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All Journal Journal of Health (JoH) Journal Of Vocational Health Studies Journal of Research and Technology Envirotek : Jurnal Ilmiah Teknik Lingkungan Jurnal Cakrawala Maritim
Am Maisarah Disrinama
Politeknik Perkapalan Negeri Surabaya
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing Public Health

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Journal : Journal of Health (JoH)

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Perancangan Sistem Diagnosis Kesehatan Manusia Melalui Screening Digital Berbasis Desktop Application Menggunakan Metode Forward Chaining dan Neural Network: Design of a Human Health Diagnosis System Through Desktop Application-Based Digital Screening Using Forward Chaining and Neural Network Methods Indis Dwi Agustin; Nurahmad Hadi Cahyadi; Mifta Aulia Ramadhani; Mujtaba Fa’akuli Zazila; Am Maisarah Disrinama
Journal of Health (JoH) Vol 11 No 1 (2024): Journal of Health (JoH) - January
Publisher : LPPM STIKES Guna Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30590/joh.v11n1.702

Abstract

The problem of hospital services, one of which is queuing, is important because it affects hospital productivity. Hospital queues can be caused by the large number of patients and the length of patient treatment. According to the Regulation of the Minister of Health of the Republic of Indonesia Number 30 of 2022, the standard of patient satisfaction with health services must reach ≥ 90% where one of the indicators is the long waiting time. Long waiting times or queues can cause medical services to be less than optimal, especially for patients who have emergency complaints (Prabowo, 2019). Therefore, to increase the productivity of hospital services, a human health diagnosis system is designed through a desktop application-based digital screening using the forward chaining and neural network methods to make it easier for doctors to diagnose patient diseases. This innovation is also equipped with severity detection and treatment recommendations for patients. The purpose of this study is to create a knowledge model that can predict patient disease. The results of this study were obtained that the accuracy of testing the diagnosis of patient disease reached 86.6% with the functional ability of the designed diagnostic application to function 100%. With this innovation, the diagnosis of symptoms of human disease can be carried out precisely and precisely so that hospital productivity and health status in every community in Indonesia increase.
Co-Authors Anisa Nur Fadhila Indis Dwi Agustin Indri Santiasih Mifta Aulia Ramadhani Muhammad As'addudin Mujtaba Fa’akuli Zazila Mukhammad Jamaludin Novia Hayu Rahmawati Nurahmad Hadi Cahyadi Rohmat Fais Sahhal Hafidhuddin Siskarani Siskarani