Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3937
Title: IDENTIFYING THE CHARACTERISTICS OF TRAFFIC ACCIDENT VICTIMS IN SIDOARJO IN 2016
Authors: Mukti Septianingtyas, Daniar
Keywords: ordinal logistic regression
traffic accident
Issue Date: 2019
Abstract: IDENTIFYING THE CHARACTERISTICS OF TRAFFIC ACCIDENT VICTIMS IN SIDOARJO IN 2016 Daniar Mukti Septianingtyas Departemen Biostatistika dan Kependudukan Fakultas Kesehatan Masyarakat Universitas Airlangga, Surabaya, Indonesia Alamat Korespondensi: Daniar Mukti Septianingtyas Email: daniarmukti94@gmail.com ABSTRACT Traffic accident becomes a very serious case because it causes not only material loss but also physical and psychological harms to the subject and the people around him. Accidents that occurred resulted in not only injuries but also death. This study aims to identify characteristics of traffic accident victims in Sidoarjo in 2016. It was an observational study with cross sectional design and based on daily data of traffic accident with 735 samples. Data were processed by ordinal logistic regression statistic test. In this case, variables of the study included the severity of victim, age, gender, profession, time of occurrence, type of collision, and type of vehicle. The results of characteristic identification showed that most of the victims had minor injuries, were male, aged ≥ 34 years old, workers, and got into accidents in the afternoon. The conclusion was factors affecting the severity of traffic accident victims in Sidoarjo were head-on-collisions (hitting straight) and motorcycles as the vehicle type. Modelling obtained was 3,133 for the constant of head-on-collision (hitting straight), 1,464 for the constant of vehicle type (motorcycles), and Y value of 4,597. This study was not supported by complete predictor data, thus the data need to be added so that the accuracy of classification increases and the value gets significant. Keywords: ordinal logistic regression, traffic accident
URI: http://localhost:8080/xmlui/handle/123456789/3937
Appears in Collections:VOL 14 NO 1 2019

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