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Subagio, S.
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Expert System for Stroke Diagnosis Using the Forward Chaining Method for Lecturers at UNIVA Labuhanbatu Samsir, Samsir; Syahputra, Andi; Subagio, S.
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.176-190

Abstract

Stroke is a health problem that often occurs when the blood supply to the brain lacks oxygen and nutrients. As a result, in a matter of minutes, brain cells begin to die. This condition is classified as a serious disease and can be life-threatening, therefore requiring immediate medical attention. Stroke accounts for 10% of all deaths in the world and is the third leading cause of death after coronary heart disease (13%) and cancer (12%) in developed countries. The prevalence of stroke varies in different parts of the world. The prevalence of stroke in the United States is around 7 million (30%), while in China the prevalence of stroke ranges from 1.8% (rural) to 9.4% (urban). Worldwide, China is the country with the highest death rate from stroke (19.9% ​​of all deaths in China), along with Africa and North America. The incidence of stroke worldwide is 15 million each year, one third of whom die and one third of whom experience permanent disability. The purpose of this study is to help and facilitate lecturers at Labuhanbatu University to diagnose stroke in determining treatment and how to overcome it effectively and efficiently. Lecturers at Univa Labuhanbatu can diagnose in advance what disorders they are experiencing before going to the doctor, so they can save time and money. This system is present as a means to help diagnose patients using the Forward Chaining method. With an expert system, laypeople will be able to solve quite complicated problems that can actually only be solved with the help of experts. For experts, expert systems will also help their activities as very experienced assistants. Microsoft Visual Studio .NET is a complete collection of development tools for building ASP.NET Web applications, XML Web Services, desktop applications, and mobile applications. In Visual Studio, these are the .NET programming languages ​​such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp). All use the same integrated development environment or IDE so that it is possible to share tools and facilities
Expert System for Early Detection of Depression Using Psychological Symptoms Certainty Factor Method Rambe, Nisa indriani; Samsir, Samsir; Subagio, S.
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.243-255

Abstract

Depressive disorders in the elderly often go undetected due to early symptoms that resemble normal aging processes. The absence of an early detection system becomes a major obstacle to prompt treatment. This study aims to design an expert system for early detection of depression in the elderly using the Certainty Factor (CF) method. The dataset was collected from 60 patient complaint narratives and validated by three professional psychologists with over five years of experience in geriatric psychiatry. The system design process includes symptom extraction using Natural Language Processing (NLP), CF value calculation for each symptom, and classification of depression risk (low, moderate, high). The system architecture consists of a knowledge base, inference engine, and user interface. Validation was conducted through diagnostic accuracy testing and user evaluation using a Focus Group Discussion (FGD). The results showed a validity level of 73%, and 88.6% of respondents agreed that the system can assist in early diagnosis. The novelty of this study lies in the integration of NLP and Certainty Factor tailored to the narrative patterns of the elderly, combined with a user-friendly interface design. This system is expected to serve as a supportive tool for psychologists and families in the early detection of depression in elderly individuals.