Analisis Sentimen Review Publik Pengguna Game online pada Platform Steam Menggunakan Algoritma Naïve Bayes

• ADHI PANGESTU
• 2023

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Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna di platform Steam menggunakan algoritma Naïve Bayes. Platform gaming seperti Steam menjadi tempat yang populer bagi pengguna untuk berbagi pengalaman dan pendapat mereka tentang game yang dimainkan. Analisis sentimen memberikan wawasan berharga bagi pengembang game dan pihak terkait untuk memahami respons pengguna terhadap produk mereka. Penelitian ini menggunakan data ulasan game dari Kaggle, dengan fokus pada tahun 2021, yang melalui proses filtering seperti case folding, tokenizing, stopwords removal, dan stemming menggunakan RapidMiner. Data kemudian dibagi menjadi data training (1050 examples) dan data testing (494 examples) setelah proses filtering. Algoritma Naïve Bayes diterapkan pada data training untuk membentuk model klasifikasi. Hasil pengujian menunjukkan akurasi 80,97%, recall positif 78,49%, dan recall negatif 86,67%. Presisi untuk prediksi positif mencapai 93,10%, sedangkan presisi untuk prediksi negatif mencapai 63,73%. F1-Score, gabungan dari presisi dan recall, mencapai 84,68%. Dalam kesimpulannya, algoritma Naïve Bayes dapat memberikan analisis sentimen yang baik dalam mengklasifikasikan ulasan pengguna di platform Steam. Hasil akurasi, recall, presisi, dan F1-Score menunjukkan kemampuan model dalam mengidentifikasi sentimen positif dan negatif. Penelitian ini memberikan wawasan berharga bagi pengembang game dan pihak terkait dalam meningkatkan kualitas produk mereka.

• Analisis Sentimen
• Naive Bayes

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