Penerapan Algoritma Neural Network pada Strenght Typologi Bakat untuk Memprediksi Pemilihan Profesi Studi Kasus SMK IT Bina Nusantara Garut

  • Fenty Tristanti Julfia Universitas Mohammad Husni Thamrin Jakarat
  • Eka Satryawati Universitas Mohammad Husni Thamrin Jakarta
  • M Amin Sakaria Universitas Mohammad Husni Thamrin Jakarta
DOI: https://doi.org/10.31331/joined.v8i1.3921

Abstract

Choosing a profession is not a simple thing, it needs to be well designed and planned. By knowing the talents and potentials as well as the many role activities that give rise to several roles or activities that are influenced by these 2 things. This research uses Neural Network Algorithm and Rapidminner to predict the profession (Teacher) of students later after graduating from SMK IT Bina Nusantara Garut. This research uses 8 attributes N (Network), H (Headmen), S (Servicing), T (Thinking), R (Reasioning), Te (Technical) and GI (Generating Idea). The results of data processing bring up 8 types of teaching professions (1 to 7) and other than that are not teachers. Based on research that has been conducted using rapid miner 5.3 tools on the Strength Typology dataset from the Talent Assessment Results tested with the Neural Network method, the RMSE value is 3.465 which shows that the classification results are quite good, so that students with existing parameters can be predicted which professions are teachers and which are not, so that this pattern can be used as a benchmark for the description of the profession (Teacher) so that it can be directed and maximum effort for these students.

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Published
2025-06-30
How to Cite
JULFIA, Fenty Tristanti; SATRYAWATI, Eka; SAKARIA, M Amin. Penerapan Algoritma Neural Network pada Strenght Typologi Bakat untuk Memprediksi Pemilihan Profesi Studi Kasus SMK IT Bina Nusantara Garut. Joined Journal (Journal of Informatics Education), [S.l.], v. 8, n. 1, p. 137-144, june 2025. ISSN 2620-8415. Available at: <https://www.e-journal.ivet.ac.id/index.php/jiptika/article/view/3921>. Date accessed: 05 july 2025. doi: https://doi.org/10.31331/joined.v8i1.3921.
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Vol 8 No 1 (2025): Volume 8 Nomor 1 (2025)
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Articles
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