TWITTER SENTIMENT ANALYSIS OF POST NATURAL DISASTERS USING COMPARATIVE CLASSIFICATION ALGORITHM SUPPORT VECTOR MACHINE AND NAÏVE BAYES

Ainun Zumarniansyah; Rangga Pebrianto; Normah Normah; Windu Gata

doi:10.33480/pilar.v16i2.1423

Ainun Zumarniansyah ^(1*) Ilmu Komputer, STMIK Nusa Mandiri
Rangga Pebrianto ⁽²⁾ Ilmu Komputer, STMIK Nusa Mandiri
Normah Normah ⁽³⁾ Teknik Informatika, STMIK Nusa Mandiri
Windu Gata ⁽⁴⁾ Ilmu Komputer, STMIK Nusa Mandiri

(*) Corresponding Author

DOI: https://doi.org/10.33480/pilar.v16i2.1423

Keywords: Naive Bayes, Natural Disasters, Support Vector Machine, Twitter

Abstract

Natural disasters trigger people, especially Twitter users to provide information or opinions in the form of tweets. The Tweet can be an expression of sadness, concern, or complaint. Processing of data from these tweets will create trends that can be used for information needs such as education, economics, and others. Natural disasters are events that threaten human life caused by nature, including in the form of earthquakes. The method used is the Support Vector Machine and Naive Bayes from the tweet. The data collected is filtered from tweets by deleting duplicate data. In calculating the Natural Disaster sentiment analysis using a comparison of the Support Vector Machine and the Naive Bayes algorithm, the difference in accuracy is 3.07% where the results of the Support Vector Machine are greater than Naive Bayes. The purpose of this research is to analyze sentiment for the distribution of disaster aid that does not flow information due to information & coordination in the field. so as to provide information on the location of natural disasters, natural disaster management, and its presentation to victims that can be shared evenly in an efficient time due to information and natural management so that the distribution of aid is hampered

Downloads

Download data is not yet available.

Author Biographies

Rangga Pebrianto, Ilmu Komputer, STMIK Nusa Mandiri

Ilmu Komputer, STMIK Nusa Mandiri

Normah Normah, Teknik Informatika, STMIK Nusa Mandiri

Teknik Informatika, STMIK Nusa Mandiri

Windu Gata, Ilmu Komputer, STMIK Nusa Mandiri

Ilmu Komputer, STMIK Nusa Mandiri

References

Amalia, R., Bijaksana, M. A., & Darmantoro, D. (n.d.). A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter. https://doi.org/10.1088/1742-6596/755/1/011001

Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. 2(1), 32–41.

D, I. A. P. (n.d.). Text Mining dan Knowledge Discovery. 1–9.

Erni Ernawati. (2019). Ermawati, Algoritma Klasifikasi C4.5 Berbasis Particle Swarm Optimization Untuk Prediksi Penerima Bantuan Pangan Non Tunai 513. 8(September), 513–528.

Gallardo, R. (2020). Bringing Communities into the Digital Age. 51(4), 233–241. https://doi.org/10.1177/0160323X20926696

Kristiyanti Dinar Ajeng, normah, H. A. (2019). Prediction of Indonesia Presidential Election Results for the 2019-2024 Period Using Twitter Sentiment Analysis. 36–42.

Kurikulum, K. K., Pamungkas, D. S., Setiyanto, N. A., & Dolphina, E. (2015). ANALISIS SENTIMENT PADA SOSIAL MEDIA TWITTER MENGGUNAKAN NAIVE BAYES CLASSIFIER TERHADAP. 14(4), 299–314.

Luqyana, W. A., Cholissodin, I., & Perdana, R. S. (2018). Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine. 2(11), 4704–4713.

Nurajizah, S. (2015). Sistem Informasi Perpustakaan Berbasis Web Dengan Metode Prototype: Studi Kasus Sekolah Islam Gema Nurani Bekasi. Prosiding SNIT 2015, 214–219.

Nurhuda, F., & Sihwi, S. W. (2014). Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier. 2(2).

Prasetyowati, E. (2017). DATA MINING (Moh.Afandi, ed.). Duta Media.

Ramadhan, M. I. (n.d.). PENERAPAN DATA MINING UNTUK ANALISIS DATA BENCANA MILIK BNPB MENGGUNAKAN ALGORITMA. 22(1).

Rofiqoh, U., Perdana, R. S., & Fauzi, M. A. (2017). Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features. 1(12), 1725–1732.

Rokhman Fathur, S. (2020). LINGUISTIK DISRUPTIF : Pendekatan Kekinian Memahami Perkembangan Bahasa (1st ed.; F. Azzahrah, ed.). Jakarta: PT. Bumi Aksara.

Saputra, N., Bharata, T., & Erna, A. (2015). Jurnal Dinamika Informatika Volume 5, Nomor 1, November 2015. 5(November).

Saputra, S. A., Rosiyadi, D., Gata, W., & Husain, S. M. (2019). Sentiment Analysis Analisis Sentimen E-Wallet Pada Google Play Menggunakan Algoritma Naive Bayes Berbasis Particle Swarm Optimization. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 377–382. https://doi.org/10.29207/resti.v3i3.1118

Son, Y., Kim, H., Kim, E., Choi, S., & Candidate, D. (2010). Application of Support Vector Machine for Predic- tion of Medication Adherence in Heart Failure Pa- tients. 16(4), 253–259. https://doi.org/10.4258/hir.2010.16.4.253