Analisis Prediksi Kata Kunci Situs Web MonsterMAC dengan Metode Long Short-Term Memory (LSTM) - Hanif Assalmi, Fityan; Syaifullah Jauharis Saputra, Wahyu; Muhaimin, Amri (LPPM STT Terpadu Nurul Fikri, 2024)
Amid increasingly fierce competition in the digital realm, many companies are striving to increase the number of visitors to their websites. One such competing company is MonsterMAC, a startup. This research aims to provide early warnings and analyze relevant keywords on the MonsterMAC website using the Long Short-Term Memory (LSTM) method. Visitor data from Google Analytics and keyword data from Google Trends for the period July 22, 2022, to July 15, 2023, have been collected and processed through several stages, such as preprocessing, model design, LSTM training, and testing, as well as visualization and interpretation of results. The modeling results show satisfactory performance, with MAE Train Real User = 0.0615, Vending Machine = 0.0218, IoT = 0.0284, Machine Learning = 0.0365, Digital Business = 0.0186, Business Intelligence = 0.0296. Furthermore, this research indicates that the number of visitors is predicted to increase but will also experience a sharp decline in the coming days. The use of the keyword