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

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

• By Zumarniansyah, Ainun; Pebrianto, Rangga; Normah; Gata, Windu;
• Jurnal Nasional Terakreditasi
• 285
• 169 - 174

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

• Katakunci Naive Bayes, Natural Disasters, Support Vector Machine, Twitter.
• URL https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1423
• Penerbit Jurnal Pilar Nusa Mandiri, Jakarta DKI Jakarta

REFERENSI

Aaputra, S. A., Didi Rosiyadi, Windu Gata, & Syepry

Maulana Husain. (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

Adiwijaya, I. (2006). Text Mining dan Knowledge

Discovery. http://web.ipb.ac.id/~ir-

lab/pdf/tm (text summarization).pdf

Ajeng, K. D., Normah, N., & Ahmad, H. (2019).

Prediction of Indonesia Presidential Election

Results for the 2019-2024 Period Using

Twitter Sentiment Analysis. 2019 5th

International Conference on New Media

Studies (CONMEDIA), 36–42.

https://doi.org/10.1109/CONMEDIA46929.2

019.8981823

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.

INTEGER: Journal of Information Technology,

2(1), 32–41.

https://ejurnal.itats.ac.id/integer/article/vie

w/95

Erni Ernawati. (2019). Ermawati, Algoritma

Klasifikasi C4.5 Berbasis Particle Swarm

Optimization Untuk Prediksi Penerima

Bantuan Pangan Non Tunai. Sistemasi: Jurnal

Sistem Informasi, 8(3), 513–528.

http://sistemasi.ftik.unisi.ac.id/index.php/st

msi/article/view/576

Gallardo, R. (2020). Bringing Communities into the

Digital Age. State and Local Government

Review (SLGR), 51(4), 233–241.

https://doi.org/10.1177/0160323X2092669

6

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.

Nurhuda, F., Sihwi, S. W., & Doewes, A. (2013).

Analisis Sentimen Masyarakat terhadap Calon

Presiden Indonesia 2014 berdasarkan Opini

dari Twitter Menggunakan Metode Naive

Bayes Classifier. ITSMART: Jurnal Teknologi

Dan Informasi, 2(2), 35–42.

https://jurnal.uns.ac.id/itsmart/article/view

/630

Prasetyowati, E. (2017). DATA MINING

(Moh.Afandi (ed.)). Duta Media.

Ramadhan, M. I., & Prihandoko, P. (2017).

PENERAPAN DATA MINING UNTUK

ANALISIS DATA BENCANA MILIK BNPB

MENGGUNAKAN ALGORITMA K-MEANS DAN

LINEAR REGRESSION. JURNAL ILMIAH

INFORMATIKA KOMPUTER, 22(1), 57–65.

https://ejournal.gunadarma.ac.id/index.php/

infokom/article/view/1535

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 (F. Azzahrah (ed.); 1st

ed.). PT. Bumi Aksara.

Saputra, N., Bharata, T., & Erna, A. (2015). Jurnal

Dinamika Informatika Volume 5, Nomor 1,

November 2015. 5(November).

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

Zumarniansyah, A., Febrianto, R., Normah, N., &

Gata, W. (2020). Laporan Akhir Penelitian

Mandiri: Twitter Sentiment Analysis Of Post

Natural Disasters Using Comparative

Classification Algorithm Support Vector

Machine And Naïve Bayes.