Sentiment Analysis of Land Partner Applications Using the Naive Bayes Classifier and K-Nearest Neighbor Algorithms
https://doi.org/10.58466/aicoms.v3i1.1542
Abstract
The transportation industry is now an important element as the times develop, especially for today's young generation. Mitra Darat itself is also one of these industries. An application that allows users to easily find out the bus departure schedule that they will take anywhere and anytime on their mobile device. Reviews are definitely given for every app available both positive and negative. With this, we are trying to conduct sentiment analysis research for the Mitra Darat application through reviewing comments from the Google Play Store so that we can identify sentiments related to the use of the Mitra Darat application, as well as provide valuable insights to land transportation service providers to understand user views and improve user services. from the results of our sentiment analysis. The algorithms we use are KNN and NBC. These two algorithms are commonly used by many people because of their expertise in classifying sentiment analysis data and are also popular among researchers. Based on our test results, it can be concluded that our sentiment analysis model designed using the NB algorithm displays higher accuracy performance than KNN. The accuracy of the NB model reached 99.28%, while KNN achieved an accuracy of 80%. This shows that the naïve Bayes algorithm is more suitable to obtain maximum accuracy compared to using k-nearest neighbors.
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