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All Journal KLIK: Kajian Ilmiah Informatika dan Komputer Kohesi: Jurnal Sains dan Teknologi
Muhammad Rafi Fadhilah
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Energy Engineering Other

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Analisis Perbandingan Metode Dempster Shafer dan Certainty Factor pada Sistem Pakar Untuk Mendeteksi Penyakit Jantung Koroner Muhammad Rafi Fadhilah; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1624

Abstract

This research aims to develop an expert system for detecting coronary heart disease by comparing the Demster-Shafer and Certainty Factor methods in providing accurate solutions. Coronary heart disease (CHD) is a disease that often threatens human health. To overcome this problem, the development of expert systems has become an important approach in diagnosing CHD accurately and efficiently. The problems faced include the level of complexity in diagnosing CHD and the need for solutions that can provide a high level of confidence. The method used involves collecting data from various sources and analysis using both methods to determine a diagnosis. The research results show that both methods are able to provide satisfactory results, however, a comparison between the two provides additional insight in understanding the reliability and accuracy of the expert system being developed. A thorough analysis shows that the Demster-Shafer method provides a higher degree of accuracy in some cases, while Certainty Factor tends to provide faster results. However, this research also reveals that optimal results can be achieved by combining the two methods. Thus, this research makes an important contribution to the development of an expert system for coronary heart disease detection and provides a foundation for further development in this domain. In conclusion, the integration of the Demster-Shafer and Certainty Factor methods shows the potential to improve the performance and reliability of expert systems in supporting CHD diagnosis effectively. The calculation results of both methods show that the Dempster-Shafer Method produces a certainty level of 99.8%, while the Certainty Factor Method provides a confidence level of 92%.
Penerapan Metode Dempster Shafer dalam Mendiagnosa Penyakit Pneumonia Muhammad Rafi Fadhilah; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1734

Abstract

The aim of this research is to apply the Dempster Shafer Method in diagnosing pneumonia. This research aims to apply the Dempster Shafer Method in diagnosing pneumonia. The main problem faced in the diagnosis of this disease is the complexity and uncertainty in the interpretation of symptoms and medical test results. Dempster Shafer's method, a method in belief theory that allows combining information from multiple sources with different levels of certainty, was proposed as a solution to overcome this uncertainty. In this study, symptom data and medical test results from patients suspected of suffering from pneumonia were collected. Then, the Dempster Shafer Method is applied to combine information from various sources, such as blood test results, lung X-rays, and the patient's medical history. This method makes it possible to establish the level of confidence in the resulting diagnosis. The research results show that the application of the Dempster Shafer Method in diagnosing pneumonia provides more accurate results compared to traditional approaches. By considering the uncertainty and complexity in diagnosis, the Dempster Shafer Method is able to provide more reliable estimates and help doctors make more appropriate decisions in treating pneumonia cases. Application of the Dempster Shafer Method also produces a framework that can be adapted to diagnose other diseases that require managing uncertainty. Additionally, this approach can help increase efficiency in the diagnosis process, leading to a reduction in diagnostic errors and an improvement in the overall quality of patient care. Thus, this research makes an important contribution to the development of more sophisticated and reliable diagnostic methods in the medical field. The results of analysis using the Dempster-Shafer method show that the maximum value for each combination of symptoms that is important in diagnosing pneumonia is 0.9811, which is equivalent to 98.11%. Based on this interpretation, it is estimated that the patient has a high chance of suffering from severe pneumonia.
Co-Authors Agung Triayudi Fami, Amata Malvian Fikra Raifa Mohamad Carfine Iskandar