In this study, we use a Naive Bayes classifier to develop a recommender system for data collected in the defense quality field. The Defense Agency for Technology and Quality (DTAQ) collects data of customer complaints, which is generated during the operation and maintenance of weapon systems and munitions; however, data collection is difficult due to unstructured data such as text and image data. Moreover, there is the possibility of human error, especially when text data is assigned a specific class.
Therefore, we propose a model for classification recommendation based on Naive Bayes that is one of the most widely used supervised learning models. The model's performance is indicated by the recognition rate, which is 92% to 98%. We expect that high-quality data can be collected by introducing such a recommending scheme into the defense quality field, especially the DTAQ data system. High-quality data will help to improve the quality of big data and artificial intelligence (AI) analysis results in the defense field.