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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknoinfo JIKA (Jurnal Informatika) Bulletin of Computer Science Research Insearch: Information System Research Journal Journal of Computing and Informatics Research
Heriyani, Nofitri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences Other

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Journal : Journal of Computing and Informatics Research

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Pengembangan Sistem Pakar Diagnosis Jenis Stres Menggunakan Pendekatan Dempster-Shafer Theory Sah, Andrian; Heriyani, Nofitri; Jafar Rumandan, Rhaishudin; Lasiyono, M. Munawir
Journal of Computing and Informatics Research Vol 4 No 2 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v4i2.1941

Abstract

Stress is a psychological issue commonly experienced in society and can develop into more serious disorders if not properly addressed. However, access to professional services remains limited due to constraints such as time, cost, and unequal distribution of mental health professionals. Therefore, the objective of this study is to develop an expert system using a web-based Dempster-Shafer Theory (DST) approach capable of diagnosing stress types based on user-reported symptoms. DST enables the integration of various pieces of evidence to produce conclusions with measurable confidence levels. The system is equipped with functionality for managing symptom data, stress types, and the ability to provide diagnostic results accompanied by recommended solutions. Testing results demonstrated an accuracy level of 93.33%, placing this system in the "Good" category according to standard performance evaluation classifications. The implementation of DST has proven effective in managing data uncertainty and supporting confidence-based decision-making. This research contributes to the development of DST-based diagnostic technology that can be widely accessed via a web platform, providing a reliable alternative for early detection of stress types.
Co-Authors Abdus Salam, Abdus Azhari, Lukman Handayani, Nurdiana Jafar Rumandan, Rhaishudin Lasiyono, M. Munawir Luqmanul Hakim Moh. Erkamim Mulyadi Mulyadi Nurhasan Nugroho Sah, Andrian Septarini, Ri Sabti Soares, Teotino Gomes Tonggiroh, Mursalim Yanuardi Yanuardi