scholarly journals Sistem Pakar Diagnosa Penyakit Kulit Akibat Virus Menggunakan Teorema Bayes

Telematika ◽  
2018 ◽  
Vol 15 (2) ◽  
pp. 117
Author(s):  
Putri Taqwa Prasetyaningrum ◽  
Nuri Budi Hangesti

AbstractSkin is one of the most important organs in humans, because through the skin we can feel the feel and the surface of an object. If the skin is attacked, the disease may cause many consequences, such as from mild itching or to severe, the skin can not feel anything. Based on existing problems in the community created an application that can help people to recognize skin diseases suffered, so the level of awareness will keep the cleanliness higher, namely the application of expert system of viral skin disease using bayes theorem. In this research will be designed a system using method bayes theorem this expert system will be able to produce a diagnosis of skin diseases and treatment that needs to be done. With this application of course can help people to recognize the illnesses that have been allegedly allegedly to refer to injured. Based on 20 data that have been tested against experts and systems, for patients affected by viral skin diseases were 17 patients and those not infected were 3 patients. As for the suitability of testing between the validation results of experts (doctors) and the system, obtained percentage of 85%.Keywords: Skin diseases, Expert system, Teorema Bayes.AbstrakKulit adalah salah satu organ terpenting pada manusia, karena melalui kulit kita dapat merasakan rasa dan permukaan sebuah benda. Apabila kulit terserang penyakit maka akan banyak akibat yang akan ditimbulkannya, mulai dari yang ringan yaitu terasa gatal atau sampai yang berat yakni kulit tidak dapat merasakan apapun. Berdasarkan permasalahan yang ada di dalam lingkungan masyarakat diciptakan suatu aplikasi yang dapat membantu masyarakat untuk dapat mengenali penyakit kulit yang diderita, sehingga tingkat kesadaran akan menjaga kebersihan makin tinggi, yakni aplikasi sistem pakar penyakit kulit akibat virus. Pada penelitian ini akan dirancang sebuah sistem menggunakan metode Teorema Bayes. Sistem pakar ini nantinya dapat menghasilkan diagnosa penyakit kulit serta pengobatan yang perlu dilakukan. Dengan adanya aplikasi ini tentunya bisa membantu masyarakat mengenali penyakit yang dideritanya sehingga tersadar untuk merujuknya ke dokter ahli. Berdasarkan 20 data yang telah diujikan terhadap pakar dan sistem, untuk pasien yang terkena penyakit kulit akibat virus adalah 17 pasien dan yang tidak terkena infeksi adalah 3 pasien. Sedangkan untuk kesesuaian pengujian antara hasil validasi pakar (dokter) dan sistem, diperoleh tingkat persentase keberhasilan sebesar 85%.Kata Kunci : Penyakit kulit, Sistem Pakar, Teorema Bayes. 

2019 ◽  
Vol 6 (1) ◽  
pp. 107
Author(s):  
Gita Malinda ◽  
Andi Farmadi ◽  
Muliadi Aziz

<p><em>Cats are pets that are very close to humans. Infectious pet diseases can sometimes spread quickly and can be fatal, both in animals and humans. For early prevention, the pet disease must be immediately known which in this case is a cat skin disease. To find out the diagnosis of cat skin disease using the Dempster-Shafer method. So that the results obtained the strongest confidence value of cat skin disease {A1, A3, A4} which is equal to 0.48, which was obtained from three existing symptoms, namely wet dry crust, moist inner moist ear and frequent scratching.</em></p><p><strong><em>Keywords:</em></strong><em> Cats, Expert System, Dempster Shafer.</em></p><p><em>Kucing merupakan hewan </em><em>peliharaan </em><em>yang sangat dekat dengan manusia.. Penyakit hewan</em><em> peliharaan</em><em> yang menular terkadang dapat menyebar secara cepat dan dapat berakibat fatal, baik pada hewan </em><em>dan </em><em>manusia. </em><em>Untuk pencegahan dini maka penyakit hewan peliharaan tersebut harus segera diketahui yang dalam kasus ini adalah penyakit kulit kucing. Untuk mengetahui diagnose penyakit kulit kucing tersebut menggunakan metode dempster-shafer. Sehingga memproleh hasil </em><em>Nilai keyakinan paling kuat terhadap penyakit kulit kucing  {A1,A3,A4} yaitu sebesar 0,48, yang didapat dari tiga gejala yang ada yaitu kerak kering basah, bagian dalam telinga basah lembab dan sering menggaruk.</em></p><p class="isi"><strong><em>Kata kunci:</em></strong><em> </em><em>Kucing</em><em>,</em><em> Sistem Pakar,</em><em> </em><em>Dempster Shafer.</em></p><p><em><br /></em></p>


