PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN PUBG MOBILE DI GOOGLE PLAY STORE

SARI, PUTRI RATNA and Indah, Dwi Rosa (2025) PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN PUBG MOBILE DI GOOGLE PLAY STORE. Undergraduate thesis, Sriwijaya University.

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Abstract

PlayerUnknown's Battlegrounds (PUBG) Mobile merupakan salah satu game mobile paling populer di Indonesia. Di Google Play Store, game ini mendapatkan rating 3,8 dengan 49,5 juta ulasan. Meskipun banyak pengguna merasa puas, terdapat juga kritik tentang gameplay dan fitur. Namun, interpretasi sederhana terhadap ulasan tidak selalu mencerminkan kecenderungan yang akurat, sehingga diperlukan analisis sentimen yang lebih mendalam. Penelitian ini akan melakukan analisis sentimen positif dan negatif terhadap ulasan PUBG Mobile berbahasa Indonesia di Google Play Store dengan membandingkan algoritma Naïve Bayes dan Support Vector Machine (SVM). Data ulasan pengguna dikumpulkan menggunakan teknik scraping sebanyak 2.000, kemudian dilakukan proses pelabelan berdasarkan rating, preprocessing data, pembobotan TF-IDF, dan implementasi kedua algoritma. Hasil penelitian mengungkapkan apresiasi pengguna pada kualitas grafis dan gameplay yang bagus, namun terdapat masalah teknis yang perlu diperbaiki seperti bug, ketidakstabilan server, lag, dan masalah performa. Algoritma SVM memiliki kinerja lebih baik dengan akurasi 70,95%, dibandingkan Naïve Bayes yang mencapai 69,83%. Meskipun Naïve Bayes lebih cepat dalam pemrosesan, SVM unggul dalam presisi, recall, dan F1-Score.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Naïve Bayes, Perbandingan, PUBG Mobile, Support Vector Machine
Subjects:	T Technology > T Technology (General) > T58.6-58.62 Management information systems
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Putri Ratna Sari
Date Deposited:	24 Jan 2025 07:59
Last Modified:	24 Jan 2025 07:59
URI:	http://repository.unsri.ac.id/id/eprint/166890

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