?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=IMPLEMENTASI+MODEL+VECTOR+AUTOREGRESSIVE+INTEGRATED%0D%0AMOVING+AVERAGE+(VARIMA)+PADA+PERAMALAN+DATA%0D%0APEMBUKAAN+DAN+PENUTUPAN+SAHAM+NETFLIX&rft.creator=Wira+%2C+Adiguna+Sutawa&rft.subject=500+ilmu+pengetahuan+alam+dan+matematika&rft.subject=510+Matematika&rft.description=Model+VARIMA+(Vector+Autoregressive+Integrated+Moving+Average)+adalah%0D%0Apengembangan+dari+model+ARIMA+(Autoregressive+Integrated+Moving+Average)%0D%0Ayang+digunakan+untuk+menangani+data+deret+waktu+multivariat%2C+di+mana+lebih+dari%0D%0Asatu+variabel+atau+komponen+deret+waktu+dipelajari+secara+simultan.+Model+ini%0D%0Aterdiri+dari+tiga+komponen+utama+yaitu+Vector+Autoregressive+(VAR)%2C+Differencing%0D%0Adan+Vector+Moving+Average+(VMA).+Penelitian+ini+bertujuan+untuk+meramalkan%0D%0Aharga+saham+pembukaan+dan+penutupan+Netflix+menggunakan+model+Vector%0D%0AAutoregressive+Integrated+Moving+Average+(VARIMA).+Data+yang+digunakan%0D%0Aadalah+harga+saham+dari+tahun+2014+hingga+2023%2C+yang+dianalisis+dengan+metode%0D%0Atime+series+multivariat.+Uji+stasioneritas+dilakukan+menggunakan+uji+Augmented%0D%0ADickey-Fuller+(ADF)+dan+transformasi+Box-Cox+untuk+memastikan+data+sesuai%0D%0Adengan+asumsi+model.+Model+terbaik+dipilih+berdasarkan+nilai+Akaike+Information%0D%0ACriterion+(AIC)+dan+Bayesian+Information+Criterion+(BIC).+Hasil+analisis%0D%0Amenunjukkan+bahwa+model+VARIMA(1%2C1%2C1)+memberikan+performa+terbaik+dengan%0D%0Aakurasi+prediksi+tinggi%2C+ditunjukkan+oleh+nilai+Mean+Absolute+Percentage+Error%0D%0A(MAPE)+sebesar+0.0649%25+dan+Root+Mean+Square+Error+(RMSE)+sebesar+2492.%0D%0AModel+ini+kemudian+digunakan+untuk+peramalan+harga+saham+untuk+6+bulan+ke%0D%0Adepan%2C+memberikan+prediksi+yang+dapat+membantu+dalam+pengambilan+keputusan%0D%0Ainvestasi.+Kesimpulannya%2C+model+VARIMA(1%2C1%2C1)+merupakan+alat+yang+akurat+dan%0D%0Aandal+dalam+meramalkan+pergerakan+harga+saham+Netflix%2C+memberikan+wawasan%0D%0Apenting+bagi+investor+dan+analis.%0D%0A%0D%0AKata+kunci+%3A+Peramalan%2C+Time+Series%2C+VARIMA%0D%0A%0D%0AThe+VARIMA+(Vector+Autoregressive+Integrated+Moving+Average)+model+is+an%0D%0Aextension+of+the+ARIMA+(Autoregressive+Integrated+Moving+Average)+model%2C+used%0D%0Ato+handle+multivariate+time+series+data%2C+where+more+than+one+variable+or+time+series%0D%0Acomponent+is+studied+simultaneously.+This+model+consists+of+three+main%0D%0Acomponents%3A+vector+autoregressive+(VAR)%2C+differencing%2C+and+vector+moving%0D%0Aaverage+(VMA).+This+study+aims+to+forecast+Netflix's+opening+and+closing+stock%0D%0Aprices+using+the+Vector+Autoregressive+Integrated+Moving+Average+(VARIMA)%0D%0Amodel.+This+model+consists+of+three+main+components%3A+vector+autoregressive%0D%0A(VAR)%2C+differencing%2C+and+vector+moving+average+(VMA).+The+data+used+includes%0D%0Astock+prices+from+2014+to+2023%2C+analyzed+using+multivariate+time+series+methods.%0D%0AStationarity+tests+were+conducted+using+the+Augmented+Dickey-Fuller+(ADF)+test%0D%0Aand+Box-Cox+transformation+to+ensure+the+data+meets+model+assumptions.+The+best%0D%0Amodel+was+selected+based+on+the+Akaike+Information+Criterion+(AIC)+and+Bayesian%0D%0AInformation+Criterion+(BIC).+The+analysis+results+show+that+the+VARIMA(1%2C1%2C1)%0D%0Amodel+delivers+the+best+performance+with+high+prediction+accuracy%2C+as+indicated+by%0D%0Aa+Mean+Absolute+Percentage+Error+(MAPE)+of+0.0649%25+and+a+Root+Mean+Square%0D%0AError+(RMSE)+of+2492.+This+model+was+then+used+to+forecast+stock+prices+for+the%0D%0Anext+six+months%2C+providing+predictions+that+can+aid+in+investment+decision-making.%0D%0AIn+conclusion%2C+the+VARIMA(1%2C1%2C1)+model+is+an+accurate+and+reliable+tool+for%0D%0Aforecasting+Netflix+stock+price+movements%2C+offering+valuable+insights+for+investors%0D%0Aand+analysts.%0D%0AKeyword+%3A+Forecasting%2C+Time+Series%2C+VARIMA%0D%0A&rft.publisher=FAKULTAS+MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM&rft.date=2024-10-03&rft.type=Skripsi&rft.type=NonPeerReviewed&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F84211%2F1%2FAbstrak_Wira%2520Adiguna%2520Sutawa%2520-%2520WIRA%2520ADIGUNA%2520SUTAWA.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F84211%2F2%2FSKRIPSI_FULL_WIRA%2520ADIGUNA%2520SUTAWA%2520-%2520WIRA%2520ADIGUNA%2520SUTAWA.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F84211%2F3%2FSKRIPSI_FULL%2520TANPA%2520BAB%2520PEMBAHASAN_WIRA%2520ADIGUNA%2520SUTAWA%2520-%2520WIRA%2520ADIGUNA%2520SUTAWA.pdf&rft.identifier=++Wira+%2C+Adiguna+Sutawa++(2024)+IMPLEMENTASI+MODEL+VECTOR+AUTOREGRESSIVE+INTEGRATED+MOVING+AVERAGE+(VARIMA)+PADA+PERAMALAN+DATA+PEMBUKAAN+DAN+PENUTUPAN+SAHAM+NETFLIX.++FAKULTAS+MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM%2C+UNIVERSITAS+LAMPUNG.+++++&rft.relation=http%3A%2F%2Fdigilib.unila.ac.id%2F84211%2F