2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Khoirunnisa devita Sari ◽  
Ade Eviyanti

Skin disease is a disease that often found in tropical countries like Indonesia. According to the survey, skin disease is the third of the ten most outpatient diseases. Lack of public knowledge about skin diseases and how to prevent and treat them can cause a person to develop acute skin diseases. The purpose of this research is to create an expert system application for diagnosis of human skin diseases using the web-based naïve Bayes method. With expert system, it hoped that human skin diseases can be detected early and can minimize the occurrence of more dangerous diseases. The calculation in this expert system uses the naïve Bayes method. This expert system makes diagnosis by analyzing input of symptoms experienced by patient and then processing it using certain rules according the expert knowledge that has been stored in the knowledge base. The result of this research is to build an expert system for diagnosing human skin diseases using website-based naïve Bayes. The results of the system trial of 20 respondents were the website could provide diagnosis results based on the inputted rules and could diagnose skin diseases properly. This website can used as an alternative use of technology so it can be used to diagnose skin diseases quickly, precisely and accurately. So in the future the handling of skin diseases can be faster and more efficient.


2018 ◽  
Vol 218 ◽  
pp. 02015
Author(s):  
Jenie Sundari ◽  
Hamimah ◽  
Popon Handayani ◽  
Yunita ◽  
Ibnu Dwi Lesmono ◽  
...  

Skin is a sense of touch in humans’ body which one of its functions is to feel the touch. Another function of the skin is to release the residual substances such as sweat. This part of the human body is very sensitive, easy to get hurt, and feels a sense of sensitivity. Human skin consists of epidermis and dermis. Skin diseases can be caused by a decrease in the immune system, allergies, viruses, or other causes. Skin disease is generally caused by a less guarded hygiene, bacteria, viral, allergic reactions, and low body resistance. If the cause of skin disease is only due to a lack of maintaining cleanliness, it can be prevented by changing lifestyles to be cleaner and healthier. Especially in Indonesia which is a tropical country, where the humidity is very high and can increase the development of bacteria on the skin. In another situation, not all people understand skin diseases for treatment or prevention. By the reason, The Forward Chaining Method is made to provide information about diagnoses of several skin diseases and produces conclusions. The existence of this expert system application makes it easy for people to get any information about skin diseases


2019 ◽  
Vol 3 (3) ◽  
pp. 435-442
Author(s):  
Feriantano Sundang Pranata ◽  
Jufriadif Na’am ◽  
Sumijan

Fungal skin disease is a type of disease that is often suffered people in the tropics. Phenomena in the community often assume that this disease will heal by itself. However, if left the effects of fungal skin disease can worsen the sufferer condition. The importance of early detection and treatment is very necessary, but this requires patients go to hospital or doctor so that patients spend a lot of time and money. For this reason, was build an android expert system with speech to early diagnose fungal skin diseases. From this initial diagnosis will save time and money and provide alternative prevention for sufferers. The method used in this study is the Certainty Factor of 20 patients. The accuracy of the test results to the system compared to the results of the doctor's diagnosis is 95%. So that, this expert system can be an early alternative in diagnose fungal skin diseases in humans.


2020 ◽  
pp. 48-55
Author(s):  
Deosa Putra Caniago ◽  
Sumijan

The skin is the outer protective organ for humans. Skin is the most interacting layer with the environment. Interactions that occur are not always safe against bacteria, viruses, fungi and parasites, so they will cause harm. Poor interactions will result in skin diseases. This skin disease is often considered normal, but can be dangerous and deadly and contagious. Due to the natural conditions and lack of medical personnel, sufferers of skin diseases have problems in examining their skin diseases. So this research was conducted by using an Expert System (Expert System) to help sufferers of skin diseases and help get solutions to these diseases. The solution obtained from the symptoms felt by sufferers. The method used in this study is Forward Chaining (FC) and processed using the Certainty Factor (CF) Method. The results of this study can determine the right initial steps in dealing with infectious skin diseases. So this research is very helpful for sufferers in knowing the type of skin disease, the solution in its prevention and its prevention precisely.


