<![CDATA[Flip is an Indonesian financial technology application offering free interbank transfers, whose Google Play Store review column has become a large unstructured record of user experience that a development team cannot read manually. Such reviews are noisy, containing slang, repeated characters, and very short comments that behave as outliers. This study measures whether removing DBSCAN-detected outliers from the training data improves a support vector machine that assigns Flip reviews to three rating-derived categories. From 20,000 collected reviews, 15,241 remained after duplicate removal and preprocessing. The corpus was split once into stratified training and test partitions before any fitting, so that TF-IDF, the truncated singular value decomposition and DBSCAN were fitted on the training partition alone and both scenarios were evaluated on one identical held-out test set of 3,049 documents, repeated over five random seeds. Because the k-distance curve shows no pronounced knee, eps was fixed at the ninetieth percentile of that distribution, and eps and minPts were varied over a grid of nine combinations. Accuracy was 0.8023 without filtering and 0.8033 with it, a paired difference of +0.0010 with a 95 percent confidence interval of [-0.0017, +0.0038] and p = 0.347; macro F1-score moved from 0.6390 to 0.6402. No combination in the grid produced a distinguishable improvement. Of forty flagged documents inspected manually, none was genuine noise, and 91 percent of the flagged documents belonged to the majority positive class. The results give no evidence that density-based filtering benefits short-text classification.]]>
Copyrights © 2026
