Prediction System for the Spread of Corona Virus in Central Java with K-Nearest Neighbor (KNN) Method

Farid Fitriyadi, Muqorobin Muqorobin

Abstract


Abstract—Corona Virus is currently spreading very rapidly in many parts of Indonesia, including Central Java Province. According to the current data of corona database in Central Java, today on 17th of August 2021, the number of confirmed cases is; Confirmed in Treatment (Active Cases): 16.344, Confirmed Recovered: 408.697, and Confirmed Dead: 29.148. Therefore, the total number of cases is 454.189, obtained from the sum of the number of being treated, recovered, and dead. Corona Virus is a collection of viruses that can infect the respiratory system, generally mild, such as common cold, although, some forms of diseases like; SARS, MERS, and COVID-19 are more deadly. In anticipating this case, the government has created some policies which include; limiting activities outside the house, having school activities done from home, working from home, and even having religious activities done from home too. The purpose of this study was to predict the possible rate of new cases in one of Central Java areas with confirmed cases of corona virus. Thus, it can be used as information material for the public to anticipate early. The research method applied in this research is problem analysis and literature study, data collection and implementation. The application of the K-Nearest Neighbor (KNN) method is expected to be able to predict the level of spread of COVID-19 in Central Java. The results of the research on testing the prediction system for the new cases level were tested in the Sragen area. Testing is carried out by taking samples for new cases, namely Kudu Regency/City, Confirmed: 17,599, Treated: 89, Recovered: 18,303, Died: 1,721, Suspected: 87 and Discarded Suspected: 1,711. After doing the prediction with K-NN algorithm, it showed the Condition: High.


Full Text:

PDF

References


1. Mona, Nailul. 2020. Konsep Isolasi Dalam Jaringan Sosial Untuk Meminimalisasi Efek Contagious (Kasus Penyebaran Virus Corona Di Indonesia). Jurnal Sosial HumanioraTerapan (JSHT), 2(2), pp. 117. doi: https://doi.org/10.7454/jsht.v2i2.86.

2. Saleh, Alfa. 2015. Implementasi Metode Klasifikasi Naive Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga. Creative Information Technology Journal (Citec Journal), 2(3), pp. 208-216. doi: https://doi.org/10.24076/ citec.2015v2i3. 49.

3. Bustami. 2013. PenerapanAlgoritma Naive Bayes UntukMengklasifikasi Data NasabahAsuransi. TECHSI (JurnalPenelitian Teknik Informatika), 3(2),pp. 127-146. doi: https://doi.org/10.29103/techsi.v5i2.154.

4. Ridwan, M., Suyono, H., Sarosa, M. 2013. Penerapan Data Mining untukEvaluasiKinerja AkademikMahasiswaMenggunakanAlgoritma Naive Bayes Classifier. Jurnal EECCIS, 1 (7), pp. 59-64.

5. Mujiasih, S. 2011. Pemanfaatan Data Mining UntukPrakiraanCuaca. JurnalMeteorologidan Geofisika, 12(2), pp. 189-195. doi: http://dx.doi.org/10.31172/jmg.v12i2.100

6. Yunus, N. R., &Rezki, Annisa. 2020. KebijakanPemberlakuan Lockdown SebagaiAntisipasiPenyebaran Corona Virus Covid-19. SALAM; JurnalSosial&BudayaSyar-i, 7(3), pp. 227-238. doi: https://doi.org/10.15408/sjsbs.v7i3.15083.

7. Muqorobin, M., &Rais, N. A. R. (2020). Analysis of the Role of Information Systems Technology in Lecture Learning during the Corona Virus Pandemic. International Journal of Computer and Information System (IJCIS), 1(2), 47-51.

8. Hikmah, I. N., &Muqorobin, M. (2020). Employee Payroll Information System On Company Web-Based Consultant Engineering Services. International Journal of Computer and Information System (IJCIS), 1(2), 27-30.

9. Muslihah, I., & Muqorobin, M. (2020). Texture Characteristic of Local Binary Pattern on Face Recognition with PROBABILISTIC LINEAR DISCRIMINANT ANALYSIS. International Journal of Computer and Information System (IJCIS), 1(1), 22-26.

10. Muryani, A. S., & Muqorobin, M. (2020). Decision Support System Using Cloud-Based Moka Pos Application To Easy In Input In Orange Carwash Blulukan Flash N0. 110 Colomadu. International Journal of Computer and Information System (IJCIS), 1(3), 66-69.

11. Jannah, N. F., & Muqorobin, M. (2021). Analysis Of Kasir Applications In Sales Management Information Systems at ASRI Store. International Journal of Computer and Information System (IJCIS), 2(2), 40-44.

12. Santoso, L. P., Muqorobin, M., & Fatkhurrochman, F. (2020). Online Analysis System of Application of Partners for Land Asrocument Officers of Sukoharjo District. International Journal of Computer and Information System (IJCIS), 1(3), 59-61.

13. Muqorobin, M., Apriliyani, A., & Kusrini, K. (2019). Sistem Pendukung Keputusan Penerimaan Beasiswa dengan Metode SAW. Respati, 14(1).

14. Muqorobin, M., & Rais, N. A. R. (2020, November). ANALISIS PERAN TEKNOLOGI SISTEM INFORMASI DALAM PEMBELAJARAN KULIAH DIMASA PANDEMI VIRUS CORONA. In Prosiding Seminar Nasional & Call for Paper STIE AAS (pp. 157-168).

15. Muqorobin, M., & Rais, N. A. R. (2020). Analysis of the Role of Information Systems Technology in Lecture Learning during the Corona Virus Pandemic. International Journal of Computer and Information System (IJCIS), 1(2), 47-51.




DOI: https://doi.org/10.29040/ijcis.v2i3.41

Article Metrics

Abstract view : 504 times
PDF - 270 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.