Implementation of C5.0 Algorithm for Prediction of Student Learning Graduation in Computer System Architecture Subjects


  • Nurfadillah Tanjung Universitas Labuhan Batu
  • Deci Irmayani Universitas Labuhanbatu
  • Volvo Sihombing Universitas Labuhanbatu




Data Mining; Prediction, Graduation; Computer System Architecture; C5.0 . Algorithm


Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the C5.0 algorithm classification using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it.

GS Cited Analysis


Download data is not yet available.


Cynthia, EP, & Ismanto, E. (2018). Decision Tree Algorithm Method C.45 in Classifying Sales Data for Fast Food Outlets Business. Jurassic (Journal of Information Systems Research and Informatics Engineering), 3(July), 1.

With, S., Algorithm, C., & Hidayati, W. (2018). Data Mining Determination of Nurses at Sultan Hospital. 1(2), 1–7.

Febrivani, E., & Winanjaya, R. (2021). Application of Association Data Mining on Drug Inventory. 3(3), 354–365.

Fricles A Sianturi, Hasugian, Paska Marto, Simangunsong Agustina, NB (2019). Data Mining |Weka Theory and Applications. In -: Vol. (Issue).

Kurniawan, YI (2018). Comparison of Naive Bayes Algorithm and C.45 in Data Mining Classification. Journal of Information Technology and Computer Science.

Sowmya, R., & Suneetha, KR (2017). Data Mining with Big Data. Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017.

Suyanto. (2017). Data mining for data classification and clustering. SpringerReference.

Witten, IH, Frank, E., Hall, MA, & Pal, CJ (2016). Data Mining: Practical Machine Learning Tools and Techniques. In Data Mining: Practical Machine Learning Tools and Techniques.

Wu, X., Zhu, X., Wu, GQ, & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering.


Crossmark Updates

How to Cite

Tanjung, N., Deci Irmayani, & Volvo Sihombing. (2022). Implementation of C5.0 Algorithm for Prediction of Student Learning Graduation in Computer System Architecture Subjects. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(1), 274-280.