Journal of Information Technology and Computer Science Volume 6, Number 1, April 2021, pp. 66-79 Journal Homepage: www.jitecs.ub.ac.id Decision Support System For Diagnosis Types Of Hepatitis Disease With Fuzzy Logic Sugeno Method Dimmas Mulya* 1 , Dian Pratiwi 2 , Is Mardianto 3 1,2,3 Informatics Major, Faculty of Industrial Technology, Trisakti University 1 [email protected], 2 [email protected], 3 [email protected]*Corresponding Author Received 26 July 2020; accepted 06 April 2021 Abstract. In the medical world, there are five types of hepatitis, namely Hepatitis A, B, C, D, and E. However, the five types of hepatitis have similar symptoms, including yellowing of the skin color, yellowing of the white eyeballs, loss of appetite, etc. Thus, many medical personnel often misdiagnose patients for the type of hepatitis or do not suffer from hepatitis. Therefore, previous diagnostic data from medical personnel were collected which would be processed and developed into a Java-based Decision Support System Application using the Expert System method with an output of the percentage of patients likely from each type of hepatitis and the possibility of the patient not suffering from hepatitis. With the output of this system, the probability percentage of Hepatitis A, Hepatitis B, Hepatitis C or other possible diseases can be searched based on the input value of each patient's symptom provided by the system. The results can be seen from the graph that is displayed so that it can help medical personnel to make diagnostic decisions based on the results provided by the application. The accuracy of the application in providing diagnostic decision support is 60%. Keyword : Hepatitis, symptoms, likehood, disease 1. Introduction These advancements in technology and information have made human work easier. Many fields of knowledge find it easy to solve problems in this technological age, such as in the medical fields in the world. With advanced technology today, medical treatment can be carried out faster so that it can increase the possibility of saving lives. But before taking medical treatment, every medical person needs to make a deep diagnosis to find out the type of illness suffered by a patient in order to take appropriate medical treatment. A Medical Personnel sometimes often experiences dilemma in the diagnosis of a patient's illness which causes the wrong medical action given and can end fatal in that patient. This often happens in the process of diagnosing hepatitis that has similar symptoms and even in the initial phase of the disease has blood test results that are similar to creating an incorrect decision on the two diseases. Hepatitis is a disease where inflammation occurs in the liver in the human body. This can be caused by many factors, including bacteria, viruses, and drug side effects.
Transcript
Journal of Information Technology and Computer Science Volume 6, Number 1, April 2021, pp. 66-79
Journal Homepage: www.jitecs.ub.ac.id
Decision Support System For Diagnosis Types Of Hepatitis
Disease With Fuzzy Logic Sugeno Method
Dimmas Mulya*1, Dian Pratiwi2, Is Mardianto3
1,2,3Informatics Major, Faculty of Industrial Technology, Trisakti University
Abstract. In the medical world, there are five types of hepatitis, namely Hepatitis A, B, C, D, and E. However, the five types of hepatitis have similar symptoms, including yellowing of the skin color, yellowing of the white
eyeballs, loss of appetite, etc. Thus, many medical personnel often misdiagnose patients for the type of hepatitis or do not suffer from hepatitis. Therefore,
previous diagnostic data from medical personnel were collected which would be processed and developed into a Java-based Decision Support System
Application using the Expert System method with an output of the percentage of patients likely from each type of hepatitis and the possibility of the patient not suffering from hepatitis. With the output of this system, the probability percentage of Hepatitis A, Hepatitis B, Hepatitis C or other possible diseases
can be searched based on the input value of each patient's symptom provided by the system. The results can be seen from the graph that is displayed so that it can help medical personnel to make diagnostic decisions based on the results
provided by the application. The accuracy of the application in providing diagnostic decision support is 60%. Keyword : Hepatitis, symptoms, likehood, disease
1. Introduction These advancements in technology and information have made human work
easier. Many fields of knowledge find it easy to solve problems in this technological
age, such as in the medical fields in the world. With advanced technology today,
medical treatment can be carried out faster so that it can increase the possibility of
saving lives. But before taking medical treatment, every medical person needs to
make a deep diagnosis to find out the type of illness suffered by a patient in order to
take appropriate medical treatment.
A Medical Personnel sometimes often experiences dilemma in the diagnosis of a
patient's illness which causes the wrong medical action given and can end fatal in that
patient. This often happens in the process of diagnosing hepatitis that has similar
symptoms and even in the initial phase of the disease has blood test results that are
similar to creating an incorrect decision on the two diseases.
