Analisis Prediksi Debit Sungai Amprong Dengan Model Arima (Autoregressive Integrated Moving Average) Sebagai Dasar Penyusunan Pola Tata Tanam
Keywords:ARIMA, Crop intensity, Discharge Prediction, Planting pattern
AbstractAn accurate determination of water availability in the 10-day period of the Amprong River has an important role in the planting system to support the agricultural production process in DI. Kedungkandang, because if the availability of water is not precisely determined, there will be an error in regulating irrigation water and its use is not as expected. To overcome these problems, an analysis system is needed that is able to make predictions well. One of the time series models is the ARIMA (Autoregressive Intregated Moving Average) model. The model was built by 9 period discharge data, namely 2008/2009 until 2016/2017, to predict the discharge of period 2017/2018. Of the ten tentative models obtained, there are only five models that are worth using. The best model is the ARIMA model (2,0,1) (1,2,1) 36 with the value of MSE = 22,90; KR = 6.00; MSD = 8.05; MAD = 2.04; MAPE = 18.53 and MPE = -8.98. In second crop season the crop intensity of paddy increased from 55.79% to 64.50%, and the production of GBK increased by 13.50%. While the third crop season paddy crop intensity increased from 37.22% to 49.99%, and GBK production increased by 25.54%.
Arsyad, L. 1994. Peramalan Bisnis. Yogyakarta: BPFE.
Aswi & Sukarna. 2006. Analisis Deret Waktu. Penerbit Andira, Makasar.
Chatfield. 2001. Time-Series Forecasting.
Hanggara, Ikrar dkk. 2015. Analisa Peramalan Debit Sungai Menggunakan Metode ARIMA (Auto Regressive Integrated moving Average) di Sungai Brantas Hulu. Jurnal Teknik Pengairan, Volume 6, Nomor 2, Desember 2015, Halaman 197-205.
Kementrian Pekerjaan Umum. (2009). Pendugaan Data Runtut Waktu Menggunakan Metode ARIMA.
Makridakis, S., S. C. Wheelwright dan V. E. McGee. 1988. Metode dan Aplikasi Peramalan. Jakarta: Erlangga.
Martani, Yustisia. 1997. Pengelolaan Irigasi. Dinas Pekerjaan Umum Daerah Tingkat I Jawa Timur. Surabaya.
Mulyana, 2004. Analist Data Deret Waktu, (Buku Ajar), Universitas Padjajaran, Jawa Barat.
Nigam, Rashmi et. al. 2009. Time Series Modeling and Forecast of River Flow. Journal of Current World Environment, Vol. 4 (1), 2009, 79-87.
Pramujo, Bambang dkk. 2015. Pemodelan Debit Menggunakan Metode Arima Guna Menentukan Pola Operasi Waduk Selorejo. Jurnal Teknik Pengairan, Volume 5, Nomor 2, Desember 2014, Halaman 141-148.
Subagyo, Pangestu. 1986. Forecasting konsep dan Aplikasi. Yogyakarta: BPPE UGM.
Valipour, Mohammad et. al. 2012. Parameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow Forecasting. Journal of Mathematics and Statistics, Vol. 8 (3), 2012, 330-338.
Valipour, Mohammad et. al. 2013. Comparison of The ARMA, ARIMA and the Autoregressive Artificial Neural Network Models in Forecasting The Monthly Inflow of Dez Dam Reservoir. Journal of Hydrology, Vol. 476, 2013, 433-441.
Zakaria, Saleh et. al. 2012. ARIMA Models for Weekly Rainfall in the Semi-arid Sinjar District at Iraq. Journal of Earth Sciences and Geotechnical Engineering, Vol. 2, No. 3, 2012, 25-55.
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