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
Malicious URLs detection using data streaming algorithms Data scaling performance on various machine learning algorithms to identify abalone sex Pandemic dynamics prediction in Java using the Moving Average and the Knowledge Growing System methods HSV image classification of ancient script on copper Kintamani inscriptions using GLRCM and SVM Android Application for Tasikmalaya City Tourism Guidance More related articles
Most cited articles
Pengembangan Teknologi Informasi Mobile Learning Universitas Diponegoro Berbasis Android Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms Pengembangan Permainan Edukatif Ragam Budaya Nusantara Berbasis Android Design of wireless sensor networks (WSN) to monitor temperature and humidity using nrf24l01 Application caching strategy based on in-memory using Redis server to accelerate relational data access 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: 2024-11-23 15:20:02

No citation recorded.