Increasing Prediction Accuracy with the Backpropagation Algorithm (Case Study: Pematangsiantar City Rainfall)

Yogi Prayoga(1*), Dedy Hartama(2), Jalaluddin Jalaluddin(3), Sumarno Sumarno(4), Zulaini Masuro Nasution(5),

(1) STIKOM Tunas Bangsa
(2) STIKOM Tunas Bangsa
(3) STIKOM Tunas Bangsa
(4) STIKOM Tunas Bangsa
(5) STIKOM Tunas Bangsa
(*) Corresponding Author

Abstract


The more advanced science and technology from various disciplines, currently rainfall can be predicted by carrying out various empirical approaches, one of which is by using Artificial Neural Networks (ANN). This study aims to apply ANN with backpropogation algorithm in predicting rainfall. The research data used is BPS data of the transfer city. The results of the study state that of the 6 models (4-5-1, 4-10-1, 4-25-1, 4-5-10-1, 4-5-25-1 and 4-5-50-1) architecture that was trained and tested using Matlab 6.1 application software, the results showed that the 4-5-25-1 architectural model was the best model for making predictions with 75% truth accuracy, Training MSE 0.001004582, Testing MSE 0.021882712 and Epoch 59,076 . It is expected that research can provide input to the government, especially BMKG Pematangsiantar city in predicting Rainfall based on computer science so as to improve the quality of services in the fields of Meteorology, Climatology, Air Quality and Geophysics in accordance with applicable laws and regulations.


Full Text:

PDF

References


A. P. Windarto, “Penerapan Datamining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering Method,” Techno.Com, vol. 16, no. 4, pp. 348–357, 2017.

A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” Int. J. Artif. Intell. Res., vol. 1, no. 2, pp. 26–33, 2017.

A. P. Windarto, “Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering,” Techno.COM, vol. 16, no. 4, pp. 348–357, 2017.

D. Nabila Batubara, “Analisis Metode K-MEANS Pada Pengelompokan Keberadaan Area Resapan Air Menurut Provinsi,” Semin. Nas. Sains Teknol. Inf., pp. 345 – 349, Jul. 2019.

C. Astria, “Metode K-Means Pada Pengelompokan Wilayah Pendistribusian Listrik,” Semin. Nas. Sains Teknol. Inf., pp. 306 – 312, Jul. 2019.

T. Budiharjo, Soemartono, T., Windarto, A.P., Herawan, “Predicting tuition fee payment problem using backpropagation neural network model,” Int. J. Adv. Sci. Technol., 2018.

T. Budiharjo, Soemartono, T., Windarto, A.P., Herawan, “Predicting school participation in indonesia using back-propagation algorithm model,” Int. J. Control Autom., 2018.

A. P. Windarto, M. R. Lubis, and Solikhun, “Implementasi JST Pada Prediksi Total Laba Rugi Komprehensif Bank Umum Konvensional Dengan Backpropagation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, pp. 411–418, 2018.

A. P. Windarto, M. R. Lubis, and Solikhun, “Model Arsitektur Neural Network Dengan Backpropogation Pada Prediksi Total Laba Rugi Komprehensif Bank Umum Konvensional,” Kumpul. J. Ilmu Komput., vol. 5, no. 2, pp. 147–158, 2018.

A. P. Windarto, L. S. Dewi, and D. Hartama, “Implementation of Artificial Intelligence in Predicting the Value of Indonesian Oil and Gas Exports With BP Algorithm,” Int. J. Recent Trends Eng. Res., vol. 3, no. 10, pp. 1–12, 2017.

Sumijan, A. P. Windarto, A. Muhammad, and Budiharjo, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject,” Int. J. Softw. Eng. Its Appl., vol. 10, no. 10, pp. 189–204, 2016.

S. Putra Siregar and A. Wanto, “Analysis Accuracy of Artificial Neural Network Using Backpropagation Algorithm In Predicting Process (Forecasting),” Int. J. Inf. Syst. Technol., vol. 1, no. 1, pp. 34–42, 2017.

Solikhun, A. P. Windarto, Handrizal, and M.Fauzan, “Jaringan Saraf Tiruan Dalam Memprediksi Sukuk Negara Ritel Berdasarkan Kelompok Profesi Dengan Backpropogation Dalam Mendorong Laju Pertumbuhan Ekonomi,” Kumpul. J. Ilmu Komput., vol. 4, no. 2, pp. 184–197, 2017.

B. Febriadi, Z. Zamzami, Y. Yunefri, and A. Wanto, “Bipolar function in backpropagation algorithm in predicting Indonesia’s coal exports by major destination countries,” IOP Conf. Ser. Mater. Sci. Eng., vol. 420, no. 12089, pp. 1–9, 2018.

B. Fachri, A. P. Windarto, and I. Parinduri, “Penerapan Backpropagation dan Analisis Sensitivitas pada Prediksi Indikator Terpenting Perusahaan Listrik,” J. Edukasi dan Penelit. Inform., vol. 5, no. 2, pp. 202–208, 2019.




DOI: https://doi.org/10.30645/ijistech.v3i1.27

Refbacks

  • There are currently no refbacks.







Jumlah Kunjungan:

View My Stats

Published Papers Indexed/Abstracted By: