Analisis Sentimen Ulasan Shopee di Google Play dengan TF-IDF dan Logistic Regression

Shaifany Fatriana Kadir; Ahmad Fairuzabadi

doi:10.31004/riggs.v4i2.2850

Authors

Shaifany Fatriana Kadir Universitas Widya Gama
Ahmad Fairuzabadi Universitas Widya Gama

DOI:

https://doi.org/10.31004/riggs.v4i2.2850

Keywords:

Analisis Sentimen, Shopee, Google Play, TF-IDF, Logistic Regression

Abstract

Pertumbuhan aplikasi belanja daring mendorong meningkatnya jumlah ulasan pengguna yang tersedia di Google Play Store. Ulasan ini tidak hanya mencerminkan pengalaman pengguna, tetapi juga berperan sebagai masukan penting bagi pengembang aplikasi. Penelitian ini menganalisis sentimen ulasan aplikasi Shopee Indonesia (package com.shopee.id) periode 2024–2025 dengan menggunakan pendekatan Term Frequency–Inverse Document Frequency (TF-IDF) dan algoritma Logistic Regression. Data sebanyak 5.000 ulasan dikumpulkan melalui pustaka google-play-scraper dan dilabeli otomatis berdasarkan skor rating: 1–2 negatif, 3 netral, dan 4–5 positif. Proses preprocessing meliputi normalisasi teks, penghapusan stopword bahasa Indonesia, serta stemming. Distribusi data yang timpang (4049 positif, 796 negatif, 155 netral) ditangani dengan Random Oversampling pada data latih. Evaluasi dilakukan menggunakan stratified split 80:20 dan 5-fold cross-validation. Hasil menunjukkan Logistic Regression memberikan performa terbaik dengan akurasi 85,11% dan macro-F1 0,58 pada data uji, lebih baik dibandingkan SVM dan Naive Bayes. Confusion matrix memperlihatkan bahwa kelas positif dapat dikenali dengan baik (F1=0,92), sementara kelas netral sulit diprediksi (F1=0,14). Visualisasi WordCloud digunakan untuk menggambarkan kata dominan pada tiap kelas. Studi ini menegaskan efektivitas Logistic Regression untuk analisis ulasan aplikasi berbahasa Indonesia, meskipun tantangan besar masih terdapat pada ketidakseimbangan kelas minoritas.

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