<![CDATA[This study aims to develop a gender classification system using facial images by utilizing an artificial neural network algorithm. In this context, facial images taken from various sources are used as input data for a neural network model that will classify faces into two categories, namely male and female. This study begins with the collection of facial image datasets which are then processed to extract important features that are relevant in the classification. The model training process is carried out using an artificial neural network algorithm, using training data that has been processed to optimize classification accuracy. Evaluation is carried out by comparing the classification results to separate test data, and model accuracy is calculated using a confusion matrix and other metrics. Interim results show that the applied neural network model can achieve a significant level of accuracy in classifying gender based on facial images, with an accuracy value reaching 87.33%. This study shows that the neural network algorithm has great potential to be applied in gender recognition through facial images, and can be used as a basis for developing more complex identification systems. In the future, this system can be further optimized by applying regularization and data augmentation techniques to improve classification performance in various lighting conditions and facial angles.]]>
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