Analisis Ulasan Indie Video Game Lokal pada Steam Menggunakan Analisis Sentimen dan Pemodelan Topik Berbasis Latent Dirichlet Allocation

Mochamad Yudha Febrianta, Sri Widiyanesti, Syahrul Robbiansyah Ramadhan

Abstract


Video game merupakan produk Ekonomi Kreatif yang berkembang pesat dan mempengaruhi perekonomian dunia. Di antara para pelakunya, pengembang indie video game memiliki keterbatasan sumber daya dan bergantung pada jasa distribusi digital untuk menjual produknya. Steam merupakan platform distribusi digital video game dengan fitur ulasan yang dapat dijadikan acuan pengembangan video game. Ulasan produk online cenderung berjumlah banyak dan beragam sehingga menimbulkan tantangan bagi para pengembang indie video game untuk menganalisisnya. Penelitian ini bertujuan untuk melakukan analisis berbasis machine learning terhadap ulasan produk indie video game di Steam secara otomatis. Metode yang digunakan adalah analisis sentimen dengan algoritma klasifikasi Naïve Bayes dan pemodelan topik berbasis LDA menggunakan perangkat lunak Rapidminer dan RStudio. Hasil penelitian menunjukkan sentimen positif dominan sebesar 69.8% dengan akurasi algoritma 75.45%. Penelitian ini juga menunjukkan bahwa “story”, “character”, “music”, dan “art” termasuk istilah yang sering muncul di antara topik-topik dominan.


Keywords


video game; analisis sentimen; model topik; machine learning; ulasan produk

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DOI: https://doi.org/10.24821/jags.v7i2.5162

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