DOI: https://doi.org/10.14710/jtsiskom.2021.13847

Komparasi model support vector machine dan backpropagation dalam peramalan jumlah wisatawan mancanegara di provinsi Bali

Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province

Department of Information Technology, Universitas Udayana, Bukit Jimbaran, Badung, Bali 80361, Indonesia

Received: 28 Jul 2020; Revised: 7 Dec 2020; Accepted: 26 Feb 2021; Available online: 20 Apr 2021; Published: 30 Apr 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.
Fulltext View|Download Email colleagues
Keywords: tourism; prediction; number of tourist; SVM; kernel; Backpropagation
Funding: Universitas Udayana

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Sistem Pakar Untuk Identifikasi Dan Alternatif Solusi Terhadap Permasalahan Yang Dihadapi Peserta Didik Sekolah Menengah Menggunakan Rule-Based Machine Learning SVM optimization using a grid search algorithm to identify robusta coffee bean images based on circularity and eccentricity Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning Hand motion strength forecasting using Extreme Learning Machine for post-stroke rehabilitation Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks More related articles
Most cited articles
Parameter tuning in KNN for software defect prediction: an empirical analysis Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi Pembuatan Aplikasi “Warung Keluarga” Sebagai Aplikasi E-Commerce Berbasis Web dan Mobile Sistem Monitoring Digital Penggunaan dan Kualitas Kekeruhan Air PDAM Berbasis Mikrokontroler ATMega328 Menggunakan Sensor Aliran Air dan Sensor Fotodiode Pembuatan Aplikasi Panduan Gizi Seimbang Berbasis Android Dengan Menggunakan Metode Backward Chaining More cited articles
  1. -, “Travel and tourism: economic impact reports,” World Travel and Tourism Council, 2019. [Online]. Available: https://wttc.org/Research/ Economic-Impact
  2. -, “Jumlah wisatawan mancanegara bulanan ke Bali, ,” Badan Pusat Statistik Bali, 2018. [Online]. Available: https://bali.bps.go.id/statictable/2018/02/09/ 21/jumlah-wisatawan-asing-ke-bali-menurut-bulan-1982-2020.html
  3. R. H. Kusumodestoni and S. Sarwido, “Komparasi model support vector machines (SVM) dan neural network untuk mengetahui tingkat akurasi prediksi tertinggi harga saham,” Jurnal Informatika Upgris, vol. 3, no. 1, pp. 1-9, 2017. doi: 10.26877/jiu.v3i1.1536
  4. H. Haviluddin and R. Alfred, “A genetic-based backpropagation neural network for forecasting in time-series data,” in International Conference on Science in Information Technology, Yogyakarta, Indonesia, Oct. 2015, pp. 158–163. doi: 10.1109/ICSITech.2015.7407796
  5. L. Assaffat, “Analisis akurasi support vector machine dengan fungsi kernel gaussian rbf untuk prakiraan beban listrik harian sektor industri,” Momentum, vol. 11, no. 2, pp. 64–68, 2014
  6. A. Wanto, “Analisis prediksi indeks harga konsumen berdasarkan kelompok kesehatan dengan menggunakan metode backpropagation,” Jurnal Penelitian Teknik Informatika, vol. 2, no. 2, pp. 37–44, 2017
  7. M. F. Naufal, “Peramalan jumlah wisatawan mancanegara yang datang ke indonesia berdasarkan pintu masuk menggunakan metode support vector machine (SVM),” undergraduate thesis, Institut Teknologi Sepuluh November, Surabaya, Indonesia, 2017
  8. E. W. A. Mutmainnah, “Application of support vector machine (svm) methods on stock price forecasting of PT Telekomunikasi Indonesia,” in Seminar Nasional Pendidikan Sains dan Teknologi, Semarang, Indonesia, Oct. 2018, pp. 50–60
  9. T. Misriati, “Peramalan jumlah kunjungan wisatawan mancanegara ke Lombok menggunakan jaringan syaraf tiruan,” in Seminar Nasional Ilmu Pengetahuan dan Teknologi Nusa Mandiri, Jakarta, Indonesia, Dec. 2016, pp. 13–17
  10. V. Apriana and R. I. Handayani, “Prediksi beban listrik dengan menggunakan algoritma backpropagation dan support vector machine,” in Seminar Nasional Multidisiplin Ilmu, Jakarta, Indonesia, Jan. 2017, pp. 434-442
  11. Y. Sarvina, “Pemanfaatan software open source R untuk penelitian agroklimat,” Informatika Pertanian, vol. 26, no. 1, pp. 23–30, 2017. doi: 10.21082/ip.v26n1.2017.p23-30
  12. B. Hamner, M. Frasco, and E. Ledell, “Metrics: evaluation metrics for machine learning,” 2018. [Online]. Available: https://github.com/mfrasco/Metrics
  13. P. G. Pratiwi, I. K. G. D. Putra, and D. P. S. Putri, “Peramalan jumlah tersangka penyalahgunaan narkoba menggunakan metode multilayer perceptron,” Jurnal Ilmiah Menara Penelitian Akademika Teknologi Informasi, vol. 7, no. 2, pp. 143-150, 2019. doi: 10.24843/JIM.2019.v07.i02.p06
  14. A. K. Abbas, N. A. Al-haideri, and A. A. Bashikh, “Implementing artificial neural networks and support vector machines to predict lost circulation,” Egyptian Journal of Petroleum, vol. 28, no. 4, pp. 339–347, 2019. doi: 10.1016/j.ejpe.2019.06.006
  15. P. A. Octaviani, Y. Wilandari, and D. Ispriyanti,” Penerapan metode klasifikasi support vector machine (SVM) pada data akreditasi sekolah dasar (SD) di kabupaten Magelang,” Jurnal Gaussian, vol. 3, no. 4, pp. 811-820, 2014
  16. A. Dobin and T. R. Gingeras, “Optimizing RNA-Seq mapping with STAR,” in Methods Molecular Biology, vol. 1415, pp. 245-262, 2016. doi: 10.1007/978-1-4939-3572-7_13
  17. D. Karmiani, R. Kazi, A. Nambisan, A. Shah, and V. Kamble, “Comparison of predictive algorithms: backpropagation, SVM, LSTM and Kalman Filter for stock market,” in Amity International Conference on Artificial Intelligence, Dubai, United Arab Emirates, Feb. 2019, pp. 228–234. doi: 10.1109/AICAI.2019.8701258

Last update:

No citation recorded.

Last update: 2024-11-03 08:02:19

No citation recorded.