Hepatitis is a disease where inflammation occurs in the liver in the human body.
This can be caused by many factors, including bacteria, viruses, and drug side effects.
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[1], [2]
According to Esther Monica, viral hepatitis is a liver infection caused by a virus
accompanied by destruction of liver cells and swelling of the liver, creating a series of
clinical changes. Hepatitis virus is a term used in the medical world for liver
infections caused by viruses.
Then according to Brunner & Sudarth, the striking similarities of all types of
hepatitis are in the early phases, namely the symptoms of symptoms include
yellowing of the skin color, yellowing of the eyeball, loss of appetite, etc. [1], [2], [3]
So that many of the Medical Personnel have made a mistake in diagnosing the type of
hepatitis suffered by the patient.
Many successful diagnostic data for hepatitis are stored in every hospital's
archive. But unfortunately of the many files stored, it is rarely used to analyze so that
the disease diagnosis process can produce a more accurate output. Whereas with the
available data, it can make every Medical Worker perform a better and more accurate
diagnosis to provide appropriate follow-up so that the greater the chance that the
patient will survive.
Therefore, the researcher wants to develop an application to support medical
personnel to make more accurate decisions in the process of diagnosing a patient's
disease based on previous diagnostic data by using the Decision Support System
Application in order to provide various alternatives for medical personnel. The
method used to support Decision Making Support Systems is an expert system
method and Sugeno fuzzy logic. [3], [4] This is because the expert system is a branch
of the Artificial Intelligence (AI) discipline, where a system can think like an expert
based on knowledge owned [5], [6] So that it can help Decision Support Support
Systems to provide alternatives to a problem as an expert provides suggestions and
opinions that support for making decisions. 2. Literature Review 2.1 Previous Resaerch
In this final project, researchers use information from previous research as a guide and also a comparison, both in terms of strengths and weaknesses that already exist. Previous research that researchers took as a guideline is Research made by:
Totok Mulyono in a journal published in January 2010 with the title "Aplikasi Sistem Pakar Untuk Mendiagnosa Jenis-Jenis Penyakit Hepatitis Pada Manusia". This research uses the forward-chaining method for the reasoning method. The result of this study are to provide early hepatitis diagnosis based on symptoms and solutions to deal with the disease.
Ahmad Ramdhani, R. Rizal Isnanto, and Ike Pertiwi in a journal published in January 2015 entitled "Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor". This research uses the Certainty Factor method. The result of this research is an application that can diagnose the type of hepatitis suffered by a patient with input in the form of symptoms and blood tests and provides solutions that the patient must do.
Achmad Igaz Falatehan, Nurul Hidayat, and Komang Candra Brata in a journal published in August 2018 entitled "Sistem Pakar Diagnosis Hati Menggunakan Metode Fuzzy Tsukamoto Berbasis Android". This study uses the Tsukamoto fuzzy logic method. The result of this research is an android application that can diagnose liver disease based on symptom input.
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Parlindungan Sihaloho and Wasit Ginting in a journal published in June 2017 entitled "Diagnosa Penyakit Hepatitis Menggunakan Metode Weighted Product". This research uses weighted product method. The result of this research is an application that can detect the type of hepatitis based on the symptoms suffered by the patient based on the level of the relationship between the symptoms and the type of hepatitis and the treatment solution.
Ibrahim Mailafiya and Fatima Isiaka in a journal published in March 2013 entitled “Expert System for Diagnosis Hepatitis B”. This research uses Mamdani Interference Method. The result of this research is a java application that can detect hepatitis b based on the symptoms suffered by the patient and the results of the diagnosis of this application will help doctors make decisions in the diagnostic process.
C. Mahesh, K. Kiruthika, M. Dhilsathfathima in a journal published in 2014 entitled “Diagnosing hepatitis B using artificial neural network based expert system”. This research uses ANN method. The result of this research is an application that can study the pattern of each use to diagnose hepatitis b in patients, so that this system will get better as it goes on.
The studies above contain about making an Expert System Application that can
diagnose the type of hepatitis that is suffered by patients based on the symptoms experienced by these patients. Each of these studies uses different methods to determine disguises and uncertainties, but these studies use the same reasoning method, namely forward-chaining.
