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 (Politeknik Negeri Batam, 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