Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement

(1) * Agung Ramadhanu Mail (Universitas Putra Indonesia YPTK Padang, Indonesia)
(2) Sarjon Defit Mail (Universitas Putra Indonesia YPTK Padang, Indonesia)
(3) Shahab Wahhab Kareem Mail (Erbil Polytechnic university, Iraq)
*corresponding author


Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).


Hybrid Data Mining, K-Means Algorithm, C4.5 Algorithm, Student Achievement




Article metrics

10.29099/ijair.v6i1.225 Abstract views : 409 | PDF views : 90




Full Text



I. Anugraheni, “The Effect of Learning Problem Solving Model Polya on the Ability to Solve Mathematical Problems in Indri Anugraheni Students,” J. Pendidik., vol. 4, no. 1, pp. 1–6, 2013.

W. G. Abdisara, S. Patmanthara, and D. U. Soraya, “Contribution of Student Independence and Availability of Infrastructure to Learning Outcomes of Wireless Network Subjects,” J. Pendidik., vol. 04, no. 2, pp. 55–62, 2019.

I. Amaliah and E. Sudihartinih, “Development of Multimedia Assisted Fraction Concept Teaching Materials to Improve Students' Mathematical Comprehension Ability in Inclusive Schools,” J. Pendidik., vol. 4, no. 2, pp. 6–10, 2019.

E. F. Rusydiyah, E. Purwati, and A. Prabowo, “How to use digital literacy as a learning resource for teacher candidates in Indonesia,” Cakrawala Pendidik., vol. 39, no. 2, pp. 305–318, 2020, doi: 10.21831/cp.v39i2.30551.

S. A. Nulhaqim, D. H. Heryadi, R. Pancasilawan, and M. Ferdryansyah, “The Role of Higher Education in Improving the Quality of Education in Indonesia to Face the 2015 Asean Community Case Study: University of Indonesia, Padjadjaran University, Bandung Institute of Technology,” Share Soc. Work J., vol. 6, no. 2, p. 197, 2016, doi: 10.24198/share.v6i2.13209.

S. Haryati, A. Sudarsono, and E. Suryana, “Implementation of Data Mining to Predict Student's Study Period Using the C4.5 Algorithm (Case Study: Bengkulu Dehasen University),” J. Media Infotama, vol. 11, no. 2, pp. 130–138, 2015.

L. P. Sinambela, “Lecturer Professionalism and Quality of Higher Education,” Populis, vol. 2, no. 4, pp. 579–596, 2017.

Y. Yuzarion, “Factors Affecting Students' Learning Achievement,” Ilmu Pendidik. J. Kaji. Teor. dan Prakt. Kependidikan, vol. 2, no. 1, pp. 107–117, 2017, doi: 10.17977/um027v2i12017p107.

A. Syafi’i, T. Marfiyanto, and S. K. Rodiyah, “Study of Student Achievement in Various Aspects and Affecting Factors,” J. Komun. Pendidik., vol. 2, no. 2, p. 115, 2018, doi: 10.32585/jkp.v2i2.114.

R. Rasmawan, “Students' Critical Thinking Skills Profile and Their Correlation with Academic Achievement Index,” EduChemia (Jurnal Kim. dan Pendidikan), vol. 2, no. 2, p. 130, 2017, doi: 10.30870/educhemia.v2i2.1101.

M. Shaleh, “The Influence of Motivation, Family Factors, Campus Environment and Active Organizations on Academic Achievement,” Phenom. J. Pendidik. MIPA, vol. 4, no. 2, pp. 109–141, 2016, doi: 10.21580/phen.2014.4.2.122.

Nurhasanah, Purwati, and H. Ahmad, “The Influence of the College Entrance Selection System on the Achievement Index of Students of the Department of Mathematics Education, University of Papua (UNIPA),” Pros. Semin. Nas., vol. 03, pp. 114–120, 2015.

F. Nur, M. Zarlis, and B. B. Nasution, “Application of the K-Means Algorithm to New Vocational High School Students for Department Clustering,” InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 1, no. 2, pp. 100–105, 2017, doi: 10.30743/infotekjar.v1i2.70.

E. Elisa, “Analysis and Application of the C4.5 Algorithm in Data Mining to Identify Factors that Cause PT.Arupadhatu Adisesanti Construction Accidents,” J. Online Inform., vol. 2, no. 1, p. 36, 2017, doi: 10.15575/join.v2i1.71.

G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Application of the K-Means Algorithm for Clustering Drug Data,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019, doi: 10.25077/teknosi.v5i1.2019.17-24.

R. Rismayanti, “IImplementation of the C4.5 Algorithm to Determine Scholarship Recipients at Stt Harapan Medan,” J. Media Infotama, vol. 12, no. 2, pp. 116–120, 2017, doi: 10.37676/jmi.v12i2.413.

Jaroji, Danuri, and F. P. Putra, “K-Means To Determine Candidates,” J. Inovtekpolbeng- Seri Inform., vol. 1, no. 1, pp. 87–94, 2016.

W. Dhuhita, “Clustering Using the K-Mean Method to Determine the Nutritional Status of Toddlers,” J. Inform. Darmajaya, vol. 15, no. 2, pp. 160–174, 2015.

A. H. Nasrullah, “Application of the C4.5 Method for Classification of Students with the Potential to Drop Out,” Ilk. J. Ilm., vol. 10, no. 2, pp. 244–250, 2018, doi: 10.33096/ilkom.v10i2.300.244-250.

N. Azwanti, “C4.5 Algorithm to Predict Students Who Repeat a Course (Case Study at Amik Labuhan Batu),” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 11–22, 2018, doi: 10.24176/simet.v9i1.1627.

M. L. Sibuea and A. Safta, “Mapping Student Achievement Using the K-Means Clustering Method,” Jurteksi, vol. 4, no. 1, pp. 85–92, 2017, doi: 10.33330/jurteksi.v4i1.28.

T. Tukino, “Application of the C4.5 Algorithm to Predict Profits at PT SMOE Indonesia,” J. Sist. Inf. Bisnis, vol. 9, no. 1, p. 39, 2019, doi: 10.21456/vol9iss1pp39-46.

A. F. Sallaby and E. Suryana, “Application of Data Mining to Determine the Number of Registered Job Seekers by Age and Education Using K-Means Clustering (Case Study at the Bengkulu Province Manpower and Transmigration Office),” J. Technopreneursh. Inf. Syst., vol. 1, no. 1, pp. 35–38, 2018, doi: 10.36085/jtis.v1i2.28.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


International Journal Of Artificial Intelligence Research

Organized by: Departemen Teknik Informatika STMIK Dharma Wacana
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung

Email: jurnal.ijair@gmail.com

View IJAIR Statcounter

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.