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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat Abdi Putra Journal of Software Engineering and Information System (SEIS) Journal of International Multidisciplinary Research
Wide Mulyana
Universitas Muhammadiyah Riau
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Public Health Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector Machine (SVM) Harun Mukhtar; Rahmad Gunawan; Amin Hariyanto; Syahril; Wide Mulyana
Jurnal CoSciTech (Computer Science and Information Technology) Vol 3 No 3 (2022): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v3i3.4211

Abstract

Tourism is one of the most promising ecosystems for economic sectors worldwide. A strong tourism sector directly contributes to the country's national income, fights unemployment, and improves the balance of payments. Tourism development can be seen from the increase in arrivals to a nation; based on data obtained from the UNWTO from 1995-2019, it has increased and decreased. The sudden increase and decrease in tourists will have positive and negative impacts. Forecasting is an activity to predict events that will occur in the future by taking data from the past. So this study will expect tourist arrivals to a country using the Support Vector Machine (SVM) method. SVM has properties about maximizing margins and kernel tricks to map nonlinear data. The results obtained in this study indicate that SVM Confidence is 86.3%, has a MAPE value of 56.00%, and an RMSE worth of 11126.36 from the total data of 53 countries. And forecasting is carried out in 5 countries with the highest tourist visits. The results obtained are excellent: SVM Confidence of 99.13%, a MAPE value of 2.78%, and an RMSE value of 2783.57.
Co-Authors Amin Hariyanto Arya Hakam Aryanto Aryanto Aryanto Asrianto, Rudy Doni Winarso Edo Arribe, Edo Gunawan, Rahmad Harun Mukhtar Nurul Alifa Putri Muslim Rangga Setiawan Risnal Diansyah Rizka Hafsari Setiawan Ardi Wijaya Setyawan Ardhi Wijaya Syahril Syahril Syahril