Open AccessImplementation of C5.0 Algorithm for Prediction of Student Learning Graduation in Computer System Architecture Subjects
Author(s) -
Nurfadillah Tanjung,
Deci Irmayani,
Volvo Sihombing
Publication year - 2022
Publication title -
sinkron
Language(s) - English
Resource type - Journals
eISSN - 2541-2019
pISSN - 2541-044X
DOI - 10.33395/sinkron.v7i1.11259
Subject(s) - graduation (instrument) , process (computing) , id3 algorithm , computer science , informatics , decision tree , decision tree learning , attendance , set (abstract data type) , data mining , algorithm , machine learning , artificial intelligence , incremental decision tree , engineering , mechanical engineering , programming language , economics , economic growth , operating system , electrical engineering
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.