PENGEMBANGAN APLIKASI REKOMENDASI PANDUAN WISATA DI DIY MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN)

MUHAMMAD DAHLAN, NIM.10651035 (2014) PENGEMBANGAN APLIKASI REKOMENDASI PANDUAN WISATA DI DIY MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN). Skripsi thesis, UIN SUNAN KALIJAGA.

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Abstract

Based on DIY’s statictical tourism data between 2010-2012, there was an enhancenment of the number of visitors of it. In 2010, there were only 1.456.980 visitors, while in 2011 the visitors increased up to 1.607.694 people. The most significant number of visitors happened in 2012 where there were 2.360.173 visitors who come to enjoy DIY’s tourism. The visitors themselves were the tourists who came to DIY to enjoy the natural, education, and historical (cultural) tourist resorts. This system uses client-server concept, which utilizes internet connection by using GPS (Global Positioning System). The GPS System is build on android platform. The algorithm which is used in this system is K-Nearest Neighbor (KNN) algorithm. This algorithm can be used to classify new objects based on the training data which has the nearest distance with that new object data. The testing result of the system is done based on several attributes new user, such as sex, hobby, education background, and the cost calculated from the reference data. The result shows that the system goes well where it can provide the recommendation of the tourist resorts in DIY. Meanwhile, the result of the system functionality testing are that most of the respondents agree that this appication functions as it is supposed to be 98,6 % respondents agree and only 1,3 % disagree. Based on the interface testing, the result shows that 33,3 % respondents strongly agree, 60 % agree, and only 6,6 % respondents are neutral.

Item Type: Thesis (Skripsi)
Additional Information: Pembimbing : Bambang Sugiantoro, S.Si., MT
Uncontrolled Keywords: Keyword : Recommendation, Tourism, K-NN (K- Nearest Neighbor)
Subjects: Tehnik Informatika
Divisions: Fakultas Sains dan Teknologi > Teknik Informatika (S1)
Depositing User: Miftahul Ulum [IT Staff]
Date Deposited: 15 Apr 2014 10:36
Last Modified: 18 Mar 2016 12:49
URI: http://digilib.uin-suka.ac.id/id/eprint/11875

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