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All Journal Journal of Language and Literature Science Map Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Asimetris: Pendidikan Matematika dan Sains ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Pattimura International Journal of Mathematics (PIJMath) Amalgamasi: Journal of Mathematics and Applications
Apriani, Wiwin
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Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Nursing Physics Social Sciences Other

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PEMODELAN DATA CURAH HUJAN DI KOTA LANGSA DENGAN MODEL ARIMA Apriani, Wiwin; Nurhayati
Amalgamasi: Journal of Mathematics and Applications Vol. 1 No. 2 (2022): Amalgamasi: Journal of Mathematics and Applications
Publisher : Universitas Pasifik Morotai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55098/amalgamasi.v1.i2.pp64-70

Abstract

The aim of this research is to provide the results of ARIMA modeling on rainfall data in Langsa City in 2017-2021. The initial stage of ARIMA modeling is the identification of data stationarity. Meanwhile, stationarity in the mean can be done with data plots and ACF forms. Identification of ACF and PACF forms from data that is already stationary is used to determine the order of the alleged ARIMA model. The next stage is parameter estimation to see the suitability of the model. The diagnostic check process is carried out to evaluate whether the residual model meets the white noise requirements and is normally distributed. The Ljung-Box test is a test that can be used to validate white noise requirements. Rainfall data forms a stationary time series. Furthermore, from the model fit test it was found that the MA(1) model was suitable for predicting the model. Meanwhile, AR(1) and ARMA(1,1) are not used to predict because they do not meet the model fit test. The model obtained with the MA(1) model is as follows, namely .
PREDIKSI PRODUKSI CRUDE OIL DENGAN MENGGUNAKAN MODEL DERET WAKTU: ARIMA (1,2,2) GARCH (1,1) Nurhayati; Apriani, Wiwin
Amalgamasi: Journal of Mathematics and Applications Vol. 2 No. 1 (2023): Amalgamasi: Journal of Mathematics and Applications
Publisher : Universitas Pasifik Morotai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55098/amalgamasi.v2.i1.pp10-23

Abstract

Crude oil termasuk kedalam komoditas penting yang menjadi sumber energi. Perubahan harga crude oil dapat mempengaruhi keadaan perekonomian dari suatu negara. Hal ini dikarenakan harga crude oil dalam suatu kondisi akan mengalami kenaikan atau penurunan yang signifikan. Salah satu model yang dapat digunakan untuk memprediksi jenis data deret waktu tersebut adalah ARIMA (Autoregressive Integrated Moving Average) atau GARCH (Generalized Autoregressive Conditional Heteroscedasticity). Adapun tujuan yang ingin dicapai adalah memprediksi data produksi crude oil dalam satuan barrel (BBL) pada rentang waktu Januari 2012 sampai Desember 2018. Dari hasil peramalan diperoleh bahwa model ARIMA (1,2,2) GARCH (1,1) merupakan model terbaik dan memberikan hasil prediksi yang cenderung menyerupai data asli
Co-Authors Bustan, Ariestha Widyastuty cahya, uci dwi Husaini Nurhayati Nurhayati Nurhayati Wahyu Fitriana, Aprilia Weni Astari, Weni Yusra, Syarifah Zulfa Razi Zulida, Evi