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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan MULTINETICS Jurnal Puan Indonesia Internet of Things and Artificial Intelligence Journal Journal of Informatics and Electronics Engineering Journal of Economics, Accounting, Tax and Management Journal Of Artificial Intelligence And Software Engineering Scientific Journal of Informatics
Tresnawati, Shandy
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : Journal Of Artificial Intelligence And Software Engineering

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Sentiment Analysis of Quizizz Application User Reviews Using Logistic Regression Algorithm Aditya, Ari; Tresnawati, Shandy
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7292

Abstract

Digital learning applications like Quizizz are increasingly popular for offering interactive learning experiences. As user numbers grow, so do the reviews on platforms like Google Play Store, reflecting user perceptions of app quality. This study aims to analyze user review sentiment toward the Quizizz application using the Logistic Regression algorithm. The data consists of Indonesian-language reviews collected from March to December 2024. The analysis process includes text preprocessing using the Sastrawi library, lexicon-based sentiment labeling, TF-IDF weighting, and classification using Logistic Regression. The model is evaluated using accuracy, precision, recall, and f1-score. The results show that most reviews are positive, and the model performs well in sentiment classification. These findings offer insights for developers to improve the app’s quality and user experience.
Sentiment Analysis Of Reading Difficulties In Grade 7 Secondary School Students Using The Support Vector Machine (SVM) Algorithm Algipari, Rasyid Zanuar; Tresnawati, Shandy
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7289

Abstract

The reading ability of junior high school students in Indonesia is still relatively low, as evidenced by the results of the National Assessment and the viral case of 29 grade VII junior high school students in Pangandaran who are not yet fluent in reading. This study aims to analyze public perception of this phenomenon through a sentiment analysis approach based on YouTube comment text and using the Support Vector Machine (SVM) algorithm. The steps of the SEMMA method are applied, starting with collecting comment data, preprocessing the text using dictionary-based methods and TF-IDF, and finally classification using SVM. The dataset used includes 1,055 comments. The results of the study show that the SVM algorithm is able to classify sentiment into three categories (positive, negative, and neutral) with an accuracy of 87%. These results indicate that the majority of people care about the quality of basic education. This study contributes to the computational understanding of public perception and can be used as a reference for data-based literacy guidelines.
Co-Authors Aditya, Ari Alfianti, Henny Algipari, Rasyid Zanuar Fauzi, Findri Ihsan Kamil, Ali Ihsan, Adrian Nur Iskandar, Rachmat Junia Rizqi, Kevin Latifah, Indah Nia Ekawati, Nia Novita Lestari Anggreini Pratama, Alfian Raafidiani, Reni Rohmayani, Dini Siddiq, Maulana Fadjar Sugianto, Castaka Agus Syafwan, Elvathna Syafwan, Mahdhivan Syams, Fawwaz Muhammad Syani, Mamay Tati Ernawati Tiara, Cica Wulandari, Ratna Rizky