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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Dedikasi IPTEK The Journal of Engineering Techno.Com: Jurnal Teknologi Informasi TEKNOLOGI: Jurnal Ilmiah Sistem Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) 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 DoubleClick : Journal of Computer and Information Technology JAST : Jurnal Aplikasi Sains dan Teknologi KOMPUTIKA - Jurnal Sistem Komputer PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Dialektika: Jurnal Ekonomi dan Ilmu Sosial Jurnal Repositor Jurnal Riset Pendidikan MIPA ALACRITY : Journal Of Education Jurnal Perempuan & Anak Jurnal Sintesis: Penelitian Sains, Terapan dan Analisisnya Jurnal AGRISEP: Kajian Masalah Sosial Ekonomi Pertanian dan Agribisnis
Nur Hayatin
Universitas Muhammdiyah Malang
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Journal : JOIN (Jurnal Online Informatika)

 1 
Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking Hasbi Atsqalani; Nur Hayatin; Christian Sri Kusuma Aditya
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.669

Abstract

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.
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.
Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking Atsqalani, Hasbi; Hayatin, Nur; Aditya, Christian Sri Kusuma
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.669

Abstract

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.
Co-Authors Abdul Hadiy Dyo Fatra Abidatul Izzah Abidatul Izzah Izzah, Abidatul Izzah Adhi Bagus Setiawan Ahmad Al Ghivani Ahmad Dhana Renomi Ahmad Hifdhul Abror, Ahmad Hifdhul Aini Alifatin Aini Nurul Amarul Akbar Andhini, Thathit Manon Anggraini, Syadza Anisatu Thoyyibah Asep Rohman Atsqalani, Hasbi Audi Bayu Yuliawan Ayu Puji Lestari Basuki, Setio Bayu Mavindo Bayu Yuliawan, Audi Chastine Fatichah Chita Nauly Harahap Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Dasa Ismaimuza Dede Nor Alfiansyah Deny Qutara Putra Diana Purwitasari Didih Rizki Chandranegara Dini Kurniawati Doni Yulianto Dwi A. P. Rahayu Dwi Arif Al-mubarok Dyah Hestiningtyas Dzur Rifqi Aziz Eko Budi Cahyono Elbert Setiadharma Evi D. Wahyuni Evi Dwi Wahyuni Fadil Ramadhan Farid Dadhee Fatahillah Arsyad Gama Wisnu Fajarianto Giffari Zakawaly Gita Indah Marthasari Hakim, Muhammad Nafi Maula Hasbi Atsqalani Ika Rizki Anggraini Kharisma Muzaki Ghufron Kris Setyaningsih, Kris Kuntur, Soveatin Lia Nuarini M Syawaluddin Putra Jaya Maskur Maskur Maskur Maskur Mavindo, Bayu Meilina Agustina Meilisa Musnaimah Mentari Mas'ama Safitri Muhammad Rojib Saiful Musnaimah, Meilisa Musriyadi Nabiu, Musriyadi MUSTAMIN IDRIS Mustika Mentari Nasution, Annio Indah Lestari Nirindra Primavera Dirga Nugraha Nuarini, Lia Nur Putri Hidayah Nuryasin, Ilyas Prayogi Restia Saputra Putra, Deny Qutara Rahayu, Dwi A. P. Rellanti Diana Kristy Rellanti Diana Kristy Retno Firdiyanti Rima Mediana Mashita Rizal Rakhman Mustafa Rizky Ade Mahendra Rizky Heriawan Prayogo Tanjung Ruhaila Maskat S, Vinna Rahmayanti Saiful Arif, Mukhammad Rojib Sandy Young Sari Wahyunita Sari, Tiara Intana Shofiyah Soveatin Kuntur Syadza Anggraini Syukri Adisakti Dainamang Tati Susilawati, Tati Taufik Nurahman Thathit Manon Andini Tiara Intana Sari Tri Fidriyan Arya Tsabitah Ayu Rahmawati Tutik Sulistyowati Veithzal Rivai Zainal Wahyuni, Evi D. Wicaksono, Galih Wasis Wildan Suharso Yogo Suwiknyo Yuda Munarko Yufis Azhar Yuniarti, Maulidya Zalfa Natania Ardilla