Research Article
Prediction of Job Suitability of College Graduate Candidates Using Data Mining Algorithms
@INPROCEEDINGS{10.4108/eai.24-10-2018.2280576, author={Vanessa Stefanny and Arief Wibowo}, title={Prediction of Job Suitability of College Graduate Candidates Using Data Mining Algorithms}, proceedings={The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus}, publisher={EAI}, proceedings_a={ICCSET}, year={2018}, month={11}, keywords={data mining classification j48 decision tree algorithm job suitability skkni}, doi={10.4108/eai.24-10-2018.2280576} }- Vanessa Stefanny
Arief Wibowo
Year: 2018
Prediction of Job Suitability of College Graduate Candidates Using Data Mining Algorithms
ICCSET
EAI
DOI: 10.4108/eai.24-10-2018.2280576
Abstract
Large amount and volume of the database in an educational institution can be used to improve educational quality, for example is to know students‘ performance, who are expected to have appropriate work (relevant) with the study program that leads. This study aims to find the best method in predicting job suitability for college graduate candidates that used as one of the standard assessment of National Accreditation Board in Indonesia. The classification modeling was completed by applying the ICT competency standards in Indonesia known as SKKNI into the dataset. This research was conducted by comparing the result of decision tree J48,Naive Bayes,KNN (K=1) and Random Forest. The performance test of the algorithm using K-folds cross-validation method has shown that J48 is the best algorithm for this case with accuracy rate is 85%. It can be concluded that J-48 can be applied in designing prototypes.