Arumningtyas, Maulita (2018) Peramalan Data Intermiten Curah Hujan Menggunakan Metode Penggabungan Arima Dan Group Method Of Data Handling (GMDH) (Studi Kasus: Stasiun Meteorologi Perak). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penelitian sebelumnya terkait peramalan menggunakan metode penggabungan ARIMA dan GMDH yang dilakukan oleh Shabri dan Samsudin telah menghasilkan peramalan yang efektif pada 114 data observasi Canadian Lynx. Hasil Error yang dihasilkan cenderung kecil dibandingkan menggunakan metode ARIMA ataupun Artifical Neural Network (ANN.)
Penelitian kali ini, ingin mencoba menerapkan metode yang sama pada intermittent data pada data timeseries curah hujan di daerah Surabaya. Surabaya merupakan kota yang mempunyai curah hujan rata-rata 300-400mm. Banyak daerah di Surabaya yang terkena dampak banjir dikarenakan tingginya curah hujan tersebut. Untuk menanggulangi hal tersebut, perlu adanya prediksi yang tepat akan terjadinya curah hujan yang tidak konsisten. Data yang tidak konsisten atau banyak nilai 0 (nol)-nya dapat dikatakan sebagai intermittent data. Sebelum melakukan penggabungan metode ARIMA dan GMDH, dilakukan pengelolahan data dengan menggunakan metode Bootsrap. Bootstrap merupakan salah satu metode dalam melakukan transformasi data untuk data intermiten. Metode ini mampu menghasilkan error yang kecil dibandingkan dengan metode-metode lain. beberapa model yaitu model ARIMA,ARIMA-ARCH, GMDH, dan Hybrid ARIMA-GMDH. Dengan memperhitungkan Symmetric Absolute Mean Percentage Error (SMAPE) pada masing-masing model didapatkan model terbaik yakni model Hybrid ARIMA-GMDH. Dengan data input hasil residu peramalan ARIMA dengan data bootstrap dihasilkan SMAPE data training sebesar 0,0322% dan data testing sebesar 0.0138%. Dibandingkan dengan SMAPE data testing model lain yakni ARIMA sebesar 0.0287%, ARCH sebesar 0.0391%, dan GMDH sebesar 0.0429%.
Hasil peramalan ini diharapkan dapat membantu pihak terkait dalam mempersiapkan langkah antisipasi dari kejadian yang dapat ditimbulkan saat curah hujan tinggi.=================== Recently studies about forecasting using hybrid ARIMA-GMDH method has performed by Shabri and Samsudin resulting an effective forecast using 114 Canadian Lynx data as observational data. The result of Error within hybrid ARIMA-GMDH generated tends to be smaller than individual method like ARIMA or Artifical Neural Network (ANN) method.
This research is trying to apply the same method to intermittent time series data like rainfall in Surabaya areas. City of Surabaya has 300-400mm average of rainfall. Many areas in Surabaya are affected by this high rainfall caused floods. A solutions of this problems, we should have a precise predictions of that incosistent rainfall. Incosistent data or many zero values can be refered as intermittent data. Before we perform hybrid ARIMA-GMDH method, the data should be managed by Bootstraping Method. Bootstrap is the one methods of transforming data for intermittent data. This method has capability to generating small Errors compared to other methods. This research tested several models of ARIMA, ARIMA-ARCH, GMDH, and Hybrid ARIMA-GMDH models. Using Symmetric Absolute Mean Percentage Error (SMAPE) the error of each models has calculated, Hybrid ARIMA-GMDH have selected as best model. With input data of forecasting residu of ARIMA with bootstrap data resulting error of data training 0.0322% and data testing 0.0138%. This error compared with SMAPE other testing data of each models like ARIMA is 0.0287%, ARCH is 0.0391%, and GMDH is 0.429%.
The forecasting result of this research maybe can help another concerned parties in anticipating some emergency events caused by the high rainfall.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Peramalan, ARIMA, Group Method of Data Handling (GMDH), Data Intermiten, Bootstrap, Curah Hujan, Forecasting, ARIMA, Group Method of Data Handling (GMDH), Intermittent Data, Rainfall |
| Subjects: | S Agriculture > S Agriculture (General) > S600.7.R35 Rain and rainfall T Technology > T Technology (General) > T174 Technological forecasting |
| Divisions: | Faculty of Information Technology > Information System > 57201-(S1) Undergraduate Thesis |
| Depositing User: | MAULITA ARUMNINGTYAS |
| Date Deposited: | 11 Oct 2018 09:10 |
| Last Modified: | 12 Mar 2021 05:23 |
| URI: | http://repository.its.ac.id/id/eprint/52611 |
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