Studi Komparasi Prediksi Curah Hujan Metode Fast Fourier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) dan Artificial Neural Network (ANN)

https://doi.org/10.22146/agritech.9412

Dyah Susilokarti(1*), Sigit Supadmo Arif(2), Sahid Susanto(3), Lilik Sutiarso(4)

(1) Direktorat Jenderal Prasarana dan Sarana Pertanian, Kementrian Pertanian, Jl. R.M. Harsono No. 3 Ragunan - Pasar Minggu, Jakarta Selatan 12550
(2) Jurusan Teknik Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281
(3) Jurusan Teknik Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281
(4) Jurusan Teknik Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281
(*) Corresponding Author

Abstract


Optimum climate condition and water availability are essential to support strategic venue and time for plants to grow and produce.  Precipitation prediction is needed to determine how much precipitation will provide water for plants on each stage of growth. Nowadays, the high variability of precipitation calls for a prediction model that will accurately foresee the precipitation condition in the future. The prediction conducted is based on time-series data analysis. The research aims to comparethe effectiveness of three precipitation prediction methods, which are Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). Their respective performances are determined by their Mean Square Error (MSE) values. Methods with highest correlation values and lowest MSE shows the best performance. The MSE result for FFT is 14,92; ARIMA is 17,49; and  ANN is 0,07. This research concluded that Artificial Neural Network (ANN) method showed best performance compare to the other two because it had produced a prediction with the lowest MSE value.

ABSTRAK

Kondisi iklim dan ketersediaan air yang optimal bagi pertumbuhan dan perkembangan tanaman sangat diperlukan dalam upaya mendukung strategi budidaya tanaman sesuai ruang dan waktu. Prediksi curah hujan sangat diperlukan untuk untuk mengetahui sejauh mana curah hujan dapat memenuhi kebutuhan air pada setiap tahap pertumbuhan tanaman. Variabilitas curah hujan yang tinggi saat ini, membutuhkan pemodelan yang dapat memprediksi secara akurat bagaimana kondisi curah hujan dimasa yang akan datang. Prediksi yang dilakukan adalah prediksi berdasarkan urutan waktu (time-series).  Tujuan dari penelitian ini adalah untuk membandingkan akurasi prediksi curah hujan antara metode Fast Farier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) dan Artificial Neural Network (ANN). Kinerja ketiga metode yang digunakan dilihat dari nilai Mean Square Error (MSE). Metode dengan nilai korelasi tertinggi dan nilai MSE terkecil menunjukkan kinerja terbaik. Hasil penelitan untuk FFT diperoleh nilai MSE = 14,92, ARIMA = 17,49 sedangkan ANN = 0,07. Ini menunjukkan bahwa metode Artificial Neural Network (ANN) menunjukkan kinerja yang paling baik diantara dua metode lainnya karena menghasilkan prediksi yang mempunyai nilai MSE terkecil.


Keywords


Precipitation prediction; Fast Forier Transformation (FFT); Autoregressive Integrated Moving Average (ARIMA); Artificial Neural Network (ANN)

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DOI: https://doi.org/10.22146/agritech.9412

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Agritech (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.


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