Abstract
The Bali Calendar application received various responses from its users, comprising both positive accolades and negative critiques. This study employs the K-Nearest Neighbor (KNN) method to categorize sentiments into positive and negative, gauging public satisfaction with the Bali Calendar app. Additionally, the Tomek Links method is implemented to enhance the KNN classification code's performance in scrutinizing app reviews. The research distinguishes data timelines into pre and post-Covid periods due to an elevated number of negative reviews post-update during the pandemic.Pre-Covid results reveal KNN accuracy peaked at 93.7% and 94.3% without or with Tomek Links with parameter K=5 and K=3. Post-Covid, KNN accuracy reached 86.0% and 87.2% without or with Tomek Links with parameter K=9. Notably, Tomek Links yield a higher accuracy boost in post-Covid data (1.2%) compared to pre-Covid (0.6%). This underscores Tomek Links' impact on KNN accuracy amid unbalanced datasets.
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