Perbandingan Akurasi ARIMA dan Backpropogation dalam Memprediksi Intensitas Curah Hujan Kota Makassar
Keywords:
Rainfall predictionAbstract
Climatic conditions tend to change irregularly in a random period of time, so that unavoidable deviations can occur such as high intensity of rainfall which causes flooding. This has an impact on people who feel disadvantaged because floods can paralyze economic activities, threaten public safety, hamper transportation and the emergence of other negative impacts, especially in Makassar City. Therefore, it is important to do a rainfall forecast to find out information about rainfall in Makassar City so that in this study a seasonal rainfall prediction was carried out by applying the Auto Regressive Integrated Moving Average (ARIMA) and Backpropogation methods. The results of the ARIMA method with the model produced the smallest error of 174,9038 while the backpropogation method with the 12-18-18-18-1 model gave the smallest error of 199, 0376. This shows that the ARIMA method has a better level of accuracy in forecasting rainfall compared to the backpropogation method.
Downloads
References
Jhonson Arizona Saragih, I., Rumahorbo, I., Yudistira, R., Dedi Sucahyono, dan, Meteorologi Klimatologi dan Geofisika, B., Meteorologi Kualanamu, S., Serdang, D., Tinggi Meteorologi Klimatologi dan Geofisika, S., & Selatan, T. (2020). Prediksi Curah Hujan Bulanan Di Deli Serdang Menggunakan Persamaan Regresi Dengan Prediktor Data Suhu Dan Kelembapan Udara. Jurnal Meteorologi Klimatologi Dan Geofisika, 7(2), 6–14. http://bmkgsoft.database.bmkg.go.id
Susanto, Y., & Ulama, B. S. S. (2016). Pemodelan Curah Hujan dengan Pendekatan Model ARIMA , Feed Forward Neural Network dan Hybrid (ARIMA-NN) di Banyuwangi. Jurnal Sains Dan Seni ITS, 5(2), D-145-D-150. http://ejurnal.its.ac.id/index.php/sains_seni/article/viewFile/16409/3032
Susilokarti, D., Arif, S. S., Susanto, S., & Sutiarso, L. (2015). Studi Komparasi Prediksi Curah Hujan Metode Fast Fourier Transformation (Fft), Autoregressive Integrated Moving Average (Arima) Dan Artificial Neural Network (Ann). Jurnal Agritech, 35(02), 241. https://doi.org/10.22146/agritech.9412
Utomo, M. C. C., Mahmudy, W. F., & Anam, S. (2017). Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur. Jurnal Teknologi Informasi Dan Ilmu Komputer, 4(3), 160–167. https://doi.org/10.25126/jtiik.201743299
Kusumadewi, S. (2003). Artificial Intelligence: Teknik dan Aplikasinya. Edisi Pertama, Cetakan Pertama, Graha Ilmu, Yogyakarta
Bey, A. (2003). Prospek model ARIMA sebagai alat prediksi curah hujan Stasiun Karawang sebagai kasus dalam Ratag (Ed). Prediksi cuaca dan iklim. Prosiding Temu Ilmiah Nasional di Lembaga Penerbangan dan Antariksa Nasional, Bandung 2002.
Oktaviani, Cici dan Afdal, 2013, Prediksi Curah Hujan Bulanan Menggunakan Jaringan Syaraf Tiruan dengan Beberapa Fungsi Pelatihan Backpropagation, Jurnal Fisika Unand, Vol. 2, No. 4, Oktober.
PATRO, S., SAHU, K.K., 2015. Normalization: A Preprocessing Stage. ArXiv Prepr. ArXiv150306462.
FAUSETT, L.V., 1994. Fundamentals Of Neural Network: Architecture, Algorithms, and Applications, International Editions. ed. Prentice-Hall.
Additional Files
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Abstract Viewed = 219 times
|
PDF downloaded = 0 times
