Classification of Post-Graduation Choices of Secondary and Vocational School Students Using Artificial Neural Networks on RapidMiner

Amanda Puspa Negara; Sutan Krisno Adi; Elkin Rilvani

doi:10.59923/mechatronics.v2i2.536

Authors

Amanda Puspa Negara Universitas Pelita Bangsa
Sutan Krisno Adi Universitas Pelita Bangsa
Elkin Rilvani Universitas Pelita Bangsa

DOI:

https://doi.org/10.59923/mechatronics.v2i2.536

Keywords:

Artificial Neural Networks, Classification, RapidMiner, Post-graduation Options, Secondary School, SMK Students

Abstract

Determining post-graduation choices is an important stage for secondary and vocational school students, but many experience confusion due to a lack of information and career guidance. This can lead to inappropriate decisions that may not be in line with the students' potential and interests. To address this issue, this study aims to classify students' post-graduation choices, whether to continue their studies or go straight to work, using an Artificial Neural Network (ANN) algorithm. Data were obtained through a questionnaire distributed to grade 12 students and processed using RapidMiner software. The process included data preprocessing, model training, and evaluation of classification results. The model showed an accuracy of 73%, with higher accuracy and recall values in the "study" category than in the "work" category. This was due to the imbalance of data between classes. Overall, the ANN method was considered to be quite effective in mapping students' choice tendencies based on the available data and could be a useful tool in data-based career guidance.

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