Handwriting Vocal Character Pattern Recognition: Implementation of Artificial Neural Network Algorithm for Electronic Medical Record

Syamsul Arifin, Gamasiano Alfiansyah, Khafidurrohman Agustianto, Syamsiar Kautsar, Hendra Yufit Riskiawan, Adi Heru Utomo


The sterilization center is one of the important links for infection control and plays a role  in  suppressing  the  incidence  of  infection.  Sterilization  is  very  important  especially  for surgical  instruments,  especially  nowadays  the  development  of  surgical  procedures  and  the complexity  of  medical  equipment.  In  addition,  hospitals  as  health  care  providers  attempt  to prevent  the  risk  of  infection  for  patients  and  hospital  personnel.  This  study  aims  to  develop tools/systems that can minimize the spread of infection with the use of facial recognition for a location  that  potentially  infects  people  without  Self  Protective  Equipment  (APD).  Facial recognition  method developed by research is Eigenface Method. Eigenfaces are methods that use a set of eigenvectors in image processing for human face recognition. The created eigenfaces will appear as bright and dark areas arranged in a particular pattern. This pattern is how different facial features are selected to be evaluated and assessed. There will be a pattern to evaluate the symmetry, if there is a facial hair style, where the hairline is, or evaluation of the size of the nose or mouth. Other Eigenfaces  have patterns that are less easily recognizable, and eigen surface shadows may look very little like faces. This method is expected the system/tool can provide a good introduction to the person/person who is allowed or not allowed access a particular room. So the purpose of the research will be achieved to suppress the extent of infection and present the Central Sterilization System that can be widely used in the hospital.

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