Training Evaluation Analysis Using Text Mining

Mas Akhmad Nainunis; Imam Yuadi

doi:10.24014/ijaidm.v7i1.27607

Training Evaluation Analysis Using Text Mining

Mas Akhmad Nainunis, Imam Yuadi

Abstract

Training evaluation is an evaluation of the results of training that has been carried out. This evaluation includes technical and non-technical factors which are very important for the company to pay attention to when implementing training in the future. Many companies only implement training evaluations as a formality and only include evaluations that are limited by choice, such as closed questionnaires, training evaluations using open questionnaires can provide the freedom to provide positive or negative input that can be of concern to the company. This research aims to find out the words or topics that appear most frequently in open comments on training evaluation results by using the FP Growth algorithm and association rules to find out the relationship between topics or words from the training evaluation results. They are applied to 516 open-ended comments submitted via the post-training questionnaire. The research results showed that 15 association rules were created using Rapidminer using the FP-Growth algorithm with a minimum support of 0.02 and a minimum confidence of 0.5. All rules have a lift value>1 which indicates that all rules are valid or have a strong association relationship. This research can determine the pattern of comments or suggestions given by workers regarding training evaluation.

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i1.27607

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