Ethical Analysis of Online Media Journalistic Photos Worth Publishing Based on Images Using the Convolutional Neural Network Method - Rijal, Syamsul; Mardin, Aslam; Anas, Anas; Sharif, Tirta Chiantalia; Sunardi, Sunardi (Journal of Applied Informatics and Computing, 2025)
This study aims to develop and test a Convolutional Neural Network (CNN)-based artificial intelligence model to analyze and classify online media journalistic photos based on ethical criteria for publication suitability (suitable or unsuitable). In the context of digital journalism, the process of filtering sensitive visual content that potentially violates the code of ethics is often time-consuming and prone to subjectivity. Therefore, a CNN model is proposed as an automated solution to identify images containing visual elements deemed unethical. An annotated image dataset was used to train and test the CNN model. The model test results showed effective and robust performance in classifying the ethical suitability of photos. The model achieved a weighted average accuracy of 0.86 (86%) and a weighted average F1 - score of 0.86. Specifically, the model performed very well in identifying "suitable" photos with precision, recall, and F1- score values ranging from 0.88 to 0.89. Performance in the "Unsuitable" class was also relatively strong with an F1 - score of 0.81. Overall, these results confirm that the CNN method has great potential as an efficient and objective decision support system in the visual content editing process. Implementing this model not only speeds up the editorial process but also improves online media's adherence to journalistic ethical standards by minimizing the risk of publishing potentially unethical photos.