Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes

Main Article Content

Muhammad Ikbal
Septi Andryana
Ratih Titi Komala Sari

Abstract

The covid-19 virus became a pandemic in 2020. The spread of covid cases has hit the whole world, reaching 63 million cases in 190 countries as of November 2020. Information regarding the spread of covid is necessary for the general public. This research will produce a system that can provide information on the geographic distribution of covid cases. The data on the distribution of covid cases in this study were also used to analyze the classification using the Naive Bayes Classifier method. The Naive Bayes Classifier method works by using probability calculations so that this research can be used to classify the covid status in an area. The results of this study have succeeded in providing information on the status of the covid pandemic based on data on covid cases that have occurred around the world. Covid case data becomes training data for the analysis of the Naive Bayes classifier method so that it can determine the status of the Covid pandemic based on test data provided by system users. This research has succeeded in helping users to know the status of the Covid pandemic in an area well because it has reliable training data.

Downloads

Download data is not yet available.

Article Details

How to Cite
Ikbal, M., Andryana, S., & Komala Sari, R. T. (2021). Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 5(4), 389–394. https://doi.org/10.35870/jtik.v5i4.233
Section
Computer & Communication Science

References

Susilo, A., Rumende, C.M., Pitoyo, C.W., Santoso, W.D., Yulianti, M., Herikurniawan, H., Sinto, R., Singh, G., Nainggolan, L., Nelwan, E.J. and Chen, L.K., 2020. Coronavirus Disease 2019: Tinjauan Literatur Terkini. Jurnal Penyakit Dalam Indonesia, 7(1), pp.45-67.

Azimah, R.N., Khasanah, I.N., Pratama, R., Azizah, Z., Febriantoro, W. and Purnomo, S.R.S., 2020. Analisis Dampak Covid-19 Terhadap Sosial Ekonomi Pedagang Di Pasar Klaten Dan Wonogiri. EMPATI: Jurnal Ilmu Kesejahteraan Sosial, 9(1), pp.59-68.

Prasetio, Y. and Haryanto, H., 2017. Visualisasi Berbasis Naive Bayes untuk Pemetaan Penyebaran Penyakit Infeksi Saluran Pernafasan Akut. Sisfotenika, 7(1), pp.74-84.

Wibawa, A.P., Kurniawan, A.C., Adiperkasa, R.P., Putra, S.M., Kurniawan, S.A. and Nugraha, Y.R., 2019. Naïve Bayes Classifier for Journal Quartile Classification. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 7(2), pp.91-99.

Fitrani, A.S., Fajrillah, F. and Novarika, W., 2019. Implementation of Data Mining Using Naïve Bayes Classification Method To Predict Participation of Governor And Vocational Governor Selection In Jemirahan Village, Jabon District. The IJICS (International Journal of Informatics and Computer Science), 3(2), pp.66-79.

Erdiansyah, M.Z., Taufik, T. and Raharjana, I.K., 2016. Visualisasi Data Menggunakan Sistem Informasi Geografis untuk Potensi Bank Sampah di Surabaya. Journal of Information Systems Engineering and Business Intelligence, 2(1), pp.40-49.

Setyoningrum, N.R., 2016. Perbandingan Antara Tiga Sdlc Methodology, Parallel, Iterative Dan Agile Development. Jurnal Bangkit Indonesia, 5(1), pp.32-32.

Saputra, M.F.A., Widiyaningtyas, T. and Wibawa, A.P., 2018. Illiteracy Classification Using K Means-Naïve Bayes Algorithm. JOIV: International Journal on Informatics Visualization, 2(3), pp.153-158.

R. Anusha and A. Prof., 2020. Predicting t he Student’s Preference Between Conventional Learning and E-Learning. Int. J. Adv. Sci. Technol., vol. 29, no. 4, pp. 5917–5922.

Erkan, U. and Gökrem, L., The Classification of The Students Success Via The Informations Existing In E-School System. Group, 10(80), p.1.

Most read articles by the same author(s)

1 2 > >>