Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system

Mohamad Yusak Anshori (University of Nahdlatul Ulama Surabaya (UNUSA))
Teay Shawyun (Suwarnabhumi Vocational Institute of Technology)
Dennis V. Madrigal (University of Negros Occidental-Recoletos)
Dinita Rahmalia (University of Islam Darul Ulum Lamongan)
Fajar Annas Susanto (University of Nahdlatul Ulama Surabaya (UNUSA))
Teguh Herlambang (University of Nahdlatul Ulama Surabaya (UNUSA))
Dieky Adzkiya (Institut Teknologi Sepuluh Nopember)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 11, No 2: June 2022

Corona virus disease (COVID-19) have become a world health problem because they have attacked many people worldwide. Because this virus has spread massively in almost all countries, including Indonesia, the Indonesian government made some policies and rules to close down the hotels and restaurants to avoid the spread of COVID-19. Because of that, estimation of the number of closed down restaurants and hotels in Jakarta is vital for avoiding COVID-19 spreads further to other people, either domestic or foreign. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is chosen as the estimation method. In estimating the number of closed restaurants and hotels using ANFIS, supporting variables such as the amount of casualties in Jakarta, the amount of casualties in Indonesia, and the amount of casualties in the world is required. As a result, ANFIS can estimate the amount of closed down restaurants and hotels approaching the target. The simulations are organized by partitioning the dataset into two parts: data of (80%) and data of testing (20%). According to ANFIS simulations, ANFIS can estimate the number of closed down restaurants and hotels in training data with optimal RMSE equals 0.5324 and testing data with optimal RMSE equals 5.3198.