English - Mika Parwita, I Made; Siahaan, Daniel (Lontar Komputer : Jurnal Ilmiah Teknologi Informasi; Vol. 10, No. 1 April 2019DO - 10.24843/LKJITI.2019.v10.i01.p01, 2019)
The app reviews are useful for app developers because they contain valuable information, e.g. bug, feature request, user experience, and rating. This information can be used to better understand user needs and application defects during software maintenance and evolution phase. The increasing number of reviews causes problems in analysis process for developers. Reviews in textual form are difficult to understand, time-consuming, requires a lot of effort, and costly for manual analysis. Previous research shows that collection of review contains non-informative reviews because they do not have valuable information. Non-informative reviews considered as a noise and should be eliminated especially for classification process. The purpose of this research is to classify user reviews into three classes, i.e. bug, feature request, and non-informative reviews automatically. User reviews are converted into vector using word embedding. The vectors are used as input into first classifier that classify informative and non-informative reviews. The results from first classifier, that is informative reviews, then reclassified using second classifier to determine its category, e.g. bug report or feature request. The experiment using 306,849 sentences of reviews crawled from Google Play and F-Droid. The evaluation process using the following metrics: accuracy, recall, precision, and F-measure. The results show that the proposed model is able to handle large scale of imbalanced data and produces best accuracy of 0.79, precision of 0.77, recall of 0.87, and F-Measure of 0.81.