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Koniasari
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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Tongue Detection For Identification Of Syndrome Diagnosis In Heart Disease Using Convolutional Neural Network Niko Suwaryo; Koniasari; Amat Basri
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3285

Abstract

Convolutional Neural Network (CNN) which is one of the Deep Learning methods for Image identification and CNN models can identify images well but in this case it requires higher accuracy because the case is very crucial to determine the risk of heart disease. The initial stage in this study was the collection of tongue image data, 4836 training data and 1209 testing data. The image data used were the front, right side, left side of the tongue and under the tongue. The dataset was obtained from taking pictures using a smartphone camera centimeters above the object. The distribution of data in each class is shown in the following figure. The model from the two CNN algorithm experiments has accuracy performance. Based on the training results the model from the algorithm gets an accuracy value and Testing by identifying 20% of the total dataset as test data. The identification results are formed in a Confusion Matrix to then be poured into a classification report and obtain: train loss 0.301446, train accuracy 0.862696, test loss 0.314132 and test accuracy 0.850290 so that from the results of the tongue data test it can be concluded that the accuracy value is quite good, above 80%.
Analysis of Patient Safety in TM/CAM Services, Women's Health and its Legal Perspective Kusnawirawan, Iwan; Koniasari; Alfian , RM; Alpiah, Dini Nur; Rosyita, Hafna; Amalia, Anindini Winda
Jurnal Health Sains Vol. 5 No. 3 (2024): Journal Health Sains
Publisher : Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jhs.v5i3.1246

Abstract

Ensuring patient safety is a crucial aspect of healthcare including TM/CAM, particularly in the field of women's health. Healthcare providers must prioritize the safety of women during pregnancy and childbirth to prevent any harm or potential risks. This research used a traditional review, increasing the amount of literature that has been read and analyzed by the researcher. Normative-juridical principles in a prescriptive analytical manner with a statutory regulatory approach and the thinking method is deductive. General patient safety principles can be used in TM/CAM services by implementing the International Classification of Patient Safety (ICPS). Women are at risk due to their genital, hormonal, and pregnancy characteristics and may be affected by disease conditions acquired before or during pregnancy, and complications from physiological events at the end of pregnancy. Regulations on traditional health services and women's health care in Law No. 17 of 2023 and other regulations. This arrangement must guarantee safety, quality, and affordability. Patient safety is specifically regulated by Regulation of the Minister of Health of the Republic of Indonesia Number 11 of 2017 concerning Patient Safety. TM/CAM health services have developed in society and have to assess quality and safety standards, registration and accreditation have been established and TM/CAM health service practitioners, both those with formal and non-formal education, are required to have a competency certificate and Registration Certificate (STR). And Licenses (SIP) and Traditional Healers Registered Certificate (STPT).
Co-Authors Alfian , RM Alpiah, Dini Nur Amat Basri Anindini Winda Amalia Kusnawirawan, Iwan Rosyita, Hafna Suwaryo, Niko