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All Journal Jurnal Dedikasi Sainteks TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Universitas Pamulang Jurnal Repositor JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Jaring SainTek Jurnal Manajemen Informatika Jayakarta IICS Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika: Jurnal Pengembangan IT
Gita Indah Marthasari
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : JOIN (Jurnal Online Informatika)

 1 
Optimization of Sentiment Analysis for Indonesian Presidential Election using Naïve Bayes and Particle Swarm Optimization Nur Hayatin; Gita Indah Marthasari; Lia Nuarini
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.558

Abstract

Twitter can be used to analyze sentiment to get public opinion about public figures to find a trend in positive or negative responses, especially to analyze sentiments related to presidential candidates in the 2019 election in Indonesia. Naïve Bayes (NB) can be used to classify tweet feed into polarity class negative or positive, but it still has low accuracy. Therefore, this study optimizes the Naïve Bayes algorithm with Particle Swarm Optimization (NB-PSO) to classify opinions from twitter feeds to get a good accuracy of public figures sentiment analysis. PSO used to select features to find optimization values to improve the accuracy of Naïve Bayes. There are four steps to optimize NB using PSO, i.e., initializing the population (swarm), calculate the accuracy value that matched with selected features, selected the best accuracy of classification, and updating position and velocity. From this study, the group of tweets was obtained based on the positive and negative sentiments from the community towards two Indonesia presidential candidates in 2019. The NB-PSO test shows the accuracy result of 90.74%. The result of accuracy increases by 4.12% of the NB algorithm. In conclusion, the inclusion of the Particle Swarm Optimization algorithm for Naïve Bayes classification algorithm gives a significant accuracy, especially for sentiment analysis cases.
Optimization of Sentiment Analysis for Indonesian Presidential Election using Naïve Bayes and Particle Swarm Optimization Hayatin, Nur; Marthasari, Gita Indah; Nuarini, Lia
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.558

Abstract

Twitter can be used to analyze sentiment to get public opinion about public figures to find a trend in positive or negative responses, especially to analyze sentiments related to presidential candidates in the 2019 election in Indonesia. Naïve Bayes (NB) can be used to classify tweet feed into polarity class negative or positive, but it still has low accuracy. Therefore, this study optimizes the Naïve Bayes algorithm with Particle Swarm Optimization (NB-PSO) to classify opinions from twitter feeds to get a good accuracy of public figures sentiment analysis. PSO used to select features to find optimization values to improve the accuracy of Naïve Bayes. There are four steps to optimize NB using PSO, i.e., initializing the population (swarm), calculate the accuracy value that matched with selected features, selected the best accuracy of classification, and updating position and velocity. From this study, the group of tweets was obtained based on the positive and negative sentiments from the community towards two Indonesia presidential candidates in 2019. The NB-PSO test shows the accuracy result of 90.74%. The result of accuracy increases by 4.12% of the NB algorithm. In conclusion, the inclusion of the Particle Swarm Optimization algorithm for Naïve Bayes classification algorithm gives a significant accuracy, especially for sentiment analysis cases.
Co-Authors Abu Hanifah Adam Novrisal Agus Eko Minarno Akbi, Denar Regata Aldiensyah Alfin Lutfi Sidiq Amelia Deastu Amelia Dwi Deastu Aminudin AMINUDIN Aminudin, Aminudin Anastasia Lidya Maukar Andjani Chaerun Nisha Anggraini, Syadza Ani Tri Wahyuningsih Anik Vega Vitianingsih Arif Rahmadhani Basuki, Setio Belli Kafilla Gani Briansyah Setyo Wiyono Cahyanto, Mochammad Andre Chita Nauly Harahap Christian Sri Kusuma Aditya Darfian Ardiansyah Diah Risqiwati Didih Rizki Chandranegara Dwi Kurnia Puspitaningrum Effendy, Nico Ardia Eko Budi Cahyono Elsyah Ayuningrum Elza Norazizah Evi Dwi Wahyuni Faiqurrahman, Mahar Fajarisma Asfiana Putri Fajrur Rahman Suprapto Fakhrul Islami Fathoni, Muhammad Asrar Fatimah Defina Setiti Alhamdani Febri Ayu Fitriani Ferin Reviantika Frengky Prastyo Gita Ismadianti Hanafi Prasetyoko Haqim, Gilang Nuril Haris Diyaul Fata Harizal Iqmal Hasan I'anatut Thoifah Imam Halimi Indrabayu, Rizky Irsandro, Ahmad Karima Maydina Yanti Kresna Arief Nugraha Lailatul Husniah Lia Nuarini Luqman Hakim M. Isnainur Hidyatullah Mahar Faiqurahman Mairissa Anggraini Moh. Taufiq Hidayat Muhammad Alfian Ramadhani Muhammad Asrar Fathoni Muhammad Gufron Muhammad Ilham Muhammad Iqbal Muhammad Iqbal Ramadhan Muhammad Rizky Aviansyah Muhammad Ulfi Nabillah Annisa Rahmayanti Nina Mauliana Noor Fajriah Nirma Dwi Wulansari Nirma Noviasari, Vebrian Nuarini, Lia Nur Hayatin Nur Riyan Sahara Nuryasin, Ilyas Pendi, Wendi Praadita, Firman Noor Rafli, Muhammad Alvin Rellanti Diana Kristy Risdianto Risdianto Rizky Irwan Saputra Roni Hadi Wijaya S, Vinna Rahmayanti Saiful Amien Silcillya Ayu Astiti Syadza Anggraini Syaifudin Zuhri Tsabitah Ayu Rahmawati Tutik Sulistyowati Vebrian Noviasari Wahyu Andhyka Kusuma Waliyyullah Mufid Wicaksono, Galih Wasis Wildan Suharso Wiyono, Briansyah Setio Wiyono, Briansyah Setyo Yudi Ananta Prasetya Yufis Azhar Yuniarti, Maulidya Zachra, Fatimatus Zakaria, Irfan Zakiyah Mahfudho Zamah Sari Zildan Rahmatullah