The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia


Aris Martono(1); Henderi Henderi(2*); Giandari Maulani(3);

(1) Universitas Raharja
(2) Universitas Raharja
(3) Universitas Raharja
(*) Corresponding Author

  

Abstract


This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.


Keywords


Deep Learning; k-Nearest Neighbor; RMSE; Support Vector Machine

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 427 times
PDF view: 160 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v15i2.1234.240-249 		  

Cite

How to cite item

References


Y. Zhang and J. Sun, "A COVID-19 Epidemics Trend Prediction Algorithm Based on LSTM," in 2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China, 2021, doi: 10.1109/CCET52649.2021.9544257.

A. Damone, A. Damone, A. Damone, F. Bonino, S. Nuti and G. Ciuti, "Decision-Making Algorithm and Predictive Model to Assess the Impact of Infectious Disease Epidemics on the Healthcare System: The COVID-19 Case Study in Italy," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 3661 - 3672, August. 2022, doi: 10.1109/JBHI.2022.3174470.

A. Hernita, M. A. Soeleman and A. Z. Fanani, "Optimization Of Infant Birth Predictions During The Covid-19 Pandemic Using The Particle Swarm Optimization Based K-Nn Algorithm Method," in 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, 2022, doi: 10.1109/iSemantic55962.2022.9920468.

T. Mantoro, R. T. Handayanto, M. A. Ayu and J. Asian, "Prediction of COVID-19 Spreading Using Support Vector Regression and Susceptible Infectious Recovered Model," in 2020 6th International Conference on Computing Engineering and Design (ICCED), Sukabumi, Indonesia, 2020, doi: 10.1109/ICCED51276.2020.9415858.

M. D. Darma, M. R. Faisal, I. Budiman, R. Herteno, J. P. Utami and B. Abapihi, "In Silico Prediction of Indonesian Herbs Compounds as Covid-19 Supportive Therapy using Support Vector Machine," in 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), Depok, Indonesia, 2021, doi: 10.1109/IC2IE53219.2021.9649383.

A. Y. A. Saeed and A. E. B. Alawi, "Covid-19 Diagnosis Model Using Deep Learning with Focal Loss Technique," in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Taiz, Yemen, 2021, doi: 10.1109/ICOTEN52080.2021.9493477.

N. Agarwal and R. Dutta, " Comparative Predictive Analysis of Mortality Rate after Covid-19 Vaccination Using Various Machine Learning Approaches," in 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2022, doi: 10.1109/ICCCI54379.2022.9740854.

T. Thongrod, A. Lim, T. Ingviya and B. A. Owusu, "Prediction of PM2.5 and PM10 in Chiang Mai Province: A Comparison of Machine Learning Models," in 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Phuket, Thailand, 2022, doi: 10.1109/ITC-CSCC55581.2022.9894884.

D. Alekseeva, N. Stepanov, A. Veprev, A. Sharapova, E. S. Lohan and A. Ometov, "Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network," IEEE Access , vol. 9, pp. 159495 - 159514, November 2021, doi: 10.1109/ACCESS.2021.3129850.

J. Xie and Q. Wang, "Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models," IEEE Transactions on Biomedical Engineering, vol. 67, no. 11, pp. 3101 - 3124, February 2020, doi: 10.1109/TBME.2020.2975959.

H. H. Elmousalami, "Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis," IEEE Transactions on Engineering Management , vol. 68, no. 1, pp. 183 - 196, February 2021, doi: 10.1109/TEM.2020.2972078.

O. Eyecioglu, B. Hangun, K. Kayisli and M. Yesilbudak, "Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation," in 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), Brasov, Romania, 2019, doi: 10.1109/ICRERA47325.2019.8996541.

I. Ahmed, T. E. Chowdhury, B. B. Routh, N. Tasmiya, S. Sakib and A. A. Chowdhury, "Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction," in 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, doi: 10.1109/IEMCON56893.2022.9946591.

S. Kaya and M. Yağanoğlu, "An Example of Performance Comparison of Supervised Machine Learning Algorithms Before and After PCA and LDA Application: Breast Cancer Detection," in 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, 2020, doi: 10.1109/ASYU50717.2020.9259883.

P. Cıhan and H. Coşkun, "Performance Comparison of Machine Learning Models for Diabetes Predictio," in 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, 2021, doi: 10.1109/SIU53274.2021.9477824.

A. Sunarya, Henderi and I. Tasyriqan, "The Comparison Between Sequential Minimal Optimization and Multilayer Perceptron Neural Network Methods in Predicting the Commodity Prices," in 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia, 2019, doi: 10.1109/ICIC47613.2019.8985679.

P. Subarkah, W. R. Damayanti and R. A. Permana, "Comparison of correlated algorithm accuracy Naive Bayes Classifier and Naive Bayes Classifier for heart failure classification," ILKOM Jurnal Ilmiah, vol. 14, no. 2, pp. 120-125, August 2022.

P. Marquez, D. Pinos and I.-O. Juan, "Performance comparison in network traffic prediction for polynomial regression to P1P versus ARIMA and MWM," in 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow and St. Petersburg, Russia, 2018, doi: 10.1109/EIConRus.2018.8317034.

E. H. Yossy, Y. Heryadi and Lukas, "Comparison of Data Mining Classification Algorithms for Student Performance," in 2019 IEEE International Conference on Engineering, Technology and Education (TALE), Yogyakarta, Indonesia, 2019, doi: 10.1109/TALE48000.2019.9225887.

S. A. Septianingrum, M. A. Dzikri, M. A. Soeleman, P. Pujiono and M. Muslih, "Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House," in 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, 2022, doi: 10.1109/iSemantic55962.2022.9920454.

G. R. Thummala, R. Baskar and N. Thiyaneswaran, "Prediction of Heart Disease Using Naive Bayes in Comparison with KNN Based on Accuracy," in 2022 International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates, 2022, doi: 10.1109/ICCR56254.2022.9995841.

Y. Niu, X. Lu, X. Li, W. Su, Z. Meng and S. Zhang, "Modeling and Analysis of Runway Friction Coefficient Prediction Methods Based on Multivariable Coupling," IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022, doi: 10.1109/TIM.2022.3148760.

B. C. M and N. Deepa, "Improving Performance Analysis in Classification with Accuracy of Adult Income Salary using Novel Gated Residual Neural Network in Comparison with Logistic Regression Algorithm," in 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, 2022, doi: 10.1109/ICSES55317.2022.9914103.

P.-Y. Hsu and C. Holtz, "A Comparison of Machine Learning Tools for Early Prediction of Sepsis from ICU Data," in 2019 Computing in Cardiology (CinC), Singapore, 2019, doi: 10.22489/CinC.2019.206.

M. Chellamani, S. Murugesan and N. Raju, "Heart disease prediction using Boosting Algorithms: Performance Analysis and Comparison," in 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, doi: 10.1109/MysuruCon55714.2022.9972536.

Y. Hong and F. Lv, "Data Mining and Knowledge Discovery in Computer Aided Medical Diagnosis System," in 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2021, doi: 10.1109/I-SMAC52330.2021.9640713.

S. Khlamov, V. Savanevych, O. Briukhovetskyi, I. Tabakova and T. Trunova, "Astronomical Knowledge Discovery in Databases by the CoLiTec Software," in 2022 12th International Conference on Advanced Computer Information Technologies (ACIT), Ruzomberok, Slovakia, 2022, doi: 10.1109/ACIT54803.2022.9913188.

V. Gancheva and P. Borovska, "SOA Based System for Big Genomic Data Analytics and Knowledge Discovery," in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, doi: 10.1109/IDAACS.2019.8924370.

N. Farhat, R. Fatima, S. Kazi and H. Naaz, "A Comparison of Machine Learning Algorithms for Emotional Intelligence Measure," in 2022 Fifth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU), Riyadh, Saudi Arabia, 2022, doi: 10.1109/WiDS-PSU54548.2022.00036.

M. F. Suleiman and B. Issac, "Performance Comparison of Intrusion Detection Machine Learning Classifiers on Benchmark and New Datasets," in 2018 28th International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, 2018, doi: 10.1109/ICCTA45985.2018.9499140.

Henderi, T. Wahyuningsih and E. Rahwanto, "Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer," International Journal of Informatics and Information System, vol. 4, no. 1, pp. 13-20, 2021.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Aris Martono, Henderi, Giandari Maulani

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