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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) JSAI (Journal Scientific and Applied Informatics) International Journal of Informatics and Computation REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal E-Komtek Jurnal Teknologi Informatika dan Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Ilmiah Wahana Pendidikan Journal of Technology and Informatics (JoTI) Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Pelitabangsa JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah VIDHEAS: Jurnal Nasional Abdimas Multidisiplin SAINTEK International Journal of Integrated Science and Technology Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Akademika Jurnal Teknologi Pendidikan
Ahmad Turmudi Zy
UNiversitas Pelita Bangsa
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Journal : International Journal of Informatics and Computation

 1 
Sentiment Analysis on Canva Reviews Using Naive Bayes Method Faiza Muhammad Julianto; Ahmad Turmudi Zy; Elkin Rilvani
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.107

Abstract

Sentiment analysis for user review is a growing research topic in digital applications. In this study, we analyze user reviews from the Google Play Store to classify sentiments as positive or negative. The primary objective of this research is to evaluate the performance of the Naive Bayes classifier in sentiment classification. The methodology involves comprehensive data preprocessing, model training, and evaluation using performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the proposed model achieves an accuracy = 92%, precision = 85%, recall = 88%, and F1-score= 86%, respectively. These findings show the effectiveness of the proposed method that can extract valuable insights from user reviews to increase user satisfaction.
Co-Authors Abdul Ghofir Agung Nugroho Ahmad Rivaldi Anggi Muhammad Rifa'i Anggi Muhammad Rifai Antika Zahrotul Kamalia Aprilia Nur Sa'adah Ary Fahrizal Aswan S Sunge Aswan S Sunge Aswan S. Sunge Aswan Supriyadi Sunge Bagas Pamungkas Butsianto, Sufajar Danang Nurcahyo Daspar Daspar Diani Putri Kusumaningrum Donny Maulana Edora Edora Edora Edora Edy Widodo Elkin Rilvani Endrik Ermanto Faiza Muhammad Julianto Faza Ardan Kusuma Gufranaka Samudra Irfan Afriantoro Irma Nurmaulida* Isarianto Juan Fakhri Lutfi Adji Ardiansyah M.Makmun Effendi Maha Putra Miftahuljannah Mudakir Muhamad David Ali Muhamad Fatchan Muhammad Adipa Muhammad Fatchan Muhammad Makmun Effendi Muhtajuddin Danny Nadya Khairunnisa Nugroho, Agung Putri Nurhafenda Harun Qori yumansyah Qori Ray Fathur Rizky Rhendy Diki Nugraha Riani Riani Rismawati Rismawati Rosa Noviyanti Sunita Dasman SUPRAPTO Suriyanti Suriyanti Wira Syuhada Wiyanto Wiyanto Zaenur Rozikin Zaky Ali Husaeni