Application of Multi-Input Transfer Function to Rainfall Data in Maluku

Authors

  • Sela Putri Indriati Universitas Sebelas Maret
  • Dewi Retno Sari Saputro Universitas Sebelas Maret
  • Purnami Widyaningsih Universitas Sebelas Maret

DOI:

https://doi.org/10.37010/nuc.v5i1.1551

Keywords:

rainfall, maluku, transfer function of multi-input

Abstract

If the intensity of rainfall is too high, this can cause flooding in Maluku. The intensity of rainfall in Maluku is also strongly influenced by sea surface temperature conditions in the waters of the Indian Ocean and Pacific Ocean. In mid-2022 and 2023, floods in Maluku caused residents' houses to be submerged, damaged road access, and broken bridges. In this study, a multi-input transfer function was applied to rainfall data in Maluku. This research data is secondary data. The data used consists of 73 data from August 2017 to August 2023. Transfer function can determine the relationship and influence between input and output in a period of time. Multi-input transfer functions are used to understand how inputs contribute to outputs. Rainfall acts as the output and temperature, air humidity, and wind speed act as the inputs. Based on the results of the study, the inputs affect the output so that the multi-input transfer function formed produces a MAPE value of 16.42%. This value can be concluded if the function is accurate

References

Aprilia, M., & Desviona, N. (2021). The Implementation of a Filter Kalman Method Forecasting Rainfall Obtained Through Model ARIMA in Kota Jambi. NUCLEUS, 2(2): 69–77. https://doi.org/10.37010/nuc.v2i2.607

Elake, A. Y., Talahatu, M., & Nanlohy, P. (2018). Korelasi Multivariabel Enso, Monsun, dan Dipole Mode terhadap Variabilitas Curah Hujan di Maluku. Barekeng: Jurnal Ilmu Matematika dan Terapan, 12(1): 7-16.

Fathurahman, M. (2009). Pemodelan Fungsi Transfer Multi Input Konsep Dasar Time Series. Jurnal Informatika Mulawarman, 4(2): 1-3.

Indonesia Baik. (2023, Oktober 13). Provinsi Paling Sering Dilanda Hujan. Retrieved from Indonesia Baik: https://indonesiabaik.id/infografis/provinsipaling-sering-dilanda-hujan,

Makridakis, S., Wheelwright, S. C., & McGee, V. E. (1993). Forecasting: Methods and Applications. New York: 1993.

Pankratz, A. (1991). Forecasting with Dynamic Regression Models. New York: John Wiley & Sons Inc.

Siregar, N. A. (2022). Peramalan Curah Hujan di Kota Medan Menggunakan Metode Support Vector Regression. Journal of Informatics Data Science, 1(1): 1-5.

Wei, W. W. (2006). Time Series Analysis (Second Edition). New York: Pearson Education.

Wilson, E. M. (1993). Hidrologi Teknik (Edisi 4). Jakarta: Erlangga.

Published

2024-06-03

How to Cite

Indriati, S. P., Saputro, D. R. S., & Widyaningsih, P. (2024). Application of Multi-Input Transfer Function to Rainfall Data in Maluku. NUCLEUS, 5(1), 55–59. https://doi.org/10.37010/nuc.v5i1.1551

Issue

Section

Artikel
Abstract viewed = 32 times

Most read articles by the same author(s)