OPTIMIZATION OF HYPERPARAMETERS FOR LSTM-BASED SENTIMENT ANALYSIS ON FACIAL SERUM DATASETS - Saputri, Merly; Hermanto, Teguh Iman; Nugroho, Imam Ma'ruf (Informatika, Universitas Jenderal Soedirman, 2024)
Air pollution and environmental pollutants directly exposed to the skin can damage the skin by accelerating premature aging, increasing the risk of acne, and causing hyperpigmentation. Skincare products such as facial serums containing vitamin C, niacinamide, and vitamin E can effectively address these issues. Awareness of the importance of using facial serums is increasing, so information about product quality through user reviews is essential before placing an order. Sentiment analysis used to classify product reviews into positive or negative, thus providing an overview of the product quality sought before placing an order. This research uses the Long Short-Term Memory (LSTM) method for the sentiment classification process. In this process, the text is converted into a number vector through feature extraction using Word2Vec. In addition, several hyperparameters such as the number of epochs, batch size, and activation function are tested to obtain optimal accuracy results. Testing the number of epochs was conducted with variations of 10, 15, and 20 to determine the performance of the resulting model as the number of epochs increased. Testing the batch size is done to evaluate the batch size in influencing the performance of the model. The batch sizes tested were 16, 32, and 64. In addition, choosing the best activation function can help the LSTM model learn more complex patterns and improve performance in sentiment analysis. The activation functions tested were Softmax, Sigmoid, and Softplus. The results of this study show that the optimal combination of the number of epochs 20, batch size 16, and Softmax activation function can provide optimal accuracy of 96.45%.