

 

Figure 4. Decession Tree Test Results  

 

From the results of the test using Rapidminer using the Decession Tree algorithm, it is explained that 

the criteria of Facility - Simply have a "No" decision for students' interest. So the Decession Tree above 

is used as the final decision in the prediction of determining the request for prospective students in 

choosing a university. 

 

 



 

Vol. 6, No.1, January 2025 | 44 
 

B. Evaluate Results 

The results of the Decession Tree from the C4.5 algorithm are then evaluated on the results of the 

Decession Tree based on the Transformation data. The evaluation aims to see if there are errors in the 

results of the Decession Tree obtained and whether it is necessary to re-test. It is then translated into the 

form of decisions. Based on the translation of the decision, it can be a tool for a decision-maker to 

predict what criteria factors support the interest of prospective students in choosing the desired 

university. So that the prediction can be taken by the campus to take appropriate actions in dealing with 

this problem. The form of the decision generated through the Rapid Miner software can be seen in 

Figure 5: 

 
Figure 5. Test Results 

 

Based on the tests that have been carried out, it can be concluded that the main factors that affect the 

interest of prospective new students in choosing a university using the C4.5 algorithm are as follows: 

1. If the tuition fee is "Cheap", then the interest of prospective students is "YES" to choose a 

university and if the tuition fee is "Expensive", then the interest of prospective students is "NO" to 

choose a university. 

2. If the tuition fee is "Affordable" and the campus accreditation is "Very Good" or "Good", then the 

interest of prospective students is "YES" to choose a college. 

3. If the tuition fee is "Affordable" and the campus accreditation is "Sufficient" and the campus 

facilities are "Very Good" or "Adequate", then the interest of prospective students is "YES" to 

choose a university, but if the tuition fee is "Affordable" and the campus accreditation is 

"Sufficient" and the campus facilities are "Adequate", then the interest of prospective students is 

"NO" to choose a university. 

 

IV. Conclusions 

Based on the results of the research obtained, it is concluded that the data mining process using the 

C4.5 Algorithm has been able to produce a decision to predict the interest of prospective new students 

in choosing universities based on the criteria factors. Based on the results of data processing using the 

C4.5 Algorithm with 25 sample data, the criteria that are the main factors for the interest of prospective 

new students in choosing a university are the Tuition, Accreditation and Facilities criteria, where Cheap 

Tuition, Excellent or Good Accreditation and Very Good or Adequate Facilities attract the interest of 

prospective new students in choosing a university. Meanwhile, Expensive Tuition, Sufficient 

Accreditation and Sufficient Facilities do not attract the interest of prospective new students in choosing 

a university. Based on testing using Rapid Miner software with 592 data, the results of the Decession 

Tree and the decision between manual testing are the same, with an accuracy level of 100%. So that the 

use of the C4.5 algorithm has produced an assessment factor that can predict the interest of prospective 

students, and the university can predict the interest of prospective new students in the future. 

 

 

 



Vol. 6, No.1, January 2025 | 45 
 

References 

[1] Alwarthan, S. A., Aslam, N., & Khan, I. U. (2022). Predicting Student Academic Performance at 

Higher Education Using Data Mining: A Systematic Review. In Applied Computational 

Intelligence and Soft Computing (Vol. 2022). Hindawi Limited.  

[2] Berrar, D. (2019). Bayes’ Theorem and Naive Bayes Classifier. In S. Ranganathan, M. Gribskov, 

K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology 

(pp. 403–412). Academic Press. 

[3] Feng, L. (2021). Research on Higher Education Evaluation and Decision-Making Based on Data 

Mining. Scientific Programming, 2021. https://doi.org/10.1155/2021/6195067 

[4] Galit Shmueli, P. C. B. I. Y. N. R. P. K. C. L. Jr. (2018). DATA MINING FOR BUSINESS 

ANALYTICS. 

[5] Gerhana, Y. A., Fallah, I., Zulfikar, W. B., Maylawati, D. S., & Ramdhani, M. A. (2019). 

Comparison of naive Bayes classifier and C4.5 algorithms in predicting student study period. 

Journal of Physics: Conference Series, 1280(2).  

[6] Hu, J., & Li, H. (2021). Composition and Optimization of Higher Education Management System 

Based on Data Mining Technology. Scientific Programming, 2021.  

[7] Masters, T. (2018). Data Mining Algorithms in C++. In Data Mining Algorithms in C++. Apress. 

https://doi.org/10.1007/978-1-4842-3315-3 

[8] Naga, J. F., & Tinam-Isan, M. A. C. (2024). EXPLORING THE INFLUENCE OF 

PERSONALITY TRAITS ON STUDENTS’ INFORMATION SECURITY RISK-TAKING 

BEHAVIORS: A BFI ASSESSMENT. Procedia Computer Science, 234, 527–536. 

https://doi.org/10.1016/j.procs.2024.03.036 

[9] Ngoc, P. V., Ngoc, C. V. T., Ngoc, T. V. T., & Duy, D. N. (2019). A C4.5 algorithm for english 

emotional classification. Evolving Systems, 10(3), 425–451. 

[10] Nurmalitasari, Awang Long, Z., & Faizuddin Mohd Noor, M. (2023). Factors Influencing Dropout 

Students in Higher Education. Education Research International, 2023.  

[11] Parteek Bhatia. (2019). Data Mining and Data Warehousing. 

[12] Wang, C. (2021). Analysis of Students’ Behavior in English Online Education Based on Data 

Mining. Mobile Information Systems, 2021. https://doi.org/10.1155/2021/1856690 

[13] Wang, J. (2022). Application of C4.5 Decision Tree Algorithm for Evaluating the College Music 

Education. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/7442352 

[14] Yang, X., & Ge, J. (2022). Predicting Student Learning Effectiveness in Higher Education Based 

on Big Data Analysis. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/8409780 

[15] Ye, H., & Li, C. (2022). Engineering Education Understanding Expert Decision System Research 

and Application. Computational Intelligence and Neuroscience, 2022.  

  


