Fitriasari, Nisa'u (2019) Evaluasi Dampak Penggunaan Mobile Positioning Data pada Data Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia dengan Pendekatan Model Intervensi dan Machine Learning. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Mobile Positioning Data (MPD) merupakan sumber data baru yang digunakan oleh Badan Pusat Statistik (BPS) untuk menghitung jumlah kunjungan wisatawan mancanegara yang berkunjung ke Indonesia yang diterapkan mulai Oktober 2016. MPD memiliki kelemahan terkait over-under coverage perangkat seluler yang menjadikan kevalidan data dipertanyakan, sehingga pada penelitian ini dilakukan evaluasi penggunaan MPD terhadap jumlah kunjungan wisatawan mancanegara (wisman) dengan pendekatan model intervensi dan Deep Learning Neural Network (DLNN) serta dilakukan peramalan jumlah kunjungan wisman pada tahun 2019. Hasil analisis menunjukkan bahwa MPD memberi efek yang signifikan terhadap kenaikan jumlah kunjungan wisman secara nasional, wisman yang berasal dari Malaysia, dan wisman yang berasal dari Tiongkok. Model terbaik untuk peramalan jumlah kunjungan wisman secara umum dengan kriteria RMSE dan MAPE adalah model intervensi tanpa transformasi. Berdasarkan hasil peramalan, jumlah kunjungan wisman pada tahun 2019 diperkirakan mencapai angka 17 juta yang artinya masih di bawah target pemerintah yaitu 20 juta kunjungan dengan jumlah kunjungan terbanyak pada bulan Juli dan jumlah kunjungan paling sedikit pada bulan Mei. Sementara itu, dari aspek ekonomi, Produk Domestik Bruto (PDB) sektor penyediaan akomodasi dan transportasi, memiliki kenaikan yang sebanding dengan kenaikan data jumlah kunjungan wisman.
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Mobile Positioning Data (MPD) is a new data source used by the Badan Pusat Statistik (BPS) to calculate the number of inbound tourist visits to Indonesia which was implemented starting in October 2016. MPD has weaknesses related to over-under coverage of cellular devices that make the data are questioned, so that this study conduct an evaluation of the use of MPD on the number of inbound tourist visits with the intervention model approach and Deep Learning Neural Network (DLNN) and forecasting the number of inbound tourist visits in 2019. The analysis shows that MPD has an effect which is significant to the increase in the number of inbound tourists visiting of national, tourists from Malaysia, and tourists from China. The best model for forecasting the number of inbound tourist visits in general with RMSE and MAPE criteria is a model of intervention without transformation. Based on the results of forecasting, the number of inbound tourists visiting in 2019 is estimated to reach 17 million, which means it is still below the government's target of 20 million visits with the highest number of visits in July and the least number of visits in May. Meanwhile, from the aspect of economy, the Gross Domestic Product (GDP) in the sector of providing accommodation and transportation, has an increase that is comparable to the increase in data on the number of tourist visits.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSSt 519.55 Fit e-1 2019 3100019082253 |
| Uncontrolled Keywords: | DLNN, inbound tourist, intervention, MPD, GDP |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G155 Tourism H Social Sciences > H Social Sciences (General) > H61.4 Forecasting in the social sciences H Social Sciences > HA Statistics > HA30.3 Time-series analysis Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) Q Science > QA Mathematics > QA76.6 Computer programming. Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Nisa'u Fitriasari |
| Date Deposited: | 15 Dec 2025 04:17 |
| Last Modified: | 15 Dec 2025 04:17 |
| URI: | http://repository.its.ac.id/id/eprint/66193 |
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