| Penulis/Author |
DWI AJI KURNIAWAN (1); Dr.Eng. Ir. Sunu Wibirama, S.T., M.Eng., IPM. (2); Ir. Noor Akhmad Setiawan, S.T., M.T., Ph.D., IPM. (3) |
| Abstrak/Abstract |
The growth of vehicles in Yogyakarta Province,
Indonesia is not proportional to the growth of roads. This problem
causes severe traffic jam in many main roads. Common traffic
anomalies detection using surveillance camera requires manpower
and costly, while traffic anomalies detection with crowdsourcing
mobile applications are mostly owned by private. This research
aims to develop a real-time traffic classification by harnessing the
power of social network data, Twitter. In this study, Twitter data
are processed to the stages of preprocessing, feature extraction,
and tweet classification. This study compares classification
performance of three machine learning algorithms, namely Naive
Bayes (NB), Support Vector Machine (SVM), and Decision Tree
(DT). Experimental results show that SVM algorithm produced
the best performance among the other algorithms with 99.77%
and 99.87% of classification accuracy in balanced and imbalanced
data, respectively. This research implies that social network
service may be used as an alternative source for traffic anomalies
detection by providing information of traffic flow condition in
real-time. |
