Feature Selection pada Phishing Detection dengan Menggunakan Parallel Genetic Algorithm dan Ensemble Learning - Alles Sandro Oktavio Gandadireja; Henry Novianus Palit; Alvin Nathaniel Tjondrowiguno (Jurnal Infra, 2021)
Phishing sites could become a threat, which retrieves personal information without the user knowing this action. Every site has numerous records, which will be converted to features. Not all features extracted are relevant. Feature selection becomes the main topic of this case. This research uses Genetic Algorithm, using Ensemble Learning as fitness function. This process requires a lot of time, parallelization then used to improve the execution time of the system. The results show that with feature selection, an improvement could be obtained. Parallelization also helps improving execution time up to 2 times faster. Using this system, it is possible to improve the effectiveness of phishing detection.