Application of Human Disease Diagnosis System Transmitted by Mosquito Vector Using Forward Chaining Method

2020 ◽  
Vol 8 (4) ◽  
pp. 421
Author(s):  
Kevin Joel Purba ◽  
I Made Widiartha
Keyword(s):  
Skin Disease ◽  
Human Disease ◽  
Skin Diseases ◽  
Disease Diagnosis ◽  
Mosquito Vector ◽  
Internal Organs ◽  
Web Based ◽  
Diagnosis System ◽  
Forward Chaining ◽  
Skin Symptoms

Disease is very easy to find in tropical countries including Indonesia. The skin itself is the most outermost organ that is easily attacked by the skin because the skin itself is an organ that is used to protect the internal organs. Many people do not consider how dangerous the skin disease is if it is not treated. Therefore, it is expected that with the advancement of technology, it can prevent the occurrence of skin diseases that can attack the community. In the research I did, I used the Forward Chaining Method. The system that I created is Web-based, where the system will issue a number of questions in the form of skin symptoms experienced by the user or patient and later when the patient has finished answering the question the system will process the patient's answer and finally the system will issue the answer being experienced by patients.

scholarly journals Implementasi Metode Certainty Factor pada Sistem Pakar Diagnosa Penyakit Ayam Berbasis Android

2019 ◽  
Vol 5 (3) ◽  
pp. 185
Author(s):  
Muqorobin Muqorobin ◽  
Prabowo Budi Utomo ◽  
Muhammad Nafi’Uddin ◽  
Kusrini Kusrini
Keyword(s):  
Expert System ◽  
Mobile Application ◽  
Expert Knowledge ◽  
Disease Diagnosis ◽  
Android Application ◽  
Certainty Factor ◽  
Web Based ◽  
Diagnosis System ◽  
Forward Chaining ◽  
Web Based System

Penelitian ini dilakukan berdasarkan kebutuhan akan adanya alat bantu bagi peternak maupun penyuluh dalam mendiagnosis penyakit pada ayam. Basis pengetahuan sepenuhnya diambil dari pengetahuan pakar yang dapat mendiagnosa dan menentukan nama penyakit yang diderita oleh ayam. Penelitian ini dibuat berdasarkan hasil analisis dengan membandingkan 4 jurnal internasional yang berjudul “Expert system of quail disease diagnosis using forward chaining method, An Expert System for Management of Poultry Diseases, Developling Mobile Expert Web-based System Using Brainstroming Method, Disease Diagnosis System”. Berdasarkan keempat jurnal tersebut dapat dikembangkan inovasi baru berupa Sistem pakar diagnosa penyakit ayam menggunakan metode Certainty Factor. Metodologi penelitian menggunakan wawancara dan studi literature sehigga dapat menggumpulkan data yang lebih lengkap. Sistem Pakar ini akan di implementasikan kedalam sebuah aplikasi mobile berbasis android dengan harapan memudahkan user dalam menggunakan karena dibuat kedalam aplikasi android sehingga bisa di install dalam handphone. Hasil akhir berupa suatu informasi data penyakit dengan nilai Certainty Factor/kepastian.Kata Kunci — Diagnosa Penyakit Ayam, Sistem Pakar, Android, Certainty FectorThis study was conducted based on the need for tools for farmers and extension workers in diagnosingdiseases in chickens. The knowledge base is completely drawn from the expert knowledge that can diagnose and determine the name of the disease suffered by chickens. This study was based on the results of the analysis by comparing four international journals entitled "Expert system of quail disease diagnosis using forward chaining method, An Expert System for Management of Poultry Diseases, Developing Mobile Expert Web-based System Using Brainstroming Method, Disease Diagnosis System". Based on the four journals can be developed a new innovation in the form of expert system of chicken disease diagnosis using Certainty Factor method. The research methodology uses interviews and literature studies so that it can collect more complete data. Expert System will be implemented into an android-based mobile application with the hope of facilitating the user in using because it is made into the android application that can be installed in the mobile phone. The final result is an information of disease data with Certainty Factor value/certainty.Keywords — Diagnosis of Chicken Disease, Expert System, Android, Certainty Fector

scholarly journals Human Facial Disease Diagnosis System Using Dempster Shafer Method

2019 ◽  
Vol 1 (1) ◽  
pp. 6-11
Author(s):  
Ahmad Tri Hidayat ◽  
Suhirman ◽  
Kais Ismail Ibraheem
Keyword(s):  
System Design ◽  
Human Body ◽  
Skin Disease ◽  
Skin Diseases ◽  
Public Awareness ◽  
Disease Diagnosis ◽  
Facial Skin ◽  
Sources Of Information ◽  
Diagnosis System ◽  
Knowledge Based

Skin is a very important and sensitive part of the human body. It is directly related to the outside world. In everyday life, the skin plays an important role. One of its functions is to remove waste substances. The lack of knowledge and limited sources of information causes low public awareness of skin health as well as not seeing a doctor. Regular skin checks can overcome the beginning of skin diseases that might trigger other more dangerous diseases. The system in this study is designed to detect and to know the symptoms of facial skin diseases that are felt. This knowledge-based system design was developed to diagnose human facial skin disease using the dempster shafer method. The results can help detecting earlier facial skin diseases and overcoming them. Key words : diagnosis system, facial skin disease, dempster-shafer

scholarly journals Pengembangan Sistem Pakar Berbasis Android untuk Menentukan Obat Generik pada Penyakit Kulit Menggunakan Metode Forward Chaining

2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Rinanza Zulmy Alhamri ◽  
Abidatul Izzah ◽  
Kunti Eliyen
Keyword(s):  
Expert System ◽  
Skin Disease ◽  
Skin Diseases ◽  
Generic Drugs ◽  
Disease Diagnosis ◽  
Generic Medicine ◽  
Design System ◽  
Common Disease ◽  
Forward Chaining ◽  
Analysis System

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.

scholarly journals Expert System for Diagnosing Human Skin Diseases Using Web-Based Naïve Bayes Method

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Khoirunnisa devita Sari ◽  
Ade Eviyanti
Keyword(s):  
Expert System ◽  
Human Skin ◽  
Skin Disease ◽  
Skin Diseases ◽  
Naive Bayes ◽  
Expert Knowledge ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Web Based ◽  
Naive Bayes Method

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.

scholarly journals Expert System To Detect Human’s Skin Diseases Using Forward Chaining Method Based On Web Mobile

2018 ◽  
Vol 218 ◽  
pp. 02015
Author(s):  
Jenie Sundari ◽  
Hamimah ◽  
Popon Handayani ◽  
Yunita ◽  
Ibnu Dwi Lesmono ◽  
...  
Keyword(s):  
Immune System ◽  
Expert System ◽  
Skin Disease ◽  
Skin Diseases ◽  
Allergic Reactions ◽  
Tropical Country ◽  
Forward Chaining ◽  
Body Resistance ◽  
Sense Of Touch ◽  
Very High

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

scholarly journals Web-Based Heart Disease Diagnosis System With Forward Chaining Method (Case Study Of Ibnu Sina Islamic Hospital)

2019 ◽  
Vol 1 (1) ◽  
pp. 43-50
Author(s):  
Refni Wahyuni ◽  
Yuda Irawan
Keyword(s):  
Heart Disease ◽  
Expert Systems ◽  
Disease Diagnosis ◽  
Public Knowledge ◽  
Public Attention ◽  
Web Based ◽  
Forward Chaining ◽  
Computer Based ◽  
Heart Disease Diagnosis

Expert systems are computer-based applications that are used to solve problems thought by experts. The benefits of expert systems are to find solutions to problems that occur in various fields including medicine and medical. The problem that often occurs is the problem of public knowledge of the initial problems of heart disease and the lack of public attention to heart health. From this problem, the writer wants to raise the title "Expert System of Web-Based Heart Disease Diagnosis with Forward Chaining Method (Case Study of Ibnu Sina Islamic Hospital)". The purpose of this system is so that people can understand, understand the facts of heart disease. The method used in making this system is forward chaining because this method is a punishment starting from the fact that it contains sentences that are received. The output of this system is a diagnosis of heart disease, as well as providing recommendations for how to treat the disease.

scholarly journals Implementasi Sistem Pakar menggunakan Metode Certainty factor Untuk Mendiagnosa Penyakit Herpes Zoster

2020 ◽  
Vol 4 (2) ◽  
pp. 110
Author(s):  
Ikhsan Romli ◽  
E Romansyah ◽  
Andika Permana
Keyword(s):  
Herpes Zoster ◽  
Skin Disease ◽  
Skin Diseases ◽  
Certainty Factor ◽  
Web Based ◽  
Factor Method ◽  
Long Time ◽  
Expert Analysis

Herpes Zoster is a skin disease that is very difficult to treat and everyone can certainly experience it, the characteristics of this disease are characterized by unilateral vesicles in groups with pain characterized by radicular around the dermatome. This study aims to establish a Web-based Certainty factor method as a tool to diagnose skin diseases. With this application, it doesn’t need long time to find out what type of herpes zoster is suffering. To use this application  is that the patient answers the questions which are provided by the system, then the system will process all the patient answers using the certainty factor method, after that, the system will produce output as the results of the diagnosis of the type of shingles. The system built can help patients to know the type of disease that is being suffered by the patients and in accordance with expert analysis of skin diseases.Keywords:Certainty factor, Herpes zoster, web.

scholarly journals ROI-based features for classification of skin diseases using a multi-layer neural network

2021 ◽  
Vol 23 (1) ◽  
pp. 216
Author(s):  
Thanh-Hai Nguyen ◽  
Ba-Viet Ngo
Keyword(s):  
Skin Disease ◽  
Classification Accuracy ◽  
Skin Diseases ◽  
Human Life ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
High Classification Accuracy ◽  
Separate Region ◽  
Occurrence Matrix

<p>Skin diseases have a serious impact on human life and health. This article aims to represent the classification accuracy of skin diseases for supporting the physicians’ correct decision on patients for early treatment. In particular, 100 images in each type of five skin diseases from ISIC database are used for balanced datasets related to the classification accuracy. In addition, this paper focuses on processing images for extracting six optimal types of eleven features of skin disease image for higher classification performance and also this takes less time for training. Therefore, skin disease images are filtered and segmented for separating region of interests (ROIs) before extracting optimal features. First, the skin disease images are processed by normalizing sizes, removing noises, segmenting to separate region of interests (ROIs) showing skin disease signs. Next, a gray-level co-occurrence matrix (GLCM) method is applied for texture analysis to extract eleven features. With the optimal six features chosen, the high classification accuracy of skin diseases is about 92% evaluated using a matrix confusion. The result showed to illustrate the effectiveness of the proposed method. Furthermore, this method can be developed for other medical datasets for supporting in disease diagnosis.</p>

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