Optimasi Neural Network Dengan Menggunakan Algoritma Genetika Untuk Prediksi Jumlah Kunjungan Wisatawan

  • Fatimatuzzahra Fatimatuzzahra Universitas Bumigora
  • Rifqi Hammad Universitas Bumigora
  • Ahmad Zuli Amrullah Universitas Bumigora
  • Pahrul Irfan Universitas Bumigora
Keywords: Prediction, Optimization, Neural Network, Genetic Algorithm

Abstract

West Nusa Tenggara is one of the tourist attractions in Indonesia which has a certain attraction for tourists. With the increase in tourism in NTB, it is necessary to make adequate efforts to maintain tourist objects and attractions. In an effort to maintain a tourist attraction, the NTB provincial tourism office needs to analyze and predict the arrival of local and international tourists. The current analysis and prediction process is still being carried out by collecting data from each tourist attraction entrance. The processed data produces predictions of tourist arrivals, both local and international, where the data processing process takes a long time and requires high human resources. To overcome these problems, it is done by applying computational predictions. Computational predictions can minimize the prediction time and human resources required. The method used is a neural network algorithm with optimized parameters using a genetic algorithm. The optimized parameters are the hidden layer, the number of neurons in the input layer, momentum and others. The data used is time series data from 1997 to 2018. From the neural network experiment, the parameters of the number of neurons in the input layer xt-7 are determined, the number of neurons in the hidden layer 10, the training cycle value is 400, the learning rate value is 0.3 and the momentum value is 0.2. From the experiment, the RMSE value of 0.050 was obtained. While the RMSE value for the neural network algorithm parameters optimized using the genetic algorithm is 0.044. Because of this, it can be stated genetic algorithm with neural network can be used to determine the hidden layer and the number of hidden nodes, the right features, momentum, initialize, and optimize the weight of the neural network. So that the application of the genetic algorithm to optimize the parameter values of the neural network algorithm is better than the application of the neural network algorithm without optimization.

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References

M. D. Nguyen et al., “Soft-computing techniques for prediction of soils consolidation coefficient,” Catena, vol. 195, 2020, doi: 10.1016/j.catena.2020.104802.

A. Dinesh Kumar, R. Pandi Selvam, and K. Sathesh Kumar, “Review on prediction algorithms in educational data mining,” Int. J. Pure Appl. Math., vol. 118, no. Special Issue 8, 2018.

E. Frank, M. A. Hall, and I. H. Witten, “The WEKA Workbench Data Mining: Practical Machine Learning Tools and Techniques,” Morgan Kaufmann, Fourth Ed., p. 128, 2016.

I. Ghalehkhondabi, E. Ardjmand, W. A. Young, and G. R. Weckman, “A review of demand forecasting models and methodological developments within tourism and passenger transportation industry,” J. Tour. Futur., vol. 5, no. 1, pp. 75–93, 2019.

E. Işığıçok, R. Öz, and S. Tarkun, “Forecasting and Technical Comparison of Inflation in Turkey With Box_Jenkins (ARIMA) Models and the Artificial Neural Network,” Int. J. Energy Optim. Eng., vol. 9, no. 4, pp. 84–103, 2020.

T. Šestanovi´c and J. Arneri´c, “Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?,” Mathematics, vol. 9, no. 2486, 2021.

A. K. Petrova, “Application of Neural Networks in the HR Tasks,” 2021.

A. F. Atiya, “Bankruptcy prediction for credit risk using neural networks: A survey and new results,” IEEE Trans. Neural Networks, vol. 12, no. 4, 2001, doi: 10.1109/72.935101.

G. A. Vasilakis, K. A. Theofilatos, E. F. Georgopoulos, A. Karathanasopoulos, and S. D. Likothanassi, “A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading,” Comput. Econ., vol. 42, pp. 415–431, 2013.

D. A. Suryaningrum, D. E. Ratnawati, and B. D. Setiawan, “Prediksi Waktu Panen Tebu Menggunakan Gabungan Metode Backpropagation dan Algoritma Genetika,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 1, no. 11, 2017.

D. I. Puspitasari, “Penerapan Data Mining Menggunakan Perbandingan Algoritma Greedy Dengan Algoritma Genetika Pada Prediksi Rentet Waktu Harga Crude Palm Oil,” Elinvo (Electronics, Informatics, Vocat. Educ., vol. 2, no. 1, 2017, doi: 10.21831/elinvo.v2i1.13033.

D. Setiawan, R. N. Putri, and R. Suryanita, “Implementasi Algoritma Genetika Untuk Prediksi Penyakit Autoimun,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 4, no. 1, 2019, doi: 10.36341/rabit.v4i1.595.

S. A. Salimu and Y. Yunus, “Prediksi Tingkat Kedatangan Wisatawan Asing Menggunakan Metode Backpropagation (Studi Kasus: Kepulauan Mentawai),” J. Inform. Ekon. Bisnis, 2020, doi: 10.37034/infeb.v2i4.50.

S. S. M. S. H.Lalu Moh Fauzal, Statistik Pariwisata Provinsi Nusa Tenggara Barat, 2018th ed. Mataram: Pesona Lombok Sumbawa, 2017.

M. Nanja and P. Purwanto, “Metode K-Nearest Neighbor Berbasis Forward Selection Untuk Prediksi Harga Komoditi Lada,” Pseudocode, vol. 2, no. 1, 2015, doi: 10.33369/pseudocode.2.1.53-64.

W. Wang and Y. Lu, “Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model,” in IOP Conference Series: Materials Science and Engineering, 2018, vol. 324, no. 1, doi: 10.1088/1757-899X/324/1/012049.

Published
2022-02-20
How to Cite
Fatimatuzzahra, F., Hammad, R., Amrullah, A. Z., & Irfan, P. (2022). Optimasi Neural Network Dengan Menggunakan Algoritma Genetika Untuk Prediksi Jumlah Kunjungan Wisatawan. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 3(4), 227-235. https://doi.org/10.35746/jtim.v3i4.190
Section
Articles

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