Twitter Data as Decision Tree Parameter for Analysis of Tourism Potential Policies

Edy Subowo, Imam Rosyadi, Hadwitya Handayani Kusumawardhani
2020 Proceedings of the 1st Borobudur International Symposium on Humanities, Economics and Social Sciences (BIS-HESS 2019)   unpublished
This research provides an analysis of tourism potential in Pekalongan Regency based on Twitter media so that it can provide input for related agencies to develop new potential tourism objects. Decision tree method with C4.5 is used to classify positive reviews where tourists will visit again and vice versa negative reviews with tweet data parameters related to tourism object, like location access, service satisfaction, conditions and functionality of existing facilities, and shopping
more » ... shopping experiences. The results of the review classification from the decision tree serve as input to the promotion strategy to be applied in the website media. Training data collection of 250 tweets related to the name of the tourism object in Pekalongan Regency was conducted and the experimental process was carried out in testing the model using RapidMiner 5.3. Stratified sampling with the C4.5 Decision Tree obtained the highest accuracy rate of 92% using fold = 6:4. 20 tweet sampling tests related to the Welo Asri Petungkriyono found 17 positive review tweets with the most words related to the condition and functionality of existing facilities and 3 negative reviews related to location access and service satisfaction. This is an open access article distributed under the CC BY-NC 4.0 license -http://creativecommons.org/licenses/by-nc/4.0/. 474 form of word classifications showing positive and negative reviews. The second process starts from mining the testing data to then do the classification process with classification rules so
doi:10.2991/assehr.k.200529.099 fatcat:md7zzphvynfzdnlr3uvksjiddq