Full Description
| Cataloguing Source | LibUI ind rda |
| Content Type | text (rdacontent) |
| Media Type | unmediated (rdamedia); computer (rdamedia) |
| Carrier Type | volume (rdacarrier); online resource (rdacarrier) |
| Physical Description | xiii, 66 pages. : illustration ; 28 cm + appendix |
| Concise Text | |
| Holding Institution | Universitas Indonesia |
| Location | Perpustakaan UI, Lantai 3 |
- Availability
- Digital Files: 1
- Review
- Cover
- Abstract
| Call Number | Barcode Number | Availability |
|---|---|---|
| S57356 | 14-17-808254367 | TERSEDIA |
| No review available for this collection: 20386399 |
Abstract
Kecelakaan lalu lintas merupakan salah satu penyebab terbesar tingginya angka kematian di suatu negara termasuk di Indonesia. Oleh karena itu, diperlukan penelitian untuk mengetahui faktor-faktor yang mempengaruhi tingkat fatalitas kecelakaan lalu lintas. Metode data mining digunakan dalam penelitian ini karena data mengenai kecelakaan berjumlah besar dan bersifat heterogen. K-means clustering digunakan untuk mengatasi heterogenitas data dengan mengelompokkan kasus kecelakaan yang sama karakteristiknya. Kemudian bayesian network digunakan untuk menganalisis faktor-faktor yang mempengaruhi tingkat fatalitas kecelakaan lalu lintas pada setiap cluster. Berdasarkan hasil penelitian, faktor-faktor yang mempengaruhi tingkat fatalitas kecelakaan lalu lintas pada setiap cluster berbeda-beda
Traffic accident is one of the major causes of the mortality rate in countries, including Indonesia. Thus, research is needed to determine factors that affect the traffic accidents injury severity. Data mining is used in this study because the traffic accidents data are massive and heterogeneous. K-means clustering is used to divides heterogeneous data of accidents into several homogenous classes or clusters. Then bayesian network is used to analyze the factors that affect the traffic accidents injury severity in each cluster. Based on this research, the factors that affect the traffic accidents injury severity in each cluster is different.
Traffic accident is one of the major causes of the mortality rate in countries, including Indonesia. Thus, research is needed to determine factors that affect the traffic accidents injury severity. Data mining is used in this study because the traffic accidents data are massive and heterogeneous. K-means clustering is used to divides heterogeneous data of accidents into several homogenous classes or clusters. Then bayesian network is used to analyze the factors that affect the traffic accidents injury severity in each cluster. Based on this research, the factors that affect the traffic accidents injury severity in each cluster is different.