Open AccessTravel Time Estimation for Destination In Bali Using kNN-Regression Method with Tensorflow
Author(s) -
Murni Murni,
Rifki Kosasih,
Achmad Fahrurozi,
Tri Handhika,
Ilmiyati Sari,
Dewi Lestari
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/854/1/012061
Subject(s) - mean squared error , estimation , regression , computer science , global positioning system , regression analysis , statistics , data mining , mathematics , machine learning , engineering , telecommunications , systems engineering
On a tour activity, travel time estimation is needed so that the travel itinerary goes according to the plan. Travel time estimation is very important so we can estimate the time needed to arrive at the destinations in the travel itinerary. Therefore we need a method that can estimate travel time from one place to another. In this study, we propose the k-Nearest Neighbors Regression (kNN-Regression) method with Tensorflow to construct an estimation model. The proposed number of features in our estimation model is 8 features, i.e. zone information, time information, day information, weather information, temperature information, wind speed information, humidity information, and precipitation information. The data obtained from travel information from Ngurah Rai airport to Kuta Beach using GPS and weather information using weather application in real-time. We divide our data into two groups: a historical group consisting of 177 data and a testing group consisting of 51 data. In the testing stage, kNN-Regression will find the historical data closest to the testing data, so that the estimation value of the travel time of some testing data is not much different from the value of the nearest historical data. As a result, our proposed model gives the Mean Absolute Error (MAE) of 2.196078, Root Mean Square Error (RMSE) of 2.977036294 and accuracy rate 88.1819%.