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All Journal JTT (Jurnal Teknologi Terpadu) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika ILKOM Jurnal Ilmiah Voice Of Informatics AL-ULUM: JURNAL SAINS DAN TEKNOLOGI JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) JSR : Jaringan Sistem Informasi Robotik Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Ilmu Komputer Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science
Hadi Asnal, Hadi
STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : JAIA - Journal of Artificial Intelligence and Applications

 1 
Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media M. Khairul Anam; Rahmaddeni; Muhammad Bambang Firdaus; Hadi Asnal; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 2 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (537.215 KB) | DOI: 10.33372/jaia.v1i2.794

Abstract

Vaccines are one way to prevent the coronavirus from entering the human body, although it is not 100% accurate. However, the implementation of vaccination in Indonesia is still controversial. People give their opinions directly or through social media such as Twitter. Retrieval of tweets using the Twitter API and using python. The data obtained is then preprocessed using case folding, cleaning, tokenizing, filtering, and stemming. After that, the model was evaluated using the Naive Bayes method. Naïve Bayes is a classification method that can predict the probability of a class to produce decisions based on learning data. Currently, nave Bayes is one of the methods to find accuracy in sentiment analysis that is often used and is the best. The results of this study obtained an accuracy of 79%.
Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media Bunga Nanti Pikir; M. Khairul Anam; Hadi Asnal; Rahmaddeni; Triyani Arita Fitri; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 1 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.792 KB) | DOI: 10.33372/jaia.v2i1.795

Abstract

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.
Co-Authors Agus Alamsyah Agustin Agustin Agusviyanda Agusviyanda Aldi Syahputra Anam, M Khairul Aprilita, Windi Arifandi, Fajar Bayu Yuliansyah Bunga Nanti Pikir Deni, Rahmad Didik Sudyana Edwar Ali Efrizoni, Lusiana Eiva Fatdha Elisawati, Elisawati Erlinda, Susi Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Gunadi Gunadi Hamdani Hamdani - Hartomi, Zupri Henra Helda Yenni, Helda Ivan Gunawan Jamaris, Muhamad Jamaris, Muhammad Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Lusiana Efrizoni Mardainis Mardainis Marni, Prina Mitrin, Abdullah Mohd Rinaldi Amartha Muhammad Bambang Firdaus Muhammad Febri Devara muhammad Fuad Muhammad Muadin Muhammad Nur Cahyadi Murtadho Habibi Habibi Nasrul Sani Nora Lizarti Nurhadi Oktavianda Oktavianda Pratama, Ahmad Tara Prilly Maya Gita Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rahmiati renaldi, reno Rini Yanti Rometdo Muzawi, Rometdo Supahri, Hafid Azis Susanti, Mega Susanti, Susanti Susi Erlinda T. Sy. Eiva Fatdha Tashid Torkis Nasution Triyani Arita Fitri Ulfah, Aniq Noviciate Waleska, Rangga Febrio Yansyah Saputra Wijaya Yuda Irawan Zaki Mubarok G