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

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.

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

MATEC Web of Conferences ◽

10.1051/matecconf/201821802015 ◽

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

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Akurasi dalam Mendeteksi Penyakit Kulit Menular menggunakan Gabungan Metode Forward Chaining dengan Certainty Factor

Jurnal Informasi & Teknologi ◽

10.37034/jidt.v2i2.58 ◽

2020 ◽

pp. 48-55

Author(s):

Deosa Putra Caniago ◽

Sumijan

Keyword(s):

Expert System ◽

Skin Disease ◽

Skin Diseases ◽

Medical Personnel ◽

Certainty Factor ◽

Natural Conditions ◽

Forward Chaining ◽

The Right ◽

Infectious Skin Diseases

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.

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

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v08.i04.p08 ◽

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.

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Aplikasi Sistem Pakar Diagnosa Penyakit Pada Anak Bawah Lima Tahun Menggunakan Metode Forward Chaining

Journal of Information Systems Engineering and Business Intelligence ◽

10.20473/jisebi.3.1.61-67 ◽

2017 ◽

Vol 3 (1) ◽

pp. 61 ◽

Cited By ~ 1

Author(s):

Bagus Fery Yanto ◽

Indah Werdiningsih ◽

Endah Purwanti

Keyword(s):

Expert System ◽

System Application ◽

Disease Diagnosis ◽

Information Collection ◽

Forward Chaining ◽

Second Stage ◽

Children Health ◽

Third Stage ◽

Testing Data ◽

Three Stages

Abstrak— Anak-anak pada usia 2 bulan sampai 5 tahun (Balita) lebih rentan terkena penyakit. Lingkungan sangat mempengaruhi kesehatan Balita. Penelitian ini bertujuan untuk membuat sebuah aplikasi sistem pakar diagnosa penyakit pada Balita berbasis mobile. Penelitian ini terdiri dari tiga tahap. Tahap pertama adalah pengumpulan data dan informasi dari Manajemen Terpadu Balita Sakit (MTBS) dan wawancara dengan Bidan. Dari pengumpulan data dan informasi tersebut ditemukan fakta penyakit, keluhan, gejala dan saran penanganan. Tahap kedua adalah pembuatan rule dengan 18 penyakit. Tahap ketiga adalah implementasi aplikasi sistem pakar berbasis mobile dengan fitur diagnosa penyakit, riwayat diagnosa dan kumpulan penyakit. Aplikasi sistem pakar yang dibuat dapat mendiagnosa penyakit dan memberikan saran penanganan. Hasil evaluasi dari 50 data uji coba menghasilkan tingkat akurasi sebesar 82%, dimana 41 hasil diagnosa yang benar dan 9 diagnosa yang salah. Kata Kunci— Sistem Pakar, Forward Chaining, Diagnosa Penyakit, Manajemen Terpadu Balita Sakit, Knowladge BaseAbstract— Children at the age of 2 months to 5 years (toddlers) are more susceptible to disease contagious. Environmental condition significantly influences the children health. This research aimed to create a mobile-based expert system application to diagnose disease in toddlers. This research consist of three stages. The first stage were data and information collection from Manajemen Terpadu Balita Sakit (MTBS) and interview with medical staffs. From the first stage, we can discover the disease facts, signs, symptoms and treatment advices. The second stage was the construction of rules for 18 diseases. The third stage was the implementation of mobile-based expert system application with features of disease diagnosis, diagnosis history and collection of disease diagnosis. Expert system application made able to diagnose the disease and provide treatment advice. The results of evaluation using 50 testing data provides the level of accuracy of 82%, where 41 diagnosis result were true and 9 diagnosis were false. Keywords— Expert System, Forward Chaining, Disease Diagnosis, Manajemen Terpadu Balita Sakit, Knowledge Base

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Perancangan Sistem Diagnosa Penyakit Campak Dengan Menggunakan Certainty Factor

Creative Information Technology Journal ◽

10.24076/citec.2019v6i2.245 ◽

2021 ◽

Vol 6 (2) ◽

pp. 96

Author(s):

I Wayan Rangga Pinastawa ◽

Ema Utami ◽

M. Rudyanto Arief

Keyword(s):

Expert System ◽

System Design ◽

Disease Diagnosis ◽

Design System ◽

Rule Base ◽

Certainty Factor ◽

Activity Diagram ◽

System Disease ◽

Engine Design ◽

Output System

Penyakit campak merupakan salah satu penyakit menular yang masih menjadi masalah kesehatan di Indonesia, karena sering dilaporkan dibeberapa daerah. Menurut data IDAI insiden campak pada balita sebesar 582/10.000. Metode yang digunakan adalah metode Certainty Factor atau Metode Kepastian. Tujuan penelitian ini adalah membuat perangkat lunak sistem pakar yang diharapkan dapat membantu masyarakat dalam mendiagnosa jenis penyakit campak dan rubella. Perangkat lunak sistem pakar ini meliputi analisis kebutuhan user, analisis kebutuhan sistem dan perancangan rekayasa pengetahuan dimana dalam pembuatan rekayasa perangkat lunak ini, data yang terkumpul direpresentasikan sebagai basis pengetahuan keputusan, basis aturan dan perancangan mesin inferensi, selanjutnya perancangan sistem, yang merancang pembuatan pemodelan proses yang terdiri usecase dan activity diagram, pemodelan data yang terdiri dari perancangan table, pengembangan proses selanjutnya adalah implementasi menggunakan berbasis website. Hasil penelitian berupa program aplikasi sistem pakar yang mampu mendiagnosa penyakit Campak. Keluaran sistem berupa hasil diagnosis meliputi tentang penyebab penyakit, penularan penyakit, pencegahan penyakit dan solusi penyakit campak pada anak. Dan juga dilengkapai dengan MB, MD dan nilai C.F yang diperoleh dengan perhitungan menggunakan metode Certainty Faktor.Kata Kunci— Diagnosa, Campak, Rubella, Certainty FactorMeasles is a contagious disease that is still a health problem in Indonesia , as often reported in some areas . According to data SKR (1996 ) the incidence of measles in infants at 582/10.000. The method used is the Certainty Factor method or methods Kepastian.Tujuan this research is to create a software expert system that is expected to assist the community in diagnosing type measles expert system software include user needs analysis, requirements analysis and system design engineering knowledge which in pmbuatan this software engineering, data collected represented as a knowledge base, decision, rule base and inference engine design, system design further, the design creation process use case and activity diagram, data modeling which consists of designing Table Mapping, designing go round the table next process inplementasi development using website based. The results in the form of an expert system application program that is able to diagnose diseases such as measles output system disease diagnosis include the value of MB and CF values obtained by calculation using the method of Certainty factors, causes and solutions.Keywords—Diagnosis, Measles, Rubella, Certainty Factor

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DEMPSTER SHAFER'S ROLE IN THE EXPERT SYSTEM IN DIAGNOSING PET SKIN DISEASES

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v6i1.216 ◽

2019 ◽

Vol 6 (1) ◽

pp. 107

Author(s):

Gita Malinda ◽

Andi Farmadi ◽

Muliadi Aziz

Keyword(s):

Expert System ◽

Skin Disease ◽

Skin Diseases ◽

Early Prevention ◽

Confidence Value

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.Keywords: Cats, Expert System, Dempster Shafer.Kucing merupakan hewan peliharaan yang sangat dekat dengan manusia.. Penyakit hewan peliharaan yang menular terkadang dapat menyebar secara cepat dan dapat berakibat fatal, baik pada hewan dan manusia. 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 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.Kata kunci: Kucing, Sistem Pakar, Dempster Shafer.

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

Procedia of Engineering and Life Science ◽

10.21070/pels.v1i2.1021 ◽

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.

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SISTEM PAKAR BERBASIS LOGIKA FUZZY UNTUK DETEKSI LEVEL PENYAKIT SCABIES

CCIT Journal ◽

10.33050/ccit.v10i2.536 ◽

2017 ◽

Vol 10 (2) ◽

pp. 186-196

Author(s):

Muhamad Safi’i ◽

Hanny Haryanto

Keyword(s):

Expert System ◽

Expert Systems ◽

Skin Disease ◽

Direct Contact ◽

Disease Diagnosis ◽

The Body ◽

Infected Person ◽

History Of ◽

Short Time

Scabies is a skin disease that is easily transmitted, either through direct contact or through intermediaries such as clothes, bed sheet, and water. The disease causes itching on the body, especially at night. Transmission of the scabies disease is very fast and can develop into an outbreak in a community. Obstacles that occur in the treatment of this disease is not all people with the disease could check it for a variety of reasons, such as too busy with work and economic limitations. This research using fuzzy Tsukamoto to develop an expert system for the detection of scabiesdisease. The use of expert systems is very important because it can be a tool to substitute the expert, the expert system is easy to use, and can make decisions in a short time. Scabies disease diagnosis process carried out by way of input symptoms that suffered by patients. Symptoms that are used as input is a scratchy, itchy location, number of lesions or spots, and an extensive history of itching or the number of infected person. The outcome of this process is the level of scabies from mild to severe

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Sistem Pakar Diagnosa Penyakit Kulit Akibat Virus Menggunakan Teorema Bayes

Telematika ◽

10.31315/telematika.v15i2.3128 ◽

2018 ◽

Vol 15 (2) ◽

pp. 117

Author(s):

Putri Taqwa Prasetyaningrum ◽

Nuri Budi Hangesti

Keyword(s):

Expert System ◽

Skin Disease ◽

Skin Diseases ◽

Bayes Theorem ◽

Existing Problems

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.

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i1.pp216-228 ◽

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

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.

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