Pengaruh Pengembangan Aplikasi Web Deteksi Clickbait Pada Judul Berita Online Berbahasa Indonesia Menggunakan Metode Naïve Bayes Classifier - Muhammad Ro’is Misbakhul Munir; Budi Hartono; Eko Siswanto (Jurnal Informatika Dan Tekonologi Komputer (JITEK), 2026)
This study aims to develop a web-based application for detecting clickbait in Indonesian online news headlines using the Naïve Bayes Classifier method, with case studies on Kompas.com and Detik.com. The problem underlying this research is the widespread use of sensational headlines that may mislead readers. The system is developed as a web application using XAMPP as the local server environment, Apache as the web server, and MySQL as the database. The system process begins with users entering news headlines, followed by text preprocessing, which includes tokenization, filtering, and word weighting before classification is performed using the Naïve Bayes algorithm. The application also provides model training and search history features that run in the local XAMPP environment to store previous detection results. The testing results show that the system can automatically classify news headlines into clickbait and non-clickbait categories with a fast processing time. The implementation of the Naïve Bayes method is considered effective because it is simple, lightweight, and suitable for short text classification, such as news headlines. However, the system’s accuracy is still influenced by the amount of training data used. This application is expected to help users identify clickbait news headlines, improve public digital literacy, and support more objective and reliable information consumption.