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All Journal Jurnal Informatika Universitas Pamulang MIND (Multimedia Artificial Intelligent Networking Database) Journal Jambura Journal of Informatics Building of Informatics, Technology and Science Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jumat Informatika: Jurnal Pengabdian Masyarakat Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Informatika: Jurnal Pengembangan IT
Sukmawati, Cici Emilia
Universitas Buana Perjuangan Karawang
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Other

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Analisa Perbandingan Algoritma Support Vector Machine dan K-Nearest Neighbors Terhadap Ulasan Aplikasi Vidio Gumilar, Rizki Bintang; Cahyana, Yana; Sukmawati, Cici Emilia; Siregar, Amril Mutoi
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5640

Abstract

Internet usage in Indonesia reached 77% of the total population in January 2023, with Over The Top (OTT) services showing user growth of 25% every year. The Vidio application, one of the popular OTT platforms with downloads exceeding 50 million, has a 3.5 star rating based on 649 thousand reviews on the Google Play Store. Despite its popularity, Vidio faces complaints regarding limited film selection, payment errors, and excessive advertising, which affects user satisfaction. This research aims to analyze the opinions of Vidio application user comments by applying the SVM (Support Vector Machine) method and the KNN (K-Nearest Neighbors) method to determine the model with the best accuracy. 15,000 review data were collected through scraping, then processed using text preprocessing and TF-IDF vectorization techniques. Model evaluation shows that SVM has an accuracy value of 82%, a precision value of 82%, a recall value of 83%, and an F1-score value of 82%, while KNN has an accuracy of 69%, precision 74%, recall 73%, and F1-score 69% . The research results show that SVM is superior to KNN in classifying the sentiment of Vidio application reviews. It is hoped that these findings can be used by application developers in an effort to improve service and satisfaction of Vidio application users.
Analisis Sentimen Rencana Penerapan Cukai Pada Minuman Manis Kemasan Menggunakan Algoritma Naive Bayes dan Logistic Regression Gozali, Gozali; Baihaqi, Kiki Ahmad; Sukmawati, Cici Emilia; Wahiddin, Deden
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7411

Abstract

The plan to impose excise tax on packaged sweetened beverages (PSB) is proposed as a strategic measure to reduce sugar consumption among the public. This policy has elicited various responses from society, especially on social media platforms such as TikTok. The purpose of this study is to evaluate public sentiment towards the PSB excise tax policy by analyzing comments posted on the TikTok platform, comparing the performance of the Naive Bayes and Logistic Regression algorithms. Data were collected from comments on news videos about the implementation of the excise tax on PSB posted by official journalist accounts on TikTok, using the TikTok Comments Scraper available on the apipy website, resulting in 1,332 comments. The data were processed through preprocessing steps including text cleaning, tokenization, stemming, and word weighting using TF-IDF. After expert sentiment labeling, the data were then split into training and testing sets with an 80:20 ratio. Evaluation was conducted using a confusion matrix to obtain performance metrics such as accuracy, precision, recall, and F1-score for each model. The analysis revealed that negative comments dominated at 65.2%, while positive comments accounted for 34.8%. The Logistic Regression algorithm achieved an accuracy of 81.37%, precision of 86.22%, recall of 75.14%, and an F1-score of 77.06%. Meanwhile, the Naive Bayes algorithm obtained an accuracy of 79.85%, precision of 82.19%, recall of 74.17%, and an F1-score of 75.76%. It can be concluded that the majority of TikTok users still express negative responses to the PSB excise tax policy, and the Logistic Regression algorithm demonstrates superior performance in sentiment classification compared to the Naive Bayes algorithm.
Co-Authors Abigael, Rakha Amril Mutoi Siregar ARIF, SITI NOVIANTI NURAINI Baihaqi, Kiki Ahmad Bayu Prasetyo, Bayu Damaiarta Tejayanda, Rigger Faisal, Muhamad Agus Fitri Nur Masruriyah, Anis Gozali, Gozali Gumilar, Rizki Bintang Hanny Hikmayanti Handayani Iin Kurniawati Juwita, Ayu Ratna Khairani, Nova Pustita Mudzakir, Tohirin Al Novalia, Elfina Novita, Hilda Yulia Nurmayanti, Trisya nurul latifah Pratama, Adi Rizky RAMADHAN, ANGGA RAMDA Rohana, Tatang Saputra, Arbi Niandi Siregar, Amril Mutoi Tejayanda, Rigger Damaiarta Wahiddin, Deden Yana Cahyana