Kombinasi SOM-RBF untuk prediksi drought code berdasarkan data curah hujan dan suhu udara

Combination of SOM-RBF for drought code prediction using rainfall and air temperature data

*Dwi Marisa Midyanti  -  Department of Computer Engineering, Universitas Tanjungpura, Indonesia
Received: 31 Jan 2019; Revised: 7 Nov 2019; Accepted: 18 Nov 2019; Published: 31 Jan 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Original Research Articles
Language: ID
Statistics: 213 38
Abstract
This study aims to predict Drought Code (DC) in Kabupaten Kubu Raya using a combination of SOM-RBF. The final weight value of SOM was used as a center on the RBF network. The input data variables are rainfall data and air temperature data for three days with three binary outputs to predict DC values. This study also observed the effect of the number of neurons, learning rates, and the number of iterations on the results of the SOM-RBF network training. The smallest MSE of training result from the SOM-RBF network was 0.159933 using 65 neurons in the hidden layer, learning rate 0.007, and epoch 45000. The detection accuracy of SOM-RBF was 91.34 % from 245 test data.
Keywords: drought code prediction; self organizing map; radial basis function

Article Metrics:

  1. -, “Laporan hasil pemantauan sumberdaya alam dan lingkungan serta mitigasi bencana alam berdasarkan data satelit penginderaan jauh,” Lembaga Penerbangan dan Antariksa Nasional Indonesia, 2017. [Online]. Available: http://pusfatja.lapan.go.id/index.php/publikasi
  2. Y. Yang, M. Uddstrom, G. Pearce, and M. Revell, “Reformulation of the drought code in the canadian fire weather index system implemented in New Zealand,” Journal of Applied Meteorology and Climatology, vol. 54, pp. 1523-1537, 2015. doi: 10.1175/JAMC-D-14-0090.1
  3. S. Haykin, Neural Networks and Learning Machines, 3nd Edition. New Jersey: Pearson Education, 2009.
  4. R. Hidayati, D. M. Midyanti, and S. Bahri, “Klasifikasi bibit tanaman lahan gambut berdasarkan bentuk daun menggunakan metode radial basis function (RBF),” Semnasteknomedia Online, vol. 6, no. 1, pp. 12-18, 2018.
  5. A. C. D. de Souza and M. A. C. Fernandes, “Parallel fixed point implementation of a radial basis function network in an FPGA,” Sensors (Basel), vol. 14, no. 10, pp. 18223–18243, 2014. doi: 10.3390/s141018223
  6. U. M. Tukur and S. M. Shamsuddin, “Radial basis function network learning with modified backpropagation algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 13, no. 2, pp. 369-378, 2015.
  7. D. M. Alemayehu, A. D. Mengistu, and S. G. Mengistu, “Computer vision for ethiopian agricultural crop pest identification,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 3, no. 1, pp. 209-214, 2016.
  8. A.D. Cahyani, B. K. Khotimah, and R. T. Rizkillah, “Perbandingan metode SOM (self organizing map) dengan pembobotan berbasis RBF (radial basis function),” Jurnal Teknologi Technoscientia, vol.7, no.1, pp. 85-92, 2014.
  9. F. J. M. Lopez, J. A. T. Arriaza, S. M. Puertas, and M. M. P. Lopez, “Multilevel neuronal architecture to resolve classification problems with large training sets: Parallelization of the training process,” Journal of Computational Science, vol. 16, pp. 59-64, 2016. doi: 10.1016/j.jocs.2016.04.002
  10. H. K. Hommod and T. K. Jebur, “Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network,” Journal of Physics: Conference Series, vol. 1003, pp. 1-11, 2018. doi: 10.1088/1742-6596/1003/1/012040
  11. R. H. Julia, N. Nikentari, and N. Hayaty, “Penerapan self organizing maps (SOM) dan radial basis function (RBF) untuk memprediksi kecepatan angin di perairan kota Tanjungpinang,”, Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan, vol. 7, no. 2, pp. 102-107, 2018. doi: 10.31629/sustainable.v7i2.627
  12. S. Mohammadi and F. Amiri, “An efficient hybrid self-learning intrusion detection system based on neural networks,” International Journal of Computational Intelligence and Applications, vol. 18, no. 1, pp. 1950001, 2019. doi: 10.1142/S1469026819500019
  13. A. H. Osman and A. A. Alzahrani, “New approach for automated epileptic disease diagnosis using an integrated self-organization map and radial basis function neural network algorithm,” IEEE Access, vol. 7, pp. 4741-4747, 2018. doi: 10.1109/ACCESS.2018.2886608
  14. M. R. Noviansyah, T. Rismawan, and D. M. Midyanti, “Penerapan data mining menggunakan metode k-nearest neighbor untuk klasifikasi indeks cuaca kebakaran berdasarkan data AWS (automatic weather station) (studi kasus : kabupaten Kubu Raya),” Jurnal Coding, Sistem Komputer Untan, vol. 6, no. 2, pp. 48-56, 2018.
  15. E. Supartini et al., Membangun Kesadaran, Kewaspadaan dan Kesiapsiagaan Dalam Menghadapi Bencana. Jakarta: Badan Nasional Penanggulanan Bencana, 2017.
  16. T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biological Cybernetics, vol. 43, no. 1, pp. 59-69, 1982. doi: 10.1007/bf00337288

No citation recorded.