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All Journal Semantik JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik Jurnal Teknologi Komputer dan Sistem Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Bantenese Jurnal Pengabdian Masyarakat Building of Informatics, Technology and Science TEPIAN Jurnal Sistem Informasi dan Sistem Komputer JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) J-Intech (Journal of Information and Technology) DiJITAC : Digital Journal of Information Technology and Communication Informatika Wicida Ensiklopedia Research and Community Service Review Journal of Artificial Intelligence and Digital Business JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Amelia Yusnita
STMIK Widya Cipta Dharma
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Journal : J-Intech (Journal of Information and Technology)

 1 
Application of Naive Bayes Algorithm for Analysis of User Reviews on Mobile Legends Game: Bang Bang Roba, Al-Muchlis Syachrul Ramadani; Lailiyah, Siti; Yusnita, Amelia
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1881

Abstract

Mobile Legends: Bang Bang is a highly popular MOBA game, especially among students, which generates a large volume of user reviews on the Google Play Store. These reviews provide a valuable data source for understanding user sentiment. This study conducts sentiment analysis on user reviews using three variants of the Naïve Bayes algorithm: BernoulliNB, GaussianNB, and MultinomialNB. From an initial 5,000 reviews collected via web scraping using Python, 4,428 reviews were used after neutral reviews were removed to focus solely on positive and negative sentiments.The preprocessing steps included case folding, word normalization, tokenization, stopword removal, and stemming. Sentiment labeling was carried out using a lexicon-based approach, comparing the frequency of positive and negative words in each review. The dataset was split in an 80:20 ratio for training and testing.The results show that MultinomialNB achieved the highest accuracy at 75%, followed by BernoulliNB with 74%, and GaussianNB with 50%. MultinomialNB demonstrated superior performance in detecting positive sentiments, while BernoulliNB offered more balanced results. GaussianNB performed poorly due to its assumption of normally distributed continuous data, which is unsuitable for text classification. This study concludes that Multinomial Naïve Bayes is the most effective model for sentiment analysis of user reviews when working with word frequency-based representations.
Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews Raya, Dimas; Yusnita, Amelia; Haristyawan, Ivan
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1882

Abstract

The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.
Co-Authors Adrian, Nurul Ahmad Rofiq Hakim Alenya, Nia Andi Yushika Rangan Andi Yusika Rangan Astuty Nilasari Azahari, Azahari Basrie Dewi Rosita Ekawati Yulsilviana Ekawati, Hanifah Elys Diawati Emon Azriadi Eri Wandana Fadri, Roy Febryanto, Febryanto Fery Setiawan Harianto, Kusno Haristyawan, Ivan Hariyadi Hariyadi Heny Pratiwi I Gede Adi Merta Khoiri Saumahudi Kusnandar Kusnandar Kusnandar Lukman Hariri M. Irwan Ukkas Irwan Ukkas Ukkas Mayasari, Reny Muhammad Awaludin Muhammad Ridhuan. S Nila Ratna Wati Pahrudin, Pajar Pajar Pahruddin Patrianto, Wahyu Paul, Debora Putri Wulandari Raya, Dimas Roba, Al-Muchlis Syachrul Ramadani Rosiana Handini Salmon Salmon Siti Lailiyah Siti Lailiyah, Siti Siti Qomariah Siti Qomariah Susanti, Agustina Nona Tabrani Rija’i Theana Dwi Aprillita Vilianty Rafida Yulindawati, Yulindawati Yulindawati, Yulindawati