Analisis Sentimen Ulasan Pengguna Aplikasi M-Pajak di Google Play Store Menggunakan Algoritma Naïve Bayes
Abstract
Digital-based public services require service quality evaluation to ensure user satisfaction, one way of doing this is through sentiment analysis of M-tax app reviews on the Google Play Store. The aim of this study is to test the tendency of user sentiment toward the M-Tax application by applying the naïve bayes algorithm. The dataset utilized in this research consists of 5,263 user reviews, which were processed through several preprocessing steps, including case folding, text cleaning, tokenization, stopword removal, and stemming. The analyzed variable is user review text classified into positive and negative sentiment categories. The naïve bayes algorithm was employed to perform sentiment classification. To assess the model performance, a confusion matrix was employed with an 80:20 split between train and testt data, along with k-fold cross-valiidation using k = 5. The findings show that negative sentiment dominates the reviews at 88.34%, while positive sentiment accounts for 11.66%. The model attained an accuracy of 92%, indicating that the Naïve Bayes algorithm performs efecctively in classifying user sentiment and can serve as a basis for evaluating improvements in digital taxation services.
Keywords: Sentiment analysis; Naive Bayes; Application reviews; TF-IDF; M-Pajak
Abstrak
Pelayanan publik berbasis digital menuntut adanya evaluasi kualitas layanan untuk memastikan kepuasan pengguna, salah satunya melalui analis sentiment ulasan aplikasi M-Pajak di Google Play Store. Adapun tujuan dari penelitian ini yaitu untuk mengkaji kecenderungan sentimen pada ulasan pengguna aplikasi M-Pajak di Google Play Store dengan menerapkan algoritma Naïve Bayes. Data yang dianalisis berjumlah 5.263 ulasan pengguna, yang diproses dengan tahapan prerocessing yaitu case folding, cleaning text, tokeniation, stopword, dan stemming. Variabel yang dianalisis berupa teks ulasan pengguna dengan kelas sentimen positif dan negatif. Klasifikasi dilakukan dengan menggunakan algoritma Naïve Bayes. Untuk menilai performa model, digunakan confusiont matrix sebagai alat evaluasi dengan pembagian data latih dan data sebesar 80:20 serta teknik k-fold cross-validation dengan nilai k = 5. Penelitian ini menunjukkan hasil bahwa sentimen negatif mendominasi sebesar 88,34%, sedangkan sentimen positif sebesar 11,66%. Model menghasilkan akurasi evaluasi sebesar 92%, sehingga algoritma Naïve Bayes dinilai tepat dan efisien untuk menganalisis sentimen pada ulasan aplikasi M-Pajak. dan dapat digunakan sebagai dasar evaluasi peningkatan layanan digital perpajakan.
Keywords
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