Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web
- Alif Jalaluddin Universitas Muhammadiyah Magelang, Magelang, Indonesia
-
Endah Ratna Arumi
*
Universitas Muhammadiyah Magelang, Magelang,
Indonesia
- Dimas Sasongko Universitas Muhammadiyah Magelang, Magelang, Indonesia
- Sambodo Sriadi Pinilih Universitas Muhammadiyah Magelang, Magelang, Indonesia
- Uky Yudatama Universitas Muhammadiyah Magelang, Magelang, Indonesia
- Muhammad Resa Arif Yudianto Universitas Muhammadiyah Magelang, Magelang, Indonesia (*) Corresponding Author
Abstract
Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.
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