Development of Classification Features of Mental Disorder Characteristics Using The Fuzzy Logic

Meza Silvana, Meza and Ricky Akbar, Ricky and Derisma, Derisma and Mia Audina, Mia and Firdaus, firdaus Development of Classification Features of Mental Disorder Characteristics Using The Fuzzy Logic. International Conference on Information Technology Systems and Innovation (ICITSI) Bandung - Padang. October 22-25,2018. pp. 410-414.

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Abstract

Abstract—Mental disorders are related to self-injurious behavior problems of mind, such as the tendency to commit suicide. This research has built a system to classify the disorder. It explains that a system is used to help the people recognize mental illness as a diagnosis detection. Diagnosis can be done in the form of automation system using data mining with Fuzzy Logic method. This system can make decision to classify the mental illnesses based on symptoms. The first stage of the research was collecting and preprocessing the data by type. There are six types of psychiatric disorders that are determined, namely Schizophrenia Paranoid, Phobia, Depression, Anxiety, Obsessive Compulsive Disorder (OCD), and Anti-Social. The source of the data were questionnaires that consisted of the list of symptoms and types of disorders that were distributed to 16 selected respondents, including psychiatric specialists, psychology lecturers, general practitioners, psychiatric hospital nurses, and psychology students. The next stage was building the fuzzy process to determine ten inputs in the form of symptoms. Outputs system were six types of the disease. The fuzzy inference system used Mamdani model and obtained 65 rules in determining the classification. The result of system test is done for both training and testing data and accuracy level of 91.67% for training data and 81.94% for testing data

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