Email this article 
(Scopus ID : 35810354700 Universitas Muhammadiyah Yogyakarta, Indonesia, Indonesia)(2) Widdya Rahmalina
(Department of Informatics Engineering, Faculty of Engineering, Universitas Abdurrab, Pekanbaru, Riau, Indonesia)(3) Juni Zarman
(Department of Informatics Engineering, Faculty of Engineering, Universitas Abdurrab, Pekanbaru, Riau, Indonesia)*corresponding author
AbstractAdolescence 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. KeywordsAdolescence; Algorithms; Naïve Bayes; K-Nearest Neighbor; Classification; Dataset
|
DOIhttps://doi.org/10.29099/ijair.v4i1.118 |
Article metrics10.29099/ijair.v4i1.118 Abstract views : 1547 | PDF views : 249 |
Cite |
Full Text Download
|
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).
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Prodi Teknik Informatika Fakultas Teknologi Bisnis dan Sains
Published by: Universitas Dharma Wacana
Jl. Kenanga No. 03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.