Rahmadina, Denise Sonia (2020) Penentuan Rekomendasi Tujuan Wisata di Indonesia dari Data Tidak Terstruktur dengan Named Entity Recognition, Metode Clustering K-Means dan K-Nearest Neighbor. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pariwisata merupakan suatu varian yang kompleks mencakup serangkaian jenis operasi (transaksi, aktivitas atau peristiwa di pasar pariwisata) seperti pencarian web, kunjungan halaman web, online pemesanan & pembelian, dll. Sehingga menghasilkan data dengan jumlah yang signifikan untuk dilakukan proses pengolahan data untuk membantu para wisatawan dalam menentukan tujuan wisata yang diinginkan.
Dalam tugas akhir ini, dilakukan pembangunan model dengan Named Entity Recognition (NER), Part-of-speech (POS) Tagger, dan Rule Based Matching untuk mendeteksi entitas untuk membantu menentukan tujuan rekomendasi objek wisata di Indonesia. Selanjutnya pada tugas akhir ini, dilakukan clustering dan klasifikasi data dengan metode K-Means dan K-Nearest Neighbor untuk membagi tujuan wisata ke dalam kategori dan sesuai lokasi. Dari hasil evaluasi, didapatkan bahwa model Named Entity Recognition (NER) memiliki akurasi 99,7% dalam melabeli data, hasil clustering dengan K-Means menghasilkan 10 cluster data dengan akurasi 85% dan hasil klasifikasi dengan K-Nearest Neighbor dengan akurasi 90,1%.
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Tourism is a complex variant that includes a series of types of operations (transactions, activities or events in the tourism market) such as web search, web page visits, online ordering & purchasing, etc. So as to produce a significant amount of data to do the data processing to help tourists in determining the desired tourist destination. In this final project, a model construction with Named Entity Recognition (NER), Part-of-speech (POS) Tagger, and Rule Based Matching is carried out to detect entities to help determine the destination of tourist attraction recommendations in Indonesia. Furthermore, in this final project, clustering and data classification is carried out using the K-Means and K-Nearest Neighbor methods to divide tourist destinations into categories and according to location. From the evaluation results, it was found that the Named Entity Recognition (NER) model has an accuracy of 99.7% in labeling the data, the results of clustering with K-Means produce 10 data clusters with an accuracy of 85% and the results of the classification with K-Nearest Neighbor with 90.1 %.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSIf 005.3 Rah p-1 2020 |
| Uncontrolled Keywords: | Pariwisata, Named Entity Recognition (NER), Clustering, Klasifikasi. |
| Subjects: | Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Denise Sonia Rahmadina |
| Date Deposited: | 19 Apr 2024 08:47 |
| Last Modified: | 19 Apr 2024 08:47 |
| URI: | http://repository.its.ac.id/id/eprint/73644 |
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