Natural Disaster Monitoring Information System From Social Media Data Using Naïve Bayes Algorithm
Abstrak
In Indonesia, there have been several natural disasters, such as earthquakes, tsunamis, landslides, floods, and others. Because Indonesia is situated where the Eurasian, Pacific, and Indo-Australian plates converge, this potential natural disaster is caused by this location. Social media information is expanding quickly and becoming more useful. Social media helps to alert people of the disaster's location during a disaster like a flood. Twitter is used as a data search engine in this work. Twitter has been utilized effectively to update the public on current events during emergencies. In order to learn more, we can conduct a search using pertinent hashtags to determining for the incident's location. The test's results will show a map of the Indonesian region, and the disaster's epicenter will be determined using the geolocation provided by the tweet data. The Naive Bayes approach will be used for classification. The clustering process occurs in real time across every region of Indonesia. In this investigation, the accuracy value was 75% based on the k-fold cross-validation test, utilizing a fold value of 3.
Keywords—Natural disasters, Twitter, Naïve Baiyes.
Referensi
Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., PerezMeana, H., Portillo-Portillo, J., Sanchez, V., & Garcia Villalba, L. (2019). Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation. Sensors, 19(7), 1746. -doi.org/10.3390/s19071746.
Wu, C. - H. (2016). SOCIAL SENSOR: AN ANALYSIS TOOL FOR SOCIAL MEDIA. International Journal of Electronic Commerce Studies, 7(1), 77 - 94. -doi.org/10.7903/ijecs.1411.
Luqyana, W. A., Cholissodin, I., & Perdana, R. S. (2018). Analisis Sentimen Cyberbullying Pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(11), -4713.
J. Li et al., "Social Media: New Perspectives to Improve Remote Sensing for Emergency Response," in Proceedings of the IEEE, vol. , no. 10, pp. 1900-1912, Oct. 2017, doi: 1109/JPROC.2017.2684460.
M. K. Delimayanti, R. Sari, M. Laya, and M. R. Faisal,
J. Domala et al.,
Processing,= in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, Jul. , pp. 503-508. doi: 10.1109/ICESC48915.2020.9156031.
Ashktorab, Z., Brown, C., Nandi, M. and Culotta, A., May.
K. L.Sumathy and M. Chidambaram, "Text Mining: Concepts, Applications, Tools and Issues An Overview", International Journal of Computer Applications, vol. 80, no. 4, pp. 29-32, 2013.
Available:10.5120/13851-1685 [Accessed 23 August 2021]
S. Dang, "Text Mining : Techniques and its Application", IJETI International Journal of Engineering & Technology Innovations, vol. ,p.222014.Available:-www.researchgate.net/publication/2730 _Text_Mining_Techniques_and_its_Application. [Accessed 23 August 2021]
Kurniawan, B., Fauzi, M., & Widodo, A. Klasifikasi Berita Twitter Menggunakan Metode Improved Na _ Ove Bayes. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. , p. 1193-1200, juli 2017. ISSN 2548-964X. Tersedia pada:
