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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF SCIENCE AND APPLIED ENGINEERING Jurnal Ilmiah Kesehatan Media Husada Jurnal EnviScience (Environment Science) Jurnal Kesehatan Tambusai MAHESA : Malahayati Health Student Journal Jurnal Higiene Sanitasi Berkala Ilmiah Kedokteran dan Kesehatan Masyarakat

Rudy Joegijantoro, Rudy

STIKES Widyagama Husada Malang

Author-ID : 648246

Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Sistem Pakar Penyakit Menular Menggunakan Dempster Shafer Dengan Rekomendasi Tempat Layanan Kesehatan Istiadi Istiadi; Emma Budi Sulistiarini; Rudy Joegijantoro; Dedi Usman Effendy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Delay in the handling of a type of disease can pose a risk for someone who has the surrounding environment. Often the casualties are caused by people's ignorance of the spread of dangerous infectious diseases. People's ignorance as an action that must be done immediately and where to do to get help. Thus it is necessary to build an application of an expert system that can diagnose infectious diseases, provide recommendations for disease management, and provide recommendations for appropriate and acceptable health services. The system was built to diagnose six types of infectious diseases that are of particular concern to Malang City. Various infectious diseases with similar symptoms that appear will lead to the possibility of a diagnosis and many possibilities for diagnosis. The Dempster Shafer method is an approved one that can be used in overcoming these factors. The disease expert consultation system application using the Dempster Shafer method obtained an accuracy test result of 88.5%. While the system usability test obtained results, 76% agreed to system reliability, 85% strongly agreed to system efficiency, 83% strongly agreed to ease for use system, and 79% agreed to accurate system.

Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan Istiadi Istiadi; Emma Budi Sulistiarini; Rudy Joegijantoro; Affi Nizar Suksmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains accuracy values for the CBR K-Nearest Neighbor, CBR Minkowski Distance, and CBR 3W-Jaccard methods at a threshold of 70%, respectively 65.71%, 80%, and 85.71%. The average length of retrieve time required for each similarity method is 0.083s, 0.107s, and 6.325s, respectively. While the diagnosis of disease with Dempster-Shafer gets an accuracy value of 88.57%.

Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases Istiadi Istiadi; Emma Budi Sulistiarini; Rudy Joegijantoro; Anik Vega Vitianingsih; Affi Nizar Suksmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4656

E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3 involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification was greater than that of the two test scenarios that were suggested, which was 49.43%.

Co-Authors Affi Nizar Suksmawati Anggela, Thesa Anik Yuesti Anwar Musaddad Aviv Yuniar Rahman Dedi Usman Effendy Devita Sari Emma Budi Sulistiarini Erwin Cahya Meilanda Hazainudin, Gilang Dewi Fauziah Istiadi MARIFANI FITRI ARISA Nindi, Nindia Puteri Priyo Santoso, Fany Dwi Putri , Azuan Ludia Sabar Setiawidayat Saktiawan, Yusup Santoso Budiarjo Sediawan, MN Lisan Septia Dwi Cahyani Subhi , Misbahul Suksmawati, Affi Nizar Vira Tika Yuniar Wahyuni, Ike Dian

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