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All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis
Indah Susilawati
Informatika, Fakultas Teknologi Informasi, Universitas Mercu Buana Yogyakarta

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Penerapan SMOTE pada Algoritma Naive Bayes untuk Klasifikasi Sentimen Ulasan Wuthering Waves Fabian Gavrilio Arya Winokan; Indah Susilawati
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol. 8 No. 3 (2026): Juli 2026
Publisher :  Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i3.6

Abstract

User reviews of Wuthering Waves on the Google Play Store contain important business insights. The sentiment analysis isconstrained by class imbalance and the use of gacha community slang. This condition triggers majority bias in theclassification algorithm. This research aims to build an accurate sentiment classification system to detect user complaints.The approach used is Knowledge Discovery in Databases (KDD). Pre-processing is optimized using a Gacha LexiconDictionary and Bigram extraction (TF-IDF) to resolve jargon ambiguity. The Synthetic Minority Over-sampling Technique(SMOTE) is applied to synthesize minority class data or negative reviews. The balanced data is classified using theMultinomial Naive Bayes algorithm through GridSearchCV optimization. The test results prove that SMOTE implementationsuccessfully increased the sensitivity metric (Recall) of the negative class from 0.32 to 0.82. The combination of all theseoptimizations produces a model with a final Accuracy of 86%. The integration of lexicon and SMOTE is proven to overcomedata bias. This classification model has been implemented into an interactive analytic dashboard prototype.
Co-Authors Fabian Gavrilio Arya Winokan