In this study, researchers will use the Sugeno Fuzzy Logic Method. Why use Sugeno Fuzzy Logic Method? Based on research journals from Wahyuni Eka Sari, Oyas Wahyunggoro, and Silmi Fauziati entitled "A Comparative Study on Fuzzy Mamdani-Sugeno-Tsukamoto for the Childhood Tuberculosis Diagnosis". In that study explained that of the three fuzzy logic methods, the Sugeno method produces the best accuracy because the Sugeno method has advantages in interpreting the rules for computing fuzzy inference and defuzzification than the Mamdani and Tsukamoto methods.
2.2 Hepatitis
Commonly this disease is known as jaundice because it causes a yellow color on the nails, skin, and the whites of the eyeball. The term hepatitis comes from the Latin word hepar which means liver and affix -itis which means inflammation. So it can be interpreted, hepatitis is inflammation of the liver where bacteria, viruses or drug side effects are the cause. Inflammation causes damage to liver cells and will lead to disease complications and even death. There are many types of hepatitis that have been discovered and studied by medical experts, but in Indonesia, cases that often occur are cases of hepatitis A, B and C. [1], [2]
2.3 Expert System
Expert System or Expert System is one of the pieces of knowledge from Artificial Intelligence or artificial intelligence. Expert systems work by using a set of knowledge in accordance with the scope of the given problem, therefore expert systems are also commonly referred to as knowledge-based systems. The series of knowledge used was none other than taken from relevant experts. So it can be said that the expert system is directed to imitate all aspects controlled by an expert so that they can make decisions like an expert [5], [6]
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2.4 Forward Chaining
Forward Chaining Method is a computational model that processes from top to bottom. The process begins by providing input in the form of a set of existing facts and applying these facts to several rules, which then produces new facts and these new facts are reapplied into several rules. Then this process will continue until it reaches a predetermined goal or until no further facts can be obtained. [6], [7]
So if we put it in the concept of a conditional statement, all known fact information will be processed in accordance with the IF statement, then the results from THEN will provide new facts which will then be included in the next IF statement. This process will run until it reaches the goal or no new facts can be generated. [6], [7]
We can take an example in the case of the way a medical expert diagnoses a patient's illness. The first thing that is asked is the symptoms that occur in the patient (in this case the symptoms are considered as facts), then from the facts that have been obtained by medical experts processed and medical experts can draw reasonable conclusions or build hypotheses based on symptoms that are suffered by patients in order to examine deeper. [7] For a clearer explanation about the algorithm, you can see the image below. [8]
Fuzzy Logic or fuzzy logic was first introduced by Prof. Lotfi A. Zadeh in 1965. The core process of fuzzy processing lies in the fuzzy set theory. In theory, the fuzzy set is obtained from the set of degrees of membership of each variable. The set of degrees of membership itself is obtained from fact input which is then processed with a membership degree graph. [9] According to Kusumadewi and Purnomo, the value of the set of degrees of membership becomes an important thing from the process of reasoning using fuzzy logic..
The degree of membership owned by fuzzy logic has a value between 0 to 1. However, its concept differs from boolean logic or True or False logic which only has a value of 1 or value 0. Fuzzy logic is usually used as a translator of a quantity expressed by linguistic not numeric, for example, a good quantity of game is expressed with ‘very well’, ‘pretty good’, ‘ordinary’, ‘pretty bad’, and ‘very bad’. And fuzzy logic can show the amount of how far it has the right value and how far it has the wrong value. [9] According to Kusumadewi and Purnomo, fuzzy is expressed in the degree of membership and the degree of truth. Therefore something can be said to be partly right and partly wrong at the same time.
There are 3 types of Fuzzy Logic, namelypFuzzy LogicpTsukamotopmethod, Fuzzy Logic Mamdani method, dan FuzzypLogic Sugeno method. [9]
The difference between the three methods of Fuzzy Logic lies in the process of
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inference and defuzzication. In the Tsukamoto Method, the inference process (fuzzy inference system) uses the Min-Max method, and the Heigh Method defuzzification process. Then, in the Mamdani Method, the inference process uses the Min-Max method, and the Center of Weight defuzzification process Then, in the Sugeno Method, the inference process uses the Min-Max method; Order 0; Order 1, and the Weight Average defuzzification process. [10]
Then what distinguishes fuzzy logic from boolean logic? The point of difference between the two logic is the output given. In boolean logic, the output is only true or false, while in fuzzy logic, the output ranges from 0 to 1, thus making the output of the program more varied. [11]
2.6 Decision Support System
Decision Support System or Decision Support System is a fraction of the expert
system discipline, because it has a close relationship in the use of knowledge based. Supporting decisions itself means helping people who work alone or together to collect anxiety, produce alternatives and make choices. In the process of supporting choice making, it involves supporting estimates, evaluations and / or comparisons between alternatives. [4,12] 3. Research Method 3.1 Research Methodology
Research Methodology Final Project is carried out by researchers through several stages. In the first stage, the researcher conducted a literature study regarding previous research that had discussed the methods of expert systems and decision support systems in detecting types of disease in humans. A literature study was also conducted to find out about Hepatitis as an object of research to create a decision support system for the type of Hepatitis in humans. After understanding the concept of the system and the types of methods in expert systems and decision support systems as well as the required data forms, the Researcher takes steps to collect the necessary symptom data and the Medical Experts as a knowledge base of the expert system the researcher wants to develop.
