Prediction Retweet Using User-Based, Content-Based and Time-Based Features with ANN-Firefly Classification Method - Alif Aqshal; Indri Anugrah Ramadhani (LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram, 2026)
Retweets on the Twitter platform serve as a critical indicator of information dissemination. However, there is a lack of effective predictive systems to estimate whether a tweet will be retweeted. This research aims to develop a retweet prediction model by leveraging a combination of user-based, content-based, and time-based features. The model is constructed using an Artificial Neural Network (ANN) and optimized through the Firefly Algorithm (FA) to enhance classification accuracy. The dataset was collected from Twitter using a data crawling technique via the Tweepy library, focused on the keyword “Covid Vaccination,” resulting in 12,796 Indonesian-language tweets. The collected data underwent preprocessing stages, including text cleaning, tokenization, normalization, and stemming. The ANN-FA model was then trained to classify the likelihood of a tweet being retweeted. Experimental results demonstrate that the ANN-FA model achieved an accuracy of 90.29%, outperforming the baseline ANN model without optimization. These findings indicate that applying the Firefly Algorithm significantly improves classification performance. The contribution of this study lies in developing a retweet prediction system that integrates multi-dimensional features with metaheuristic optimization, which can be utilized to support digital information dissemination strategies on social media platforms