?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=PENERAPAN+METODE+HYBRID+AUTOREGRESSIVE+INTEGRATED%0D%0AMOVING+AVERAGE+(ARIMA)-ARTIFICIAL+NEURAL+NETWORK+(ANN)%0D%0ADALAM+MERAMALKAN+TINGKAT+INFLASI+DI+INDONESIA&rft.creator=%09Anisa%2C++Aprilia&rft.subject=500+ilmu+pengetahuan+alam+dan+matematika&rft.description=Time+series+adalah+data+yang+disusun+berdasarkan+urutan+waktu%2C+data+time+series%0D%0Adigunakan+untuk+meramalkan+kejadian+di+masa+depan.+Metode+deret+waktu+yang%0D%0Asering+digunakan+yaitu+Autoregressive+Integrated+Moving+Average+(ARIMA)+yang%0D%0Amerupakan+model+univariate+yang+menangkap+pola+linear+pada+suatu+data.+Metode%0D%0AArtificial+Neural+Network+(ANN)+merupakan+metode+yang+menggunakan+prinsip%0D%0Ajaringan+syaraf+manusia+keuntungan+dari+metode+ini+yaitu+memiliki+kemampuan%0D%0Ayang+fleksibel+dalam+memodelkan+pola+nonlinear+pada+deret+waktu.+Sehingga%0D%0Apenggunaan+Metode+hybrid+ARIMA-ANN+dapat+memaksimalkan+hasil+peramalan%0D%0Ayang+berbentuk+pola+linear+dan+nonlinear.+Penelitian+ini+bertujuan+untuk%0D%0Amenerapkan+metode+hybrid+ARIMA-ANN+untuk+meramalkan+tingkat+inflasi+di%0D%0AIndonesia.+Hasil+analisis+menunjukan+penggunaan+metode+ARIMA+menghasilkan%0D%0Anilai+MAPE+sebesar+0.0850+dan+MSE+sebesar+6.3713e-05.+Kemudian+dengan%0D%0Ametode+hybrid+ARIMA-ANN+diperoleh+penggunaan+splitting+data+dengan+skema%0D%0A70%25+data+training+30%25+data+testing+memiliki+performa+yang+terbaik+dengan+nilai%0D%0AMSE+sebesar+1.2997e-05%2C+MAPE+sebesar+0.0968%2C+dan+akurasi+sebesar+99.9032%25.%0D%0ABerdasarkan+hasil+yang+diperoleh+nilai+MSE+metode+hybrid+dan+hasil+visualisasi%0D%0Aperamalan+metode+hybrid+lebih+baik+sehingga+metode+hybrid+lebih+baik+digunakan%0D%0Adalam+peramalan+jangka+panjang.%0D%0A%0D%0AKata+Kunci%3A+Hybrid+ARIMA-ANN%2C+ARIMA%2C+ANN%2C+Peramalan%2C+Inflasi%0D%0ATime+series+is+data+organized+by+time+sequence%2C+time+series+data+is+used+to+forecast%0D%0Afuture+events.+Time+series+method+that+often+used+is+Autoregressive+Moving%0D%0AAverage+(ARIMA)+which+is+a+univariate+model+that+capture+liniar+pattern+in+a+data.%0D%0AArtificial+Neural+Network+(ANN)+method+is+a+method+that+used+a+principle+of%0D%0Ahuman+neural+network+which+have+a+benefit+to+had+a+flexible+capabilities+to+model%0D%0Aa+nonlinear+pattern+in+time+series.+Regardy+this+capabilities%2C+the+us+of+hybrid%0D%0AARIMA-ANN+method+can+maximize+the+result+of+forecast+that+had+the+liniar+and%0D%0Anonlinear+pattern.+This+study+had+a+purpose+to+apply+hybrid+ARIMA-ANN+method%0D%0Afor+forecasting+inflation+rate+in+Indonesia.+The+analysis+shows+that+the+use+of+the%0D%0AARIMA+method+produces+a+MAPE+value+of+0.0850+and+MSE+of+6.3713e-05.+Then%0D%0Awith+the+hybrid+ARIMA-ANN+method%2C+it+is+found+that+the+use+of+data+splitting+with%0D%0A70%25+training+data+30%25+testing+data+scheme+has+the+best+performance+with+MSE%0D%0Avalue+of+1.2997e-05%2C+MAPE+of+0.0968%2C+and+accuracy+of+99.9032%25.+Based+on+the%0D%0Aresult%2C+MSE+value+from+hybrid+method+and+visualization+forecasting+result+of+hybrid%0D%0Amethod+are+better+to+use+for+forecasting+in+a+long+time+period.%0D%0A%0D%0AKeywords%3A+Hybrid+ARIMA-ANN%2C+ARIMA%2C+ANN%2C+Forecasting%2C+Inflation&rft.publisher=FAKULTAS+MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM+&rft.date=2024-04-01&rft.type=Skripsi&rft.type=NonPeerReviewed&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F83502%2F1%2FABSTRAK%2520-%2520Anisa%2520Aprilia.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F83502%2F2%2FSKRIPSI%2520FULL%2520-%2520Anisa%2520Aprilia.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F83502%2F3%2FSKRIPSI%2520TANPA%2520BAB%2520PEMBAHASAN%2520-%2520Anisa%2520Aprilia.pdf&rft.identifier=+++Anisa%2C+Aprilia++(2024)+PENERAPAN+METODE+HYBRID+AUTOREGRESSIVE+INTEGRATED+MOVING+AVERAGE+(ARIMA)-ARTIFICIAL+NEURAL+NETWORK+(ANN)+DALAM+MERAMALKAN+TINGKAT+INFLASI+DI+INDONESIA.++FAKULTAS+MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM+%2C+UNIVERSITAS+LAMPUNG.+++++&rft.relation=http%3A%2F%2Fdigilib.unila.ac.id%2F83502%2F