From the results of the symptom data obtained by the researcher from reliable medical information sources such as medical sites and medical research journals, the researcher contacted a medical expert to validate the data that had been obtained so that it could be processed better and could be more reliable. In the process of data validation, the researcher also raised several important things related to the process of making rules and decision trees which became the knowledge base in the expert system to provide decision support for medical personnel.
Then, after obtaining the symptom data, rules and decision tree, the researcher re-analyzes the expert system method according to the form of the data obtained and conducts a literature study again to determine the best method of the methods that can be used. Then the researcher does the design and modeling for the application in accordance with the system requirements analysis, the features required, as well as the expert system method that has been determined by the Waterfall software development method. In this software development stage, the Researcher contacts the Medical Specialist in the process to ensure that the development features implemented remain within the medical scope. Such as modeling the input of each symptom value and the output that will be displayed to provide decision support to medical personnel. Researchers also use the MATLAB application as a comparison of the correctness of the results in the calculation of the fuzzy inference system issued by the system with
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calculations from the MATLAB application. Furthermore, at the trial stage, the Researcher contacted the Medical Specialist
again to find out the level of accuracy of the system that the Researcher had successfully developed. At this stage, the researcher takes a test case accompanied by a medical expert so that he can create a collection of test cases for the system. From this stage, the level of accuracy is based on the amount of conformity of the system output with the results of the Medical Expert's diagnosis.
For a description of the research methodology carried out by the researcher, it can be seen in the flowchart in Fig. 3.
3.2 Method of Collecting Data
Researchers have not had the opportunity yet to collect the data needed from relevant agencies due to large-scale social restrictions in accordance with the circular from the Government to reduce the increase in the spread of Covid-19. Then the researchers also did not find a data set that was consistent with this study on the internet. Therefore, to overcome this, researchers look for alternatives using data on the symptoms of each type of hepatitis from previous research journals with a theoretical basis for hepatitis symptoms from various electronic books, medical journals, and trusted medical field sites.
The data used in this study are symptoms of each type of hepatitis. Based on Totok Mulyono's research in a journal published in January 2010 with the title "Aplikasi Sistem Pakar Untuk Mendiagnosa Jenis-Jenis Penyakit Hepatitis Pada Manusia". In the journal there is a table that explains the symptoms of each type of hepatitis. Then the researchers reworked the data table with various information obtained from trusted sites and other medical journals along with researcher's friend, Juan Alexsandra Prasetyo, who studied at the Faculty of Medicine at Atma Jaya University in Jakarta and his lecturer named Prof. dr. Suwandhi Widjaja, Sp.PD, Ph.D. Resulting in a table like the following.
For a description of the research methodology carried out by the researcher, it can be seen in the flowchart in Table. 1.
Table. 1. Table of Symptoms of Each Type of Hepatitis
The decision tree modeling used in the system is designed together with
medical experts so that the direction of reasoning from one fact to the next does not go
in the wrong direction. Based on recommendations from Medical Experts, here is a
picture of the decision tree used in this Decision Support System.
Fig. 3. Decision Tree
3.4 Rules Model
The modeling rules used in the system are designed together with the Medical
Experts so that in the process of reasoning the percentage of each disease can produce the correct value and in accordance with the medical expert's own analysis process. Based on the recommendations from Medical Experts, there are nine rules that are used in this Decision Support System.
Fig. 4. Nine Rules which used in System
On Fig. 4. shows the nine rules that are used in this Decision Support System.
The medical expert explained that the nine rules were obtained from a combination of nine scenarios to determine the value of the Fuzzy Inference System calculation from
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the two symptom values. The scenario combination comes from the possible input values of the two symptoms.
