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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) ILKOM Jurnal Ilmiah Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Journal of Information Systems and Technology Research Jurnal Sains Informatika Terapan (JSIT) Indonesian Journal of Informatic Research and Software Engineering Innovative: Journal Of Social Science Research Journal Of Indonesian Social Society The Indonesian Journal of Computer Science
Pratiwi, Mutiana
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Journal : The Indonesian Journal of Computer Science

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Twitter Sentiment Analysis of Public Space Opinions using SVM and TF-IDF Methods Arsyah, Ulya Ilhami; Pratiwi, Mutiana; Muhammad, Abulwafa
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3594

Abstract

Public space opinion reviews are currently a source of information for interested parties and decision-makers. Twitter is a social media that is a means of expressing themselves for people to express their opinions and criticize the current situation. This becomes information for readers. Information published on Twitter contains elements of commentary on a situation or object Sentiment analysis of public space opinion on Twitter using Machine Learning with the Support Vector Machine (SVM) method with the data weighting process using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Dataset obtained by scraping using the Twitter API as much as 5000 data then labeled where the goal is to get accuracy on positive, negative, or neutral sentiment. The results of research conducted experiments on three Machine Learning algorithms with the extraction function "TF-IDF" obtained an accurate training model with good classification capabilities, especially SVM of 91,6% on data distribution 70: 30; SVM is 92.8% in the case of data distribution of 80: 20; the SVM is 91,8% in the case of 90:10 decomposition data.
Machine Learning on Opinion Mining of Netizen's Hate Speech Pratiwi, Mutiana; Liana Gema, Rima
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3617

Abstract

Netizen comments written in an online news portal through social media platforms, one of which is Instagram, can be used as material in the sentiment analysis process, which can be classified into positive, negative, or neutral sentiments. Sentiment analysis is part of the study of text mining, the science of discovering unknown knowledge by automatically extracting information from large volumes of unstructured text into useful information. The resulting information is in the form of sentiment towards a topic, whether it tends to be positive, negative, or neutral. The classification method used in this research is Support Vector Machine (SVM) and TF-IDF data weighting to classify text. Stages to perform data analysis are pre-processing to clean data, word weighting, labeling data into positive, negative, or neutral classes, and classifying and visualizing data with graphs. Accuracy tests using 70:30 split data showed that the accuracy reached 98%. Tests with 80:20 and 90:10 split data also showed high accuracy of 98% and 99%.
Co-Authors Abulwafa Muhammad Aggy Pramana Gusman Aminuyati Anam, M Khairul Arsyah, Rahmatul Husna Arsyah, Ulya Ilhami Devia Kartika Dinul Akhiyar Fryonanda, Harfebi Gema, Rima Liana Gushelmi Haidir, Ahmad Irzal Arief Wisky Jihan Sy, Yulia Kris Hiburan Laoli, Vince kurnia, rahmi putri Liana Gema, Rima Liga Mayola Lusinia, Shary Armonitha Mardhiah, Putri Marfalino, Hari Muhammad, Abulwafa Munawir Munawir Nitami, Dian Rianti, Eva safira, silky Sarjon Defit Surmayanti, Surmayanti Tukino, Tukino Yori Adi Atma