Research Article Predicting the Number of COVID-19 Sufferers in Malang City Using the Backpropagation Neural Network with the Fletcher–Reeves Method Syaiful Anam , Mochamad Hakim Akbar Assidiq Maulana , Noor Hidayat , Indah Yanti , Zuraidah Fitriah , and Dwi Mifta Mahanani Department of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, East Java, Indonesia CorrespondenceshouldbeaddressedtoSyaifulAnam;syaiful@ub.ac.id Received 19 December 2020; Revised 20 March 2021; Accepted 12 April 2021; Published 29 April 2021 AcademicEditor:WanHannaMelini Copyright©2021SyaifulAnametal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited. COVID-19 is a type of an infectious disease that is caused by the new coronavirus. e spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for suﬀerers with congenital diseases and a weak immune system. COVID-19spreadsthroughdirectcontact,whereintheinfectedindividualspreadstheCOVID-19virusthroughcough,sneeze,or close contacts. Predicting the number of COVID-19 suﬀerers becomes an important task in the eﬀort to curb the spread of COVID-19. Artiﬁcial neural network (ANN) is the prediction method that delivers eﬀective results in doing this job. Back- propagation, a type of ANN algorithm, oﬀers predictive problem solving with good performance. However, its performance dependsontheoptimizationmethodappliedduringthetrainingprocess.Ingeneral,theoptimizationmethodinANNisthe gradientdescentmethod,whichisknowntohaveaslowconvergencerate.Meanwhile,theFletcher–Reevesmethodhasafaster convergenceratethanthegradientdescentmethod.Basedonthishypothesis,thispaperproposesapredictionmodelforthe number of COVID-19 suﬀerers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. e experimentalresultsshowthattheBackpropagationneuralnetworkwiththeFletcher–Reevesmethodhasabetterperformance thantheBackpropagationneuralnetworkwiththegradientdescentmethod.isisshownbytheMeansSquareError(MSE) resultingfromtheproposedmethodwhichissmallerthantheMSEresultingfromtheBackpropagationneuralnetworkwiththe gradient descent method. 1.Introduction AttheendofDecember2019,Indonesiaandtheworldwere shockedbytheemergenceofaninfectiousdiseasethatat- tackstherespiratoryorgans.isdiseaseiscalledCOVID-19 [1].eCOVID-19diseaseisaninfectioncausedbyanew type of coronavirus. is virus was ﬁrst discovered in Wuhan City, Hubei Province, China, and then it spread throughout the world, including Indonesia. It spread through direct contact with disease suﬀerers who traveled fromtheinfectedareas[2]. eeﬀectsofthisdiseaseareveryseriousbecauseres- piration is a vital human organ that helps metabolic pro- cesses and balances substances in the body. In addition, COVID-19 can cause death for the suﬀerers [3], especially those with congenital diseases or a weak immune system. is disease quickly spreads because, like any other infec- tiousrespiratorydisease,thetransmissionofthevirusoccurs throughadropletfromthenoseormouthofthepersonwith COVID-19whentheycough,sneeze,orareinclosecontacts. erefore,duringapandemic,itishighlyrecommendedto putonmasksorprotectiveequipmentandcarryoutsocial restrictionstoreducethepotentialofspreadingthevirus[4]. e number of people with COVID-19 is increasing everyday.eincrementofthenumberofsuﬀererswiththis disease should be directly proportional to adequate health services. Predicting the number of COVID-19 suﬀerers basedonthedataofthenumberofpreexistingsuﬀerersis Hindawi Applied Computational Intelligence and So Computing Volume 2021, Article ID 6658552, 9 pages https://doi.org/10.1155/2021/6658552