Prediksi Curah Hujan Bulanan Berdasrkan Parameter Cuaca Menggunakan Jaringan Saraf Tiruan Levenberg Marquardt

 (*)Setiyaris Setiyaris Mail (UIN Maulana Malik Ibrahim Malang, Malang, Indonesia)
 Mokhamad Amin Hariyadi (UIN Maulana Malik Ibrahim Malang, Malang, Indonesia)
 Cahyo Crysdian (UIN Maulana Malik Ibrahim Malang, Malang, Indonesia)

(*) Corresponding Author

Submitted: June 11, 2023; Published: July 23, 2023


Accurate prediction of rainfall is very important for warning services for hydrometeorological disasters or disasters caused by rain, so high accuracy is required in making predictions of rainfall. Artificial Neural Networks are becoming a trend in the field of computers because they provide the best accuracy in making predictions. Artificial neural networks are very powerful in recognizing data patterns to model and predict rainfall. The purpose of this research is to predict rainfall using the Levenberg Marquardt algorithm artificial neural network method. The data used for analysis are 120 data consisting of temperature, humidity, pressure, wind speed and solar radiation. To get accurate predictions, calculations are carried out by varying the amount of input and output data as well as varying the number of neurons in the hidden layer. The best performance of a model is measured from the value of MSE or Mean Square Error. The result shows that the network with a data composition of 90% input data, 10% output data and 25 neurons in the hidden layer is the best architecture with an MSE value of 0.029.


Predictions; Weather; Rainfall; Artificial Neural Networks; Levenberg Marquardt

Full Text:


Article Metrics

Abstract view : 655 times
PDF - 355 times


H. Abdul-Kader, M. Abd-Elsalam, and M. Mohamed, “Hybrid Machine Learning Model for Rainfall Forecasting,” J. Intell. Syst. Internet Things, vol. 1, no. 1, pp. 5–12, 2020, doi: 10.54216/jisiot.010101.

N. Z. Mohd Safar, A. A. Ramli, H. Mahdin, D. Ndzi, and K. M. N. Ku Khalif, “Rain prediction using fuzzy rule based system in north-west Malaysia,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 3, pp. 1564–1573, 2019, doi: 10.11591/ijeecs.v14.i3.pp1564-1573.

D. Shukla, V. Rajvir, and M. S. Patel, “Rainfall Prediction Using Neural Network,” Comput. Intell. Time Ser. Anal., pp. 127–141, 2018.

A. Chand and R. Nand, “Rainfall prediction using Artificial Neural network in the South Pacific region,” pp. 1–7, 2019.

M. E. Akiner, “Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach,” Adv. Meteorol., vol. 2021, no. X, 2021, doi: 10.1155/2021/5524611.

B. T. Pham et al., “Development of advanced artificial intelligence models for daily rainfall prediction,” Atmos. Res., vol. 237, no. November 2019, p. 104845, 2020, doi: 10.1016/j.atmosres.2020.104845.

A. Kala and S. G. Vaidyanathan, “Prediction of Rainfall using Artificial neural Network,” Proc. Int. Conf. Inven. Res. Comput. Appl. (ICIRCA 2018), vol. 5, no. 3, pp. 248–253, 2018.

S. Srivastava, N. Anand, S. Sharma, S. Dhar, and L. K. Sinha, “Monthly rainfall prediction using various machine learning algorithms for early warning of landslide occurrence,” 2020 Int. Conf. Emerg. Technol. INCET 2020, pp. 1–7, 2020, doi: 10.1109/INCET49848.2020.9154184.

F. R. Hashim, N. G. Nik Daud, K. A. Ahmad, J. Adnan, and Z. I. Rizman, “Prediction of Rainfall Based on Weather Parameter Prediction of Rainfall Based on Weather Parameter Using Artificial Neural Network,” J. Fundam. Appl. Sci., vol. 4, no. 1, pp. 9–10, 2018, [Online]. Available:

H. N. Nguyen et al., “Prediction of daily and monthly rainfall using a backpropagation neural Network,” J. Appl. Sci. Eng., vol. 24, no. 3, pp. 367–379, 2021, doi: 10.6180/jase.202106_24(3).0012.

G. I. Merdekawati and Ismail, “Prediksi Curah Hujan Di Jakarta Berbasis Algoritma Levenberg Marquardt,” J. Ilm. Inform. Komput., vol. 24, no. 2, pp. 116–128, 2019, doi: 10.35760/ik.2019.v24i2.2366.

A. N. Alfiyatin, W. F. Mahmudy, C. F. Ananda, and Y. P. Anggodo, “Penerapan Extreme Learning Machine (Elm) Untuk Peramalan Laju Inflasi Di Indonesia Implementation Extreme Learning Machine for Inflation Forecasting in Indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 2, pp. 179–186, 2018, doi: 10.25126/jtiik.20186900.

M. A. Bukhari, A. H., Sulaiman, M., Islam, S., Shoaib, M., Kumam, P., & Zahoor Raja, “bukhari2019.pdf,” Alexandria Eng. Journal., vol. 59, no. 1, pp. 101–116, 2019.

N. Amalya, “BULLETIN OF COMPUTER SCIENCE RESEARCH Algoritma Backpropagation Metode Levenberg Marquardt Dalam Memprediksi Penyakit Stroke,” vol. 3, no. 2, pp. 191–196, 2023, doi: 10.47065/bulletincsr.v3i2.229.

A. F. Zuhri et al., “Seminar Nasional Sains dan Teknologi Informasi (SENSASI) Optimasi Levenberg-Marquardt backpropagation dalam Mempercepat Pelatihan Backpropagation,” Semin. Nas. Sains dan Teknol. Inf., vol. 3, no. 1, pp. 627–630, 2021, [Online]. Available:

P. Malik, A. Gehlot, R. Singh, L. R. Gupta, and A. K. Thakur, “A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction with Real-Time Data,” Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 3183–3201, 2022, doi: 10.1007/s11831-021-09687-3.

H. Al Kautsar, “Model Fourier Untuk Prediksi Harga Saham Astrazeneca Menggunakan Algoritma Levenberg-Marquardt,” J. Tika, vol. 6, no. 02, pp. 40–50, 2021, doi: 10.51179/tika.v6i02.486.

X. Ying, “An Overview of Overfitting and its Solutions,” J. Phys. Conf. Ser., vol. 1168, no. 2, 2019, doi: 10.1088/1742-6596/1168/2/022022.

N. Mishra, H. K. Soni, S. Sharma, and A. K. Upadhyay, “Development and analysis of Artificial Neural Network models for rainfall prediction by using time-series data,” Int. J. Intell. Syst. Appl., vol. 10, no. 1, pp. 16–23, 2018, doi: 10.5815/ijisa.2018.01.03.

BMKG, “Masyarakat Indonesia Sadar Iklim dan Cuaca,” pp. 9–25, 2019.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Prediksi Curah Hujan Bulanan Berdasrkan Parameter Cuaca Menggunakan Jaringan Saraf Tiruan Levenberg Marquardt


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.