Paulina, Wirdhayanti (2020) nalisis Sentimen Berbasis Aspek Ulasan Pelanggan Terhadap Kertanegara Premium Guest House Menggunakan Support Vector Machine. Sarjana thesis, Universitas Brawijaya.
Abstract
Saat ini, situs pemesanan perjalanan wisata atau Online Travel Agent (OTA) bukan hanya berfungsi untuk reservasi akomodasi wisata tetapi juga mempunyai peran baru sebagai media Electronic Word of Mouth (E-WOM). Kertanegara Premium Guest House adalah salah satu penginapan di Kota Malang yang terletak di Jl. Semeru No.59. Kertanegara sangat menyadari pentingnya peran teknologi dan E- WOM bagi kelangsungan bisnisnya. Hal ini dibuktikan dengan sebanyak 90 persen proses booking berasal dari situs OTA (Online Travel Agent). Sehingga penting untuk menampilkan image positif dalam situs tersebut untuk menghasilkan E- WOM yang positif. E-WOM dapat disebarkan melalui ulasan pelanggan. Kertanegara mengumpulkan ulasan pelanggan melalui dua sumber yaitu Guest Review dan situs OTA. Saat ini, proses pengolahan ulasan pelanggan masih berfokus hanya pada Guest Review. Hal ini menyebabkan evaluasi manajemen Kertanegara menjadi tidak efektif karna hanya mengandalkan Guest Review. Sementara itu ulasan pelanggan lebih banyak berasal dari ulasan online di situs OTA yang belum secara maksimal dimanfaatkan oleh pihak Kertanegara. Salah satu cara yang dapat digunakan untuk menganalisis dan mengolah teks ulasan/review tersebut adalah analisis sentimen. Analisis sentimen dilakukan dalam tingkat aspek untuk menentukan layanan dan aspek yang memiliki polaritas negatif atau positif menggunakan metode klasifikasi Support Vector Machine (SVM) dan Term Weighting (TF-IDF). Analisis sentimen menggunakan aspek lokasi, kamar, makanan, harga, dan layanan. Data teks ulasan yang digunakan berbahasa Indonesia berasal dari situs Agoda.com, Expedia, Pegi-Pegi, Booking.Com, TripAdvisor dan berjangka waktu dari tahun 2012 sampai tahun 2019. Pengujian hasil klasifikasi menghasilkan rata-rata nilai Accuracy diatas 70%. Hasil analisis kemudian divisualisasikan melalui dashboard dengan menampilkan 6 komponen penting sehingga dapat membantu Kertanegara dalam pengambilan langkah strategis untuk membenahi, memperbaiki dan meningkatkan layanan yang memiliki polaritas negatif.
English Abstract
At present, the travel booking site or Online Travel Agent (OTA) not only functions for tourism accommodation reservations but also has a new role as the Electronic Word of Mouth (E-WOM) media. Kertanegara Premium Guest House is one of the inns in the city of Malang, located on Jl. Semeru No.59. Kertanegara is very aware of the important role of technology and E-WOM for its business continuity. This is evidenced by as much as 90 percent of the booking process comes from the OTA (Online Travel Agent) site. So it is important to display a positive image on the site to produce a positive E-WOM. E-WOM can be disseminated through customer reviews. Kertanegara collects customer reviews through two sources namely Guest Review and OTA website. At present, the process of processing customer reviews is still focused only on Guest Review. This has caused Kertanegara management evaluation to be ineffective because it only relies on Guest Review. Meanwhile, more customer reviews come from online reviews on the OTA website that have not been maximally utilized by the Kertanegara party. One method that can be used to analyze and process the review text is sentiment analysis. Sentiment analysis is carried out at the aspect level to determine services and aspects that have negative or positive polarity using the Support Vector Machine (SVM) and Term Weighting (TF-IDF) classification methods. Sentiment analysis uses aspects of location, room, food, price, and service. The review text data used in the Indonesian language comes from the sites of Agoda.com, Expedia, Pegi-Pegi, Booking.Com, TripAdvisor and has a timeline from 2012 to 2019. Testing the classification results produce an average Accuracy value above 70%. The results of the analysis are then visualized through a dashboard by displaying 6 important components so that it can assist Kertanegara in taking strategic steps to fix, improve and improve services that have negative polarity
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | 0520150481 |
| Uncontrolled Keywords: | Guest House, Online Travel Agent, Analisis Sentimen, Klasifikasi, SVM, Dashboard.,Guest House, Online Travel Agent, Sentiment Analysis, Classification, SVM, Dashboard |
| Subjects: | 000 Computer science, information and general works > 004 Computer science |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | Unnamed user with username nova |
| Date Deposited: | 21 Sep 2022 04:32 |
| Last Modified: | 21 Sep 2022 04:32 |
| URI: | http://repository.ub.ac.id/id/eprint/194520 |
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