Klasifikasi Perubahan Perangkat Lunak pada Mobile App Review dengan Menggunakan Metode Long Short Term Memory (LSTM) - Puspaningrum, Alifia; Sari Bunga, Munengsih; Iryanto, Iryanto (IKRA-ITH Informatika : Jurnal Komputer dan Informatika, 2020)
Along with the development of mobile applications, software evolution is an essential step to be done. Mining user experience about the product is one of strategies to obtain many information about the features. Software change categories that are often used are Bug Error, Feature Request, and Non Informative. Previous research categorized software changes by analyzing the similarity of hidden topics produced by Latent Dirichlet Allocation (LDA). But the performance of labeling is not good enough because it only considers the similarity value of some terms that represent the review sentence. Therefore, this study proposes a method that considers similarity clustering value and lexical analysis of the document, and classify using Long Short Term Memory (LSTM) then. The experimental result shows the best classification for Bug Report software change categories by reaching 93.1% for accuracy, 100% for precision, and 93.1% for recall.