Then a set of rules is used for each stage of the Decision Tree when combining the values of the two symptoms and the symptom values with the values from the previous merging process.
3.5 Fuzzy Inference System
The process in the fuzzy inference system has 5 stages, namely Fuzzification, Fuzzy Logic Operation, Implication, Aggregation, and Defuzzification. [26]
Fuzzification is the process of mapping numerical values into fuzzy sets and determining the degree of membership in the fuzzy set. This is done because the data is processed based on fuzzy set theory so that data that is not in fuzzy form must be converted into fuzzy form. In this application, the answer data from medical personnel are input 0 to 2. [26]
Fig. 5. Graph to determine membership degrees.
Fig. 5. Shows a graph of the degree of membership where there is a division of three regions, namely no, maybe and yes. Data that is not yet in the form of fuzzy will be fuzzified based on the graph, so that one value entered into the fuzzification process can produce three values categorized based on the three lines on the graph. Each result is collected into a set of membership degrees.
After getting the set of membership degrees, then enter the fuzzy logic operation stage. In this stage, the set of membership degrees is processed according to the rules that have been previously determined. Rules are taken based on the experience of experts in related fields. Then the fuzzy logic operation is processed, that is, if the rule states and then the minimum membership degree set value is taken, but if the rule states or the maximum membership degree set value is taken. [26]
Then go to the implication stage. In this stage, there is a process to get the output of the rules using the minimum function. Then, enter the aggregation stage. In this stage, if there is more than one fuzzy rule being evaluated, the output of all rules is combined into a single fuzzy set. The aggregation method used is max or or against all output rules. This is where the min-max inference method in the Sugeno fuzzy logic method is run. [26]
After successfully getting the results from the min-max inference method, the last step to take is the defuzzification stage. Defuzzification is the process of mapping the quantities from a fuzzy set into numerical values, because the system used is set into real quantities, not fuzzy quantities. In the Sugeno fuzzy logic method, the defuzzification method used is the weight average. [26]
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3.6 Input and Output System Model
The input and output modeling used in the system is designed in conjunction
with Medical Experts to fit within the scope of the medical field. The input model used in this system is the presence value of 14 symptoms sourced from trusted medical sites and medical journals and has been validated by the Medical Expert himself.
Table. 2. Information For Each Symptoms
Symtomps Explanation
Yellowing Skin The skin color of the sufferer starts to turn yellow
Yellowing Eyes The whites of the eyes begin to appear yellowish patches, evenly or unevenly
Nausea Sufferers feel nauseous
Vomiting The patient experiences vomiting
Loss of Appetite Sufferers experience loss of appetite
Fever The patient has a fever
Color of Feces Looks Like Old Mango The stool of the patient will be colored like an old mango
Fatigue Patients will feel tired easily
Myalgia The sufferer feels pain in the muscles
Arthralgia The sufferer feels pain in the joints
Spider Angioma Sufferers show a collection of blood vessels that are not
normal on the surface of the skin
Rash Sufferers experience rashes - rashes on the skin
Headache Patients often experience headaches
Limp The sufferer feels weak
In Table. 2. explain the description of each symptom that will be provided by
the system based on the explanation from the Medical Expert. Input the value of each
symptom shown in the Table. 1. Has input options namely Absent, Not Sure, and
Present. The three options are used in the input process of the 14 symptom inputs. The
value of each option is 0 for the Absent option, 1 for the Not Sure option, and 2 for
the Present option. This means to describe the presence of symptoms suffered by the
patient based on the results of the conversation between the patient and the medical
personnel which are then input into the system by the medical personnel themselves.
The forms of choice of Absent, Not Sure, and Present for each symptom were selected
based on the recommendation of a Medical Expert.
Then the output model of the system has 4 forms, namely Percentage of
Possible Hepatitis A, Possible Percentage of Hepatitis B, Possible Percentage of
Hepatitis C, and Percentage of Possible Other Diseases. The percentage of other
diseases is included based on the recommendation of Medical Experts because the
possibility of a disease other than Hepatitis still exists even though the symptoms
provided by the system are those of Hepatitis. Then there is an output model which
also displays the percentage of the four outputs in graphical form, in order to make it
easier for medical personnel to see the highest possibility of the type of disease a
patient is suffering from.
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4. Result and Discussion The flow of calculations in this system goes through several stages. The first
stage is done by taking the input value of each symptom provided by the system. Then each symptom is taken its membership degree based on the process that has been described in the Fuzzy Inference System process stage. Then the Fuzzy Logic Operation is carried out on the first two symptoms based on the rules that have been previously designed. Then proceed to the next Fuzzy Inference System stage to completion. This stage is carried out based on the map that has been described in the Decision Tree.
After getting the value of the possibility of hepatitis from the main symptoms of hepatitis, namely yellowing skin, yellowing of the white eyeball, nausea. This value is used to determine the percentage of Chance of Hepatitis as well as Other Possible Diseases.
The percentage value of the Likelihood of Other Diseases is obtained by calculating the difference from the Hepatitis Likelihood Value with the highest value that can be generated by the system (in the case of this System, the highest value is 2), then divided by the highest value that can be generated and then multiplied by 100%. Similar to the process for the Hepatitis Likelihood percentage value, the Hepatitis Likelihood value is divided by the highest value that can be generated and then multiplied by 100%. So that the percentage value of the possibility of hepatitis and the possibility of other diseases if added together will produce 100%.
The value of the probability of Hepatitis A, the probability of Hepatitis B, and the probability of Hepatitis C are obtained from the Fuzzy Inference System process for each symptom based on the rules and decision tree that have been designed previously. Then the percentage value of each Hepatitis Likelihood Value is obtained by forming a comparison of each Hepatitis Likelihood Value. Then divide the results of that comparison multiplied by the Chance percentage of Hepatitis. So that the percentage value of each type of Hepatitis can be obtained within the range of the percentage of the probability of Hepatitis percentage.
The results of this application are done by taking a random sample of symptoms of hepatitis and other diseases. Data sampling was assisted by fellow researchers from the medical faculty. Basically, the success of building an expert system is measured by its accuracy in providing predictions or diagnoses with real data. Therefore, researchers took data samples to test the accuracy and level of predictions from the expert system that had been made. After the sample data from various scenarios have been collected, it is then converted into the form of input indicators for each symptom. In this study, researchers took 50 sample scenarios as shown in Table 3.
In Table 3., it can be seen that the predicted rates of application to provide support to Medical Personnel of any possible Hepatitis A, possible Hepatitis B, possible Hepatitis C, possibility of Other Diseases. Taking into account the predictions of each scenario, then the average of the predictions is calculated based on each possibility.
Then in Table 4:33, it can be seen that the application accuracy rate for providing support to medical personnel is 62%. By comparing the results of the Medical Expert's diagnosis with the output of the application, then the exact number is
calculated divided by the total number of cases. This can occur due to a lack of symptom data needed in the study, resulting in a generalization of the indicator value of each symptom. Then the similarity of symptoms between each type of Hepatitis can lead to close proportions, for example, the symptoms of Hepatitis A have some
similarities to the symptoms of Hepatitis B, as well as between Hepatitis A and Hepatitis C, between Hepatitis B and Hepatitis C and the three.
In Table. 3., it can be seen that the predicted rates of application to provide
support to Medical Personnel of any possible Hepatitis A, possible Hepatitis B,
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possible Hepatitis C, possibility of Other Diseases. Taking into account the predictions of each scenario, then the average of the predictions is calculated based on each possibility. Then in Table. 3., it can be seen that the application accuracy rate for providing support to medical personnel is 62%. By comparing the results of the Medical Expert's diagnosis with the output of the application, then the exact number is calculated divided by the total number of cases. This can occur due to a lack of symptom data needed in the study, resulting in a generalization of the indicator value of each symptom. Then the similarity of symptoms between each type of Hepatitis can lead to close proportions, for example, the symptoms of Hepatitis A have some similarities to the symptoms of Hepatitis B, as well as between Hepatitis A and Hepatitis C, between Hepatitis B and Hepatitis C and the three.
5. Conclusion Based on the application that has been successfully implemented and has
successfully gone through the trial process, the following conclusions can be drawn: a. This decision support system was developed by collecting symptom data from
each type of hepatitis and going through a validation process with medical experts which was then formulated into a knowledge base for this application. The method used is Sugeno method of Fuzzy Logic.
b. The system was developed by going through case trials by entering 50 data and seeing the results whether it was in accordance with the symptom table and the diagnosis result of a Medical Expert. The level of accuracy compared to the opinion of medical experts is 62%.
Acknowledgments. Thank for author’s two Supervisors, namely Dian Pratiwi, ST,
MTI and Is Mardianto, S.Si, M.Kom who have taken the time to guide me in working
on this paper, then my parents who have provided support and encouragement not to
give up as well as my friends who have helped provide support.
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