2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Rinanza Zulmy Alhamri ◽  
Abidatul Izzah ◽  
Kunti Eliyen

Skin disease can be suffered by all people and ages as a common disease. In general treatment for people with skin diseases is by consulting to medical experts and asking for a medicine prescription. However, by utilizing current communication technology, sufferers are able to get good information about skin diseases. The expert system for the diagnosis of skin diseases has been done before. However, the expert system is built based on web and desktop, so it is less flexible in use, moreover in supporting self assistance for the skin disease sufferer. Therefore, this study aims to develop an Android-based expert system that can help diagnose skin diseases in humans as well as provide recommendations of generic medicine as first aid. The expert system developed by implementing the Forward Chaining (FC) method as an inference technique. This research was conducted in five phases, namely data collection, analysis system, design system, implementation, and testing. The knowledge base in this expert system includes 39 types of skin diseases, 83 symptoms, 16 types of generic drugs, 150 lines rules of skin disease diagnosis, and 39 lines recommendations of generic drug.


The most unpredictable and difficult terrain in the field of medical diagnosis is dermatology. Dermatological diseases are the most prevalent diseases that one out of three men suffer skin disorder. Regardless of being prevalent , diagnosis of these diseases require more experience in domain. About 90 percent of skin disorders can be cured by primary care. This conveys that the early care for the disease is necessary . This early stage detection can be made easier by computer aided diagnosis system. Diagnostic expert-based computer systems that simulate the diagnostic ability of human body and disease. So we propose Expert system which classify skin diseases based on their appearance and its characteristics. Rather than training every diseases in single image classifier model . We categorize skin disease based on their characteristic and train model separately for each category. This system will filter and cleans data and categorize based on their characteristics. Feature extraction and classification using complex methods such as the convolutional neural network(CNN) and softmax classifier. This system will provide more accuracy , fast and efficient result than traditional method.


Author(s):  
Devi Trishistya ◽  
Poningsih Poningsih ◽  
Widodo Saputra

The purpose of writing this thesis is to build an expert system to diagnose skin diseases. in children and can make it easier for specialist doctors to diagnose skin diseases and provide solutions and information about skin diseases based on symptoms. If something happens to the child, the organ that is first affected is the skin. If there is a skin disease disorder in children, the parents trust the doctor more. But not all doctors can help overcome these problems, so they cannot know the type of disease directly. The method used is backward chaining, i.e. the inference process that starts the search from the premise or input data in the form of symptoms leading to conclusions, namely conclusions of types of diseases and solutions based on symptoms suffered by patients. The results of this study were to determine the patient's illness and its web use.


Author(s):  
Revati Kadu ◽  
U. A. Belorkar

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cheng-Cheng Deng ◽  
Yong-Fei Hu ◽  
Ding-Heng Zhu ◽  
Qing Cheng ◽  
Jing-Jing Gu ◽  
...  

AbstractFibrotic skin disease represents a major global healthcare burden, characterized by fibroblast hyperproliferation and excessive accumulation of extracellular matrix. Fibroblasts are found to be heterogeneous in multiple fibrotic diseases, but fibroblast heterogeneity in fibrotic skin diseases is not well characterized. In this study, we explore fibroblast heterogeneity in keloid, a paradigm of fibrotic skin diseases, by using single-cell RNA-seq. Our results indicate that keloid fibroblasts can be divided into 4 subpopulations: secretory-papillary, secretory-reticular, mesenchymal and pro-inflammatory. Interestingly, the percentage of mesenchymal fibroblast subpopulation is significantly increased in keloid compared to normal scar. Functional studies indicate that mesenchymal fibroblasts are crucial for collagen overexpression in keloid. Increased mesenchymal fibroblast subpopulation is also found in another fibrotic skin disease, scleroderma, suggesting this is a broad mechanism for skin fibrosis. These findings will help us better understand skin fibrotic pathogenesis, and provide potential targets for fibrotic disease therapies.


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