Tourism destination rating system based on social media analysis (proposal and dataset development in Indonesian language)

Ramadhani, Diana Mayangsari; Rahmad, Cahya; Rahutomo, Faisal

doi:10.1109/siet.2017.8304106

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…Ramadhani in [30] explained the creation of the automatically rating system of tourism destination based on a travel blog through an automatic approach by a new semantic analysis algorithm in Indonesia in 2017. However, in [31], it was found the important factors for the tourists' preferences to visit the Vredeburg museum in Yogyakarta, Indonesia in 2018.…”

Section: Smart Tourismmentioning

confidence: 99%

Smart Tourism Recommendation Model: A Systematic Literature Review

Huda¹,

Ramadhan²,

Trisetyarso³

et al. 2021

IJACSA

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The tourism industry has become a potential sector to leverage economic growth. Many attractions are detected on several platforms. Machine learning and data mining are some potential technologies to improve the service of tourism by providing recommendations for a specific attraction for tourists according to their location and profile. This research applied for a systematic literature review on tourism, digital tourism, smart tourism, and recommender system in tourism. This research aims to evaluate the most relevant and accurate techniques in tourism that focused on recommendations or similar efforts. Several research questions were defined and translated into search strings. The result of this research was promoting 41 research that discussed tourism, digital tourism, smart tourism, and recommender systems. All of the literature was reviewed on some aspects, in example the problem addressed, methodology used, data used, strength, and the limitation that can be an opportunity for improvement in future research. This study proposed some references for further study based on reviewed papers regarding tourism management, tourist experience, tourist motivation, and tourist recommendation system. The opportunities for a further research study can be conducted with more data usage especially for a smart recommender system in tourism through many types of recommendation techniques such as content-based, collaborative filtering, demographic, knowledge-based, community-based, and hybrid recommender systems.

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Section: Smart Tourismmentioning

confidence: 99%

Smart Tourism Recommendation Model: A Systematic Literature Review

Huda¹,

Ramadhan²,

Trisetyarso³

et al. 2021

IJACSA

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“…Usulan ini memberi penjabaran rinci secara komputasi dari peneliti lain sebelumnya [5]. Kemudian makalah ini menjabarkan cara bekerjanya metoda ini, resume pengujian pada beberapa kasus [6][7] [8], hingga implementasinya menjadi sebuah library pemrograman Python yang bisa diakses secara terbuka [9][10] [11]. Secara khusus makalah ini menjabarkan pengujian metode ini pada implementasi kasus tempat wisata, melanjutkan pembahasan pada makalah sebelumnya yang belum tuntas [6].…”

Section: Untukunclassified

“…Kemudian makalah ini menjabarkan cara bekerjanya metoda ini, resume pengujian pada beberapa kasus [6][7] [8], hingga implementasinya menjadi sebuah library pemrograman Python yang bisa diakses secara terbuka [9][10] [11]. Secara khusus makalah ini menjabarkan pengujian metode ini pada implementasi kasus tempat wisata, melanjutkan pembahasan pada makalah sebelumnya yang belum tuntas [6]. Berturut-turut mengikuti alur jurnal ini, bagian selanjutnya membahas metode penelitian, kemudian hasil dan pembahasan, dan ditutup dengan bagian kesimpulan.…”

Section: Untukunclassified

“…Sedangkan pengembangan library ini diceritakan lebih detail pada publikasi "Pengembangan Library Sistem Pemeringkat otomatis Berbasis Kata Sifat" [9]. Library tersebut juga diuji pada kasus wisata dengan kata kunci lainnya selain [6] dengan hasil sangat baik. Nilai korelasi mendekati 1 dengan tingkat galat hanya berkisar dalam angka belasan [9].…”

Section: Ketersediaan Implementasi Sebagai Library Pythonunclassified

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Sistem Pemeringkat Otomatis Berbasis Kata Sifat

Rahutomo

Ramadhani

Pratiwi

2019

RESTI

Self Cite

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This paper exposes a novel method has been developed during these 2 years. The method is named as “adjective based automatic rating system”. This method is developed to utilize the abundant availability of text on the internet for quality and performance rating purpose. The text is processed in such a way and leave only the adjectives. Semantic analysis is done by two knowledge: adjectives of performance definition and Indonesian adjectives database with its synonym-antonym relation. This research proposes several formula steps, therefore the method output is a rating score that can be tunned its scale. The experiment results have been gathered for several objects: tourism, courier service, and organization performance. With detail information in tourism object experiment, this paper cites the other experiment results as well. This paper also provides availability information of the method as Python library. The results show a high correlation score, always more than 0.9. The results also show acceptable error scores, never more than 45%.

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“…In other studies, semantic analysis algorithm is used to measure the words in two texts. The rate of tourism destination is created by comparing the dataset of the travel blog to make the review quality is better [7]. The semantic algorithm is used as knowledge in Tourism Mobile Recommender System (RSs) for providing information about the tourism destination [8].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of the Culinary Recommendation System Using Sentiment Analysis and Simple Adaptive Weighting in Bengkulu, Indonesia

Setiawan¹,

Susilo²,

Erlansari³

et al. 2018

EECSI

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In 2017, the minister of Indonesia tourism stated that everyone who travels spends his time for culinary about 30-40%. The key point in increasing the tourism income; especially from the culinary sector is about how to inform and promote the wealth of Indonesia culinary to all travellers. The information system of Bengkulu tourism has been developed in the previous study. However, that system has not been able to provide the best culinary recommendations to the travellers. This study focuses on designing and implementing the recommendation system of Bengkulu culinary by using sentiment analysis and simple adaptive weighting (SAW). The recommendation offered is based on the user review and criteria as well. The user review will be classified into positive, negative and neutral reviews by the sentiment analysis method. If the user needs culinary information based on criteria, the system will provide a recommendation and rank of culinary by using simple adaptive weighting. These criteria used are the average price, opening hours, facilities, distance from a central city, and transportation as well. Sentiment analysis method obtains the accuracy of recommendation classification at 79% while the recommendation rank obtained by the SAW method is 90.83%. These results show that the proposed method has a potential for assisting the travellers to gain the best culinary recommendation, especially in the Bengkulu area.

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Tourism destination rating system based on social media analysis (proposal and dataset development in Indonesian language)

Cited by 6 publications

References 7 publications

Smart Tourism Recommendation Model: A Systematic Literature Review

Smart Tourism Recommendation Model: A Systematic Literature Review

Sistem Pemeringkat Otomatis Berbasis Kata Sifat

Design and Implementation of the Culinary Recommendation System Using Sentiment Analysis and Simple Adaptive Weighting in Bengkulu, Indonesia

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