Komparasi linear regression, random forest regression, dan multilayer perceptron regression untuk prediksi tren musik TikTok - Nadia Sofie Soraya; Hendry Hendry (AITI, 2023)
Predicting how audio features correlate with popular songs on TikTok is essential in the music industry. Armed with data that has several audio features, a study was conducted using the Linear Regression, Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP Regression) methods to compare models that can effectively predict popularity and features that influence song popularity on TikTok, then Exploratory Data Analysis (EDA) was also carried out to gain insight into the data. The results of the EDA process are that the most popular of songs is in the range of 40-80, the duration of songs is between 2-3 minutes, feature loudness is positively correlated with energy, and so is between artist_pop and track_pop. The set feature importance in the LR and RFR models for the feature target track_pop is artist_pop, loudness, and duration_ms. The LR method has the most effective results between RFR and MLP Regression for the dataset used,  with MSE of 0.0313, RMSE of 0.177, and MAE of 0.118.