Komparasi Model Prediksi Kurs Pada Masa Pandemi Covid-19 Menggunakan Neural Network Berbasis Genetic Algorithm dan Particle Swarm Optimization

Ali Nur Ikhsan, Primandani Arsi, Jali Suhaman

Abstract


Data from Bank Indonesia shows that the rupiah exchange rate against dollar weakened at the beginning of the Covid-19 pandemic. This exchange rate volatility is an important problem in the Indonesian economy. Therefore, the prediction model for the exchange rate against the dollar is needed during the Covid-19 pandemic to predict the exchange rate during the Covid-19 Pandemic. This study is proposed to compare the prediction of the rupiah exchange rate against the dollar using the GA-based Neural Network algorithm and the PSO-based Neural Network algorithm. Initially the data was collected in the period 2019 to 2021, then the data is preprocessed. Validation used the k-fold validation technique with a ratio of 70:30, while the evaluation is carried out with the output of RMSE. The results showed that the performance of PSO and GA was the same, namely 0.020 +/- 0.006.


Keywords


ga-based neural network; pso-based neural network; rupiah exchange rate

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References


I.SimorangkirandSuseno,SistemdanKebijakanNilaiTukar,SeriKeban.,no.12.Jakarta:PusatPendidikandanStudiKebanksentralan(PPSK)BankBI,2004.[2]L.Shu,K.Shi,andH.Ye,“Exchangeratevolatilityandtrade:Theroleofcreditconstraints,”Rev.Econ.Dyn.,vol.30,no.April,pp.203–222,2018.[3]J.Caballero,C.Candelaria,andG.Hael,“Banklinkagesandinternationaltrade,”J.Int.Econ.,vol.115,pp.30–47,2018.[4]K.Kallahi-KaraiandP.Safari,“FutureexchangeratesandSiegel’sparadox,”Glob.Financ.J.,vol.37,pp.168–172,2018.[5]K.Lee,“Systematicexchangeratevariation:Wheredoesthedollarfactorcomefrom?,”Int.Rev.Econ.Financ.,vol.56,pp.288–307,2017.[6]C.-V.DemianandF.DiMauro,“Theexchangerate,asymmetricshocksandasymmetricdistributions,”Int.Econ.,vol.154,pp.68–85,2017.[7]S.S.Devi,“PengaruhInflasidanNilaiTukar/KursTerhadapIndeksHargaSahamGabungan(IHSG)yangTerdaftardiBursaEfekIndonesia(BEI)PadaMasaPandemiCovid-19BulanJanuari-Desember2020,”J.Inov.Mhs.Manaj.,vol.1,no.2,pp.139–149,2021.[8]C.PandaandV.Narasimhan,“Forecastingexchangeratebetterwithartificialneuralnetwork,”J.PolicyModel.,vol.29,no.2,pp.227–236,2006.[9]T.N.Pandey,A.Jagadev,S.Dehuri,andS.B.Cho,“Anovelcommitteemachineandreviewsofneuralnetworkandstatisticalmodelsforcurrencyexchangerateprediction:Anexperimentalanalysis,”J.KingSaudUniv.-Comput.Inf.Sci.,2018.[10]S.Mamadli,“Analysisofchaosandnonlinearitiesinaforeignexchangemarket,”inProcediaComputerScience,2017,vol.120,pp.901–907.[11]H.G.KeefeandH.Shadmanicor,“Foreignexchangemarketinterventionandasymmetricpreferences,”Emerg.Mark.Rev.,p.#pagerange#,2018.[12]F.Shen,J.Chao,andJ.Zhao,“Forecastingexchangerateusingdeepbeliefnetworksandconjugategradientmethod,”Neurocomputing,vol.167,pp.243–253,2015.[13]H.ThinyaneandJ.Millin,“AnInvestigationintotheUseofIntelligentSystemsforCurrencyTrading,”Comput.Econ.,vol.37,no.4,pp.363–374,2011.[14]T.Y.E.Nababan,B.Warsito,andA.Rusgiyono,“PemodelanWaveletNeuralNetworkuntukPrediksiNilaiTukarRupiahTerhadapDolarAS,”J.Gaussian,vol.9,pp.217–226,2020.[15]I.D.G.Budiastawa,I.Santiyasa,andC.R.A.Pramartha,“PrediksiDanAkurasiNilaiTukarMataUangRupiahTerhadapUSDolarMenggunakanRadialBasisFunctionNeuralNetwork,”J.IlmuElektron.IlmuKomput.Udayana,vol.7,no.4,2019.[16]A.H.Moghaddam,M.H.Moghaddam,andM.Esfandyari,“Stockmarketindexpredictionusingartificialneuralnetwork,”J.Econ.Financ.Adm.Sci.,vol.21,no.41,pp.89–93,2016.[17]N.Nikentari,H.Kurniawan,N.Ritha,andD.Kurniawan,“OptimasiJaringanSyarafTiruanBackpropagationDenganParticleSwarmOptimizationUntukPrediksiPasangSurutAirLaut,”J.Teknol.Inf.danIlmuKomput.,vol.5,no.5,2018.[18]P.ArsiandJ.Prayogi,“OptimasiPrediksiNilaiTukarRupiahTerhadapDolarMenggunakanNeuralNetworkBerbasiskanAlgoritmaGenetika,”J.Inform.BSI,vol.7,no.1,pp.8–14,2020.[19]E.VeriantoandS.B.D.Oetomo,“ArtificialNeuralNetworkModelwithPSOasaLearningMethodtoPredictMovementoftheRupiahExchangeRateagainsttheUSDollar,”Int.J.ofAppliedInf.Technol.,vol.04,no.02,pp.0–2,2021.[20]S.A.Wicaksono,“OptimasiSistemPenempatanMagangMenerapkanAlgoritmeGenetika,”J.Teknol.Inf.danIlmuKomput.,vol.6,no.1,2019.[21]RidwansyahandE.Purwaningsih,“ParticleSwarmOptimizationuntukMeningkatkanAkurasiPrediksiPemasaranBank,”PilarNusaMandiri,vol.14,no.1,pp.83–88,2018.[22]N.F.Istighfarin,“PenerapanMetodeParticleSwarmOptimization(PSO)DanGeneticAlgorithm(Ga)PadaSistemOptimasiVisibleLightCommunication(VLC)UntukMenentukanPosisiRobot,”J.SIMETRIS,vol.11,no.1,pp.279–286,2020.[23]M.Nabipour,P.Nayyeri,H.Jabani,S.S,andA.Mosavi,“PredictingStockMarketTrendsUsingMachineLearningandDeepLearningAlgorithmsViaContinuousandBinaryData;aComparativeAnalysis,”inIEEEAccess,2020,pp.150199–150212.




DOI: https://doi.org/10.35970/infotekmesin.v13i1.938

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