<![CDATA[Local Interpretable Model-agnostic Explanations (LIME) can be used to overcome black box problems in the results of sentiment analysis classification models. This research uses reviews of online loan applications on the Play Store as a dataset. Each classification model has weaknesses and its performance can be improved by using stacking ensembles, especially to overcome the problem of imbalanced data classes. The dataset that has been obtained will be cleaned, pre-processed and converted into a numerical vector using TF-IDF. Classification is carried out using three basic models, namely random forest, naïve Bayes and support vector machine (SVM). The output of the basic classification model is used as an input for stacking ensemble logistic regression. Based on the comparison of the four models, stacking ensemble has the best performance with an accuracy of 87.05%. The application of LIME for interpreting classification models with sample data succeeded in explaining the factors that influence model decisions with a prediction probability of 95% and in accordance with manual observations. The results of this research can be used as insight and education to the public about the ease of online loan and its dangers, which are reflected in the positive and negative sentiments in a review.]]>
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