A Hybrid Model using Artificial Neural Network and Genetic Algorithm for Degree of Injury Determination

• Published in: International journal of innovative technology and exploring engineering Vol. 9; no. 2; pp. 1357 - 1365
• Main Authors: Wardhana, Mohd Hadyan, Basari, Prof. Dr. Abd Samad Hasan, Mohd Jaya, Dr. Abdul Syukor, Afandi, Prof. Dr. dr. Dedi, Dzakiyullah, Nur Rachman
• Format: Journal Article
• Language: English
• Published: 30.12.2019
• ISSN: 2278-3075; 2278-3075
• DOI: 10.35940/ijitee.B6169.129219

Essentially, determination degree of injury is crucial for to support the law enforcement process. The existing models are deemed difficult in identifying the critical features for degree of injury classification. Some of which are considerable irrelevant and cause the inconsistency decision on process to determine degree of injury among the practitioners. If the Visum et Repertum (VeR) report is not well interpreted, the victim will get injustice decision. The purpose of this study is to develop a hybrid model for determining degree of injury. Based on Visum et Repertum (VeR) data. The model can classify the output of either having a minor, moderate, or serious injury which inclusively stated in Indonesian Penal Code. A hybrid model is developed from literature and case studies are conducted in three hospitals in Pekanbaru, Indonesia. Analysis is performed to discover the suitable component of the model-due to lack of comparison and analysis on the combination of critical features analysis and optimize the classification algorithm. Development and testing of the model are utilized VeR Dataset as private dataset (289 patients’ data). In validating model, three case studies are investigated based on Subject Matter Expert (SME) groups to identify the agreement level. The questionnaires consist of a component, implementation, and viability of model that involved. Hybrid model components are validated by the SMEs, whereby the group determined highest rank of accuracy performance. Result from the questionnaire reveal that the average agreement level of SMEs. In conclusion, the finding shows hybrid model is generated 99.23% accuracy. The model components are implementable as a model and acceptable by the Practitioners as contribution for determining degree of injury.