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All Journal Jurnal Abdimas Mahakam Teknika Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Ilmiah Raflesia Akuntansi Journal Of Informatics And Busisnes Journal of Empowerment and Community Service GEMBIRA (Pengabdian Kepada Masyarakat) Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat
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Journal : Teknika

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Analisis Sentimen Ulasan Game Stumble Guys Pada Playstore Menggunakan Algoritma Naïve Bayes Nurdy, Awang Herjunie; Rahim, Abdul; Arbansyah
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.993

Abstract

Perkembangan teknologi yang pesat mempermudah akses ke berbagai hiburan digital, termasuk game online seperti Stumble Guys, yang telah diunduh lebih dari 163 juta kali dan mendapatkan ulasan beragam di Google Play Store. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Stumble Guys menggunakan algoritma Naïve Bayes. Metode penelitian melibatkan tahapan Knowledge Discovery in Databases (KDD), meliputi pemilihan data, preprocessing, transformasi dengan CountVectorizer dan TF-IDF, serta pengklasifikasian dengan Naïve Bayes. Dengan menggunakan 1.500 ulasan dari Google Play Store, model Naïve Bayes mencapai akurasi 86%, dengan precision, recall, dan f1 score masing-masing sebesar 86%. Hasil penelitian menunjukkan bahwa Naïve Bayes efektif dalam mengklasifikasikan sentimen ulasan game Stumble Guys.
Comparative Analysis of Naïve Bayes Algorithm Performance in English and Indonesian Text Sentiment Classification on Duolingo Application in Playstore Serlina, Andi; Rahim, Abdul; Arbansyah
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1207

Abstract

Text classification is an important topic in Natural Language Processing (NLP), especially when conducting research on user reviews on language learning apps such as Duolingo. This study compares the effectiveness of the Naïve Bayes algorithm in identifying sentiment in English and Indonesian reviews on the Duolingo app on Playstore. The approach includes data collection, text preparation (case folding, tokenization, stopword removal, and stemming), and Naïve Bayes algorithm evaluation for each dataset. Model performance was evaluated using accuracy, precision, recall, and F1-score. The Naïve Bayes method obtained 84% accuracy on the English dataset with a 90:10 data split and 67% accuracy on the Indonesian dataset with the same split ratio. The difference in the results obtained is due to several variables, including the use of informal language, slang, and more complicated word variants in Indonesian, which make proper classification more difficult for the model to achieve.
Co-Authors Abdul Rahim Adhis Prasetyo Aksal Illal Al Alam Andi Nur Halim Anggiq Karisma Aji Restu Ardha, Dea Justicia Atiqah, Nur Quratul Nabila Barito, Raditya Andromeda Cholid, Idham Dio Ferdinan Fahrezi, Irqi Anbi Fendy Yulianto Fitriyati Agustina Hadi Suprapto, Yuyun Hamada Zein Heriyanto Hery Kurniawan Hery Tri Waloyo Ilham, Muhammad Fauzan Nur indriati, Menik Yuni Latipah, Johar Lufi Arif Tri Sudarma Muhammad Alviansyah Muhammad Fauzan Nur Ilham Muhammad Ghozi Alghifari Sholihuddin Muhammad Taufiq Sumadi Naufal Azmi Verdikha Nur Ilham, Muhammad Fauzan Nurdin, Andi Nurdy, Awang Herjunie Rachmat Risky Saputra Rahmatullah Ayu Hasmiati Reza Oktavian Noor Serlina, Andi Sigiet Haryo Pranoto Sofia Ulfa Eka Hadiyanti, Sofia Ulfa Eka Umar Bakri Zainuri, Zeva Ahmad