Comparison Analysis of K-Nearest Neighbor and Naïve Bayes in Determining Talent of Adolescence

Yessi Jusman(1*), Widdya Rahmalina(2), Juni Zarman(3),


(1) Scopus ID : 35810354700 Universitas Muhammadiyah Yogyakarta, Indonesia
(2) Department of Informatics Engineering, Faculty of Engineering, Universitas Abdurrab, Pekanbaru, Riau
(3) Department of Informatics Engineering, Faculty of Engineering, Universitas Abdurrab, Pekanbaru, Riau
(*) Corresponding Author

Abstract


Adolescence always searches for the identity to shape the personality character. This paper aims to use the artificial intelligent analysis to determine the talent of the adolescence. This study uses a sample of children aged 10-18 years with testing data consisting of 100 respondents. The algorithm used for analysis is the K-Nearest Neigbor and Naive Bayes algorithm. The analysis results are performance of accuracy results of both algorithms of classification. In knowing the accurate algorithm in determining children's interests and talents, it can be seen from the accuracy of the data with the confusion matrix using the RapidMiner software for training data, testing data, and combined training and testing data. This study concludes that the K-Nearest Neighbor algorithm is better than Naive Bayes in terms of classification accuracy.


Keywords


Adolescence; Algorithms; Naïve Bayes; K-Nearest Neighbor; Classification; Dataset

Article Metrics

Abstract view : 126 times

References


Bazmara, M., Movahed, S. V., & Ramadhani, S. (2013). KNN Algorithm for Consulting Behavioral Disorders in Children. Journal of Basic and Applied Scientific Research, 3, 12.

Crocetti, E. (2017). Identity Formation in Adolescence: The Dynamic of Forming and Consolidating Identity Commitments. Child Development Perspectives, 11(2), 145-150. doi: 10.1111/cdep.12226

Dimitrova, R., Chasiotis, A., Bender, M., van de Vijver, & R., F. J. (2013). Collective Identity and Well-Being of Bulgarian Roma Adolescents and Their Mothers. Journal of Youth and Adolescence, 43(3), 375-386. doi: 10.1007/s10964-013-0043-1

Jantan, H., Hamdan, A. R., & Othman, Z. A. (2011). Towards applying data mining techniques for talent management. Paper presented at the International Conference on Computer Engineering and Applications, IPCSIT.

Jantan, H., Hamdan, A. R., & Othman, Z. A. (2012). Intelligent DSS for talent management: a proposed architecture using knowledge discovery approach. Paper presented at the Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication.

Kirimi, J. M., & Moturi, C. A. (2016). Application of Data Mining Classification in Employee Performance Prediction. International Journal of Computer Applications, 146(7), 28-35.

Nikitinsky, N. (2018). Improving Talent Management with Automated Competence Assessment: Research Summary. Paper presented at the Scientific-Practical Conference" Research and Development-2016.

Sharma, A. K. (2013). Data Mining Based Predictions for Employees Skill. International Journal of Advanced Research in Computer Science, 4(3).




DOI: https://doi.org/10.29099/ijair.v4i1.118

________________________________________________________

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
phone. +62725-7850671
Fax. +62725-7850671
Email: info@ijair.id | internationaljournalair@gmail.com | herinurdiyanto@ieee.org 

View IJAIR Statcounter

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