Expert System for Selecting College Majors Based on Interests and Talents Using the Certainty Factor (CF) Method

An expert system is a system that adopts the expertise of an expert in a particular field into the system. Rabbits are one of the animals that are close to humans and have high body adaptability. However, rabbits are not free from the threat of disease. Treatment requires more costs so we need a way to find out the disease and the solution so that we can take the necessary actions. To overcome this, an expertise media was created that can be accessed by rabbit owners online using the Forward Chaining and Certainty Factor methods. The expert system must be able to work under uncertainty, so the Forward Chaining (FC) and Certainty Factor (CF) methods were added to overcome this problem. Forward Chaining is a decision making method that is commonly used in expert systems and the Certainty factor method is a method to prove the uncertainty of an expert's thinking. The system diagnosis results display possible diseases suffered by rabbits and provide treatment solutions for diseases with the largest CF values. With the calculation results, the level of confidence information based on the table is Definitely Yes. The accuracy of the system output is proven from the results of system validity with experts.

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

Covid-19 is a new type of virus that is currently a pandemic in almost all countries. The limitations of experts for diagnosis and recommending rapid action to patients who are diagnosed positively, as well as the limitations of the expert area in the environment wh ere the Covid-19 outbreak occurred. This expert system aims as an early diagnosis system for Covid-19 so that patients can receive treatment immediately, and help stop the wider spread of the virus. Using predetermined parameters and there is also a system that can later make it easier for users to find out the diagnosis of Covid-19 caused by the coronavirus. The method used for this expert system research is Certainty Factor (CF). This method aims to showed how much the confidence value is based on the clinical parameters given by MYCI. The advantage of the Certainty Factor method is that it can be measured something that is certain or uncertain. And to maintain accuracy, Certainty Factor can only process two data. The Expert System uses the Certainty Factor (CF) method to diagnose Covid-19 disease, starting with the collection of symptom data and giving confidence values by the expert on each symptom, then the symptom data input stage from the user and the system will calculate based on the Certainty Factor rule, multiplying the weight value namely user CF and expert CF, combining the results of the multiplication of each symptom, so that a percentage of confidence is obtained as the final diagnosis result. After calculating the accuracy test of the 3 rule system with 99% positive Covid-19 results, 48% most likely Covid-19 and 29% negative Covid-19.

Student health directly affects the quality of learning and academic achievement. When sleep patterns and daily routines become irregular, students face a higher probability of developing health-related problems. To address this issue, a web-based expert system was designed and developed to detect student health risks based on sleep patterns and daily activities, applying the Certainty Factor (CF) method, with a case study conducted at STIE BPKP. The Certainty Factor method was used to quantify the degree of confidence in a particular health risk by combining symptom data selected by users with certainty weights assigned by domain experts. The system was built as a web application featuring three core functions: symptom data input, diagnostic processing, and output presentation in the form of health risk levels along with actionable recommendations. Functional testing was carried out to verify that each module operated correctly and that diagnostic outputs aligned with the expected Certainty Factor calculations. Findings from this study confirm that the expert system successfully assists students in assessing their health risk levels in a fast and accessible manner. Beyond individual use, the system also serves as a supporting instrument for academic institutions seeking to strengthen health awareness programs among their student population. As a result, this expert system holds practical value as an early-warning tool for student health risk detection.

The objectives of this research are; 1) Creating Android software or applications using the Certainty Factor method, and 2) Early diagnosis of a person's indications using the Certainty Factor (CF) method. The research methodology uses interviews, collects information obtained, the next stage is used with the Certainty Method used to calculate the probability value of MD, MB and CF, researchers to display the diagnosis of Lupus, testing techniques using Acceptance Testing techniques. The results of the study have succeeded in making an application using the