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All Journal Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Nur Azmi Ainul Bashir
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Computer Science & IT

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Optimizing an Expert System for Diagnosing a Depression Disorder Using a Case Based Reasoning Method Septian Rico Hernawan; Nur Azmi Ainul Bashir; Ifan Hakim
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 3 No. 3 (2024)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v3.i3.39

Abstract

According to data from the World Health Organization (WHO), 3.7% of the population in Indonesia experiences depression. Depression can impact both the mental and physical conditions of an individual. WHO reports that every year, approximately 800,000 people die by suicide, with depression being one of the causes. Depression treatment is handled by a professional, and in the field, the process of diagnosing depression disorders is still generally done manually by them. This creates many opportunities for errors, despite the fact that each level of depression disorder requires different handling. Inadequate treatment can hinder the patient's recovery and may potentially worsen their condition. A precise and efficient method is needed to diagnose depression disorders. An expert system can reduce the risk of errors that occur with manual calculations. The implemented case-based reasoning method can classify depression disorders. Testing was conducted using 30 datasets as initial knowledge, with 20 sample data points for testing, randomly selected from the population through questionnaires. The classification accuracy for depression disorders reached up to 90%.
Co-Authors Ifan Hakim Septian Rico Hernawan