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 tobe done. Mining user experience about the product is one of strategies to obtain many informationabout the features. Software change categories that are often used are Bug Error, FeatureRequest, and Non Informative. Previous research categorized software changes by analyzing thesimilarity of hidden topics produced by Latent Dirichlet Allocation (LDA). But the performanceof labeling is not good enough because it only considers the similarity value of some terms thatrepresent the review sentence. Therefore, this study proposes a method that considers similarityclustering value and lexical analysis of the document, and classify using Long Short TermMemory (LSTM) then. The experimental result shows the best classification for Bug Reportsoftware change categories by reaching 93.1% for accuracy, 100% for precision, and 93.1% forrecall.