TY - GEN
T1 - COVID-19 Viruses Link Prediction Using Graph Algorithms And Random Forest
AU - Hidayat, Rahmat
AU - Baharuddin, Fikri
AU - Rakhmawati, Nur Aini
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The ongoing coronavirus pandemic has caused the spread of information related to this virus to continue growing. COVID-19 mutates and gives rise to new variants. A taxonomy graph of these viruses can help us see the connections between viruses. Thus, we implemented a taxonomic graph of COVID-19 to determine the cause of the virus and its parent of origin. The virus dataset was retrieved from Wikidata. We exploited several graph algorithms to create the taxonomy graph: link prediction, triangle/grouping coefficients, and community detection algorithms. The results of the graph algorithms become the features of the Random Forest classifier. Random Forest predicts the relationship between two viruses. The research results showed average scores of accuracy, precision, and recall of 90%, 90%, and 91 %, respectively.
AB - The ongoing coronavirus pandemic has caused the spread of information related to this virus to continue growing. COVID-19 mutates and gives rise to new variants. A taxonomy graph of these viruses can help us see the connections between viruses. Thus, we implemented a taxonomic graph of COVID-19 to determine the cause of the virus and its parent of origin. The virus dataset was retrieved from Wikidata. We exploited several graph algorithms to create the taxonomy graph: link prediction, triangle/grouping coefficients, and community detection algorithms. The results of the graph algorithms become the features of the Random Forest classifier. Random Forest predicts the relationship between two viruses. The research results showed average scores of accuracy, precision, and recall of 90%, 90%, and 91 %, respectively.
KW - Covid-19
KW - Graph Algorithm
KW - Random Forest
KW - Taxonomy Graph
UR - http://www.scopus.com/inward/record.url?scp=85181083418&partnerID=8YFLogxK
U2 - 10.1109/ICEEIE59078.2023.10334921
DO - 10.1109/ICEEIE59078.2023.10334921
M3 - Conference contribution
AN - SCOPUS:85181083418
T3 - ICEEIE 2023 - International Conference on Electrical, Electronics and Information Engineering
BT - ICEEIE 2023 - International Conference on Electrical, Electronics and Information Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2023
Y2 - 28 September 2023 through 29 September 2023
ER -