Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection
This study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2021-08-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/791 |
| _version_ | 1797850466770485248 |
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| author | Annahl Riadi Marniyati Husain Botutihe |
| author_facet | Annahl Riadi Marniyati Husain Botutihe |
| author_sort | Annahl Riadi |
| collection | DOAJ |
| description | This study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta beach. The government needs to know the level of visitor satisfaction to attempt to improve and develop the Pohon Cinta beach tourist attraction. Thus, to solve the problem above, a method that can help predict visitor satisfaction is needed. This study aims to measure visitor satisfaction through predictions using the Backpropagation algorithm and PSO feature selection to assist the government in developing tourism potential in Pohuwato Regency. The method used is the backpropagation algorithm for prediction and Particle Swarm Optimization which is considered effective in overcoming optimization problems. This algorithm is considered capable of solving problems in the backpropagation algorithm. The accuracy value of the backpropagation algorithm model is 84.67%, the accuracy value of the PSO-based backpropagation algorithm model is 85.00%, and the difference in accuracy is 0.33. The results of the application of the Backpropagation algorithm and Particle Swarm Optimization can increase the predictive accuracy value of visitor satisfaction at the Cinta Tree Beach tourist attraction. |
| first_indexed | 2024-04-09T19:00:43Z |
| format | Article |
| id | doaj.art-daf811d2d3fb4b9496b273d0229b10e4 |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-09T19:00:43Z |
| publishDate | 2021-08-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-daf811d2d3fb4b9496b273d0229b10e42023-04-08T08:20:09ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792021-08-0113211712410.33096/ilkom.v13i2.791.117-124279Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selectionAnnahl Riadi0Marniyati Husain Botutihe1Universitas PohuwatoUniversitas PohuwatoThis study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta beach. The government needs to know the level of visitor satisfaction to attempt to improve and develop the Pohon Cinta beach tourist attraction. Thus, to solve the problem above, a method that can help predict visitor satisfaction is needed. This study aims to measure visitor satisfaction through predictions using the Backpropagation algorithm and PSO feature selection to assist the government in developing tourism potential in Pohuwato Regency. The method used is the backpropagation algorithm for prediction and Particle Swarm Optimization which is considered effective in overcoming optimization problems. This algorithm is considered capable of solving problems in the backpropagation algorithm. The accuracy value of the backpropagation algorithm model is 84.67%, the accuracy value of the PSO-based backpropagation algorithm model is 85.00%, and the difference in accuracy is 0.33. The results of the application of the Backpropagation algorithm and Particle Swarm Optimization can increase the predictive accuracy value of visitor satisfaction at the Cinta Tree Beach tourist attraction.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/791predictionvisitorstourbackpropagationpso |
| spellingShingle | Annahl Riadi Marniyati Husain Botutihe Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection Ilkom Jurnal Ilmiah prediction visitors tour backpropagation pso |
| title | Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection |
| title_full | Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection |
| title_fullStr | Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection |
| title_full_unstemmed | Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection |
| title_short | Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection |
| title_sort | visitor satisfaction prediction of the pantai pohon cinta beach tourism using the backpropagation algorithm with particle swarm optimization feature selection |
| topic | prediction visitors tour backpropagation pso |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/791 |
| work_keys_str_mv | AT annahlriadi visitorsatisfactionpredictionofthepantaipohoncintabeachtourismusingthebackpropagationalgorithmwithparticleswarmoptimizationfeatureselection AT marniyatihusainbotutihe visitorsatisfactionpredictionofthepantaipohoncintabeachtourismusingthebackpropagationalgorithmwithparticleswarmoptimizationfeatureselection |