Bali as an icon of tourism in Indonesia has been visited by many foreign tourists. Thus, Bali is one of the provinces that contribute huge foreign exchange for Indonesia. However, this potential could be threatened by the effectuation of the ASEAN Economic Community as it causes stricter competition among ASEAN countries including in tourism field. To resolve this issue, Balinese government need to forecast the arrival of foreign tourist to Bali in order to help them strategizing tourism plan. However, they do not have an appropriate method to do this. To overcome this problem, this study contributed a forecasting method using Recurrent Neural Network Backpropagation Through Time. We also compare this method with Single Moving Average method. The results showed that proposed method outperformed Single Moving Average in 10 countries tested with 80%, 70%, and 70% better MSE results for 1, 3 and 6 months ahead forecast respectively.
We introduce the new task of domain name dispute resolution (DNDR), that predicts the outcome of a process for resolving disputes about legal entitlement to a domain name. The ICANN UDRP establishes a mandatory arbitration process for a dispute between a trademark owner and a domain name registrant pertaining to a generic Top-Level Domain (gTLD) name (one ending in .COM, .ORG, .NET, etc). The nature of the problem leads to a very skewed data set, which stems from being able to register a domain name with extreme ease, very little expense, and no need to prove an entitlement to it. In this paper, we describe the task and associated data set. We also present benchmarking results based on a range of models, which show that simple baselines are in general difficult to beat due to the skewed data distribution, but in the specific case of the respondent having submitted a response, a fine-tuned BERT model offers considerable improvements over a majority-class model.
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