Classification Model for Graduation on Time Study Using Data Mining Techniques with SVM Algorithm

Publons ID34095549
Wos IDWOS:000472799700006
Doi10.1063/1.5097475
TitleClassification Model for Graduation on Time Study Using Data Mining Techniques with SVM Algorithm
First AuthorZulfa, Mulki Indana; Fadli, Ari; Ramadhani, Yogi;
Last Author
AuthorsZulfa, MI; Fadli, A; Ramadhani, Y;
Publish Date2019
Journal Name1ST INTERNATIONAL CONFERENCE ON MATERIAL SCIENCE AND ENGINEERING FOR SUSTAINABLE RURAL DEVELOPMENT
Citation3
AbstractEvery college must have academic data. This academic data includes student data, course data, study plan data, achievement index, graduation data, and study period. Study period and percentage of students graduating on time become one of the important indicators in the assessment component of study program accreditation by BAN PT. The minimum number of students graduating on time is due to the unattended study period of students by study programs. The concept of data mining can be implemented to gain knowledge from these academic data. Data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract useful knowledge (information) from a large database. This knowledge will be difficult to know if only using manual processes without using data mining technology. One technique used by data mining is the SVM algorithm. The SVM algorithm will build a classification model and determine which variable has the most influence on graduation on time. As a result, the semester achievement index becomes the main factor determining whether students will graduate on time or not.
Publish TypeBook in series
Publish Year2019
Page Begin(not set)
Page End(not set)
Issn0094-243X
Eissn
Urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000472799700006
AuthorDr. MULKI INDANA ZULFA, S.T, M.T
File20571.pdf