Media Pembelajaran Pengenalan Citra Pesawat Udara Dengan Memanfaatkan Metode Jaringan Saraf Tiruan - Dzulfiqar, Mohamad Alif; Irmansyah Lubis, Ahmadi (Jurnal Teknologi dan Riset Terapan (JATRA), 2024)
This study developed an image classification model to help learn the recognition of aircraft types using the Probabilistic Neural Network (PNN) method, one of the techniques in artificial neural networks that is often used for image classification. PNN works by classifying categories based on the calculation of the distance between the concentration and probability functions. In the process, PNN consists of four main stages: Input Layer, Pattern Layer, Summation Layer, and Output Layer. This study used 90 test data from three different object classes taken from the available data sets. The test results show that the application of the PNN algorithm in aircraft image classification provides an average accuracy of 81.11 %, which is quite promising to be applied as a learning module for the introduction of aircraft types for Aircraft Maintenance Engineering students at the Batam State Polytechnic. The results of this study show that the PNN method has great potential to help automatic classification and can be optimized to improve the accuracy of classification in further learning.