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All Journal Jurnas Nasional Teknologi dan Sistem Informasi INOVTEK Polbeng - Seri Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics MEANS (Media Informasi Analisa dan Sistem) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Community Empowerment Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Kharisma, Ivana Lucia
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Public Health

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Penerapan Algoritma Random Forest untuk Menganalisis Ulasan Aplikasi Spotify pada Google Play Insany, Gina Purnama; Kharisma, Ivana Lucia; Rosmawati, Rosmawati
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.26394

Abstract

The development of internet technology and mobile devices has played an important role in transforming the music industry and driving the emergence of music streaming services, such as Spotify. This research aims to understand Spotify users' preferences and expectations through sentiment analysis of user reviews. The review data was retrieved from Google Play Store using web scraping technique, including rivew and rating of 1000 Indonesian and 1000 English reviews. Random Forest algorithm was used to model the sentiment of the reviews, with the analysis process including data collection, labeling, preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, oversampling, modeling, and evaluation with confusion matrix. Algorithm testing was conducted using 70% training data and 30% test data. The classification evaluation results of the Random Forest algorithm showed a model accuracy of 88.4% for Indonesian reviews and 93.6% for English reviews. These findings show that the Random Forest algorithm is effective for sentiment analysis in a multilingual context and can help app developers improve service quality based on user sentiment. This research was also deployed using Streamlit, enabling access and usage by users for fast and interactive sentiment analysis.
Co-Authors Agung Gumelar Al-Basori, Muhamad Cepnur Alhidamkara, Salman Alun Sujjada Angelina, Nadila Apriliyanti, Silvi Ayu Lestari, Melani Bila, Nida Aulia Salsa Dewi, Dhita Diana Fatalifi, Amerjid Ghulamson Fergina, Anggun Firdaus, Asep Rizki Gunawan, Muhamad Sahrul Hafidzh Al Muhiddin, Muhammad Ibnu Sina, Rivaldi Insany, Gina Purnama Kamdan Khairiyah Ivanka, Najwa Kustandi, Agus Mamun, Yuliana Maulana Yusuf Muhammad Dzulqarnain, Fajar Munandar, Rifki Arief Natanael, Brillian Aji Natasya, Sandra Pauzi, Yoviar Perkasa, M. Juang Pajri Prayoga , Rizki Haddi Purnama Insany, Gina Ramdan, Hari Muhammad Rohmat, Satria Rizki Rosmawati Rosmawati Sebayang, Alvin Christoper Septiani, Dhea Ayu Serliani, Siska Meyisa Amelia Somantri, S Somantri, Somantri Sukmawan, Dede Sulaeman, Asep Surahman Tresna Ati, Mutiara Annisa Yasin, Fakhriyal Riyandi Yustiana, Indra Zahra, Falya Amrina Zaki Hibatullah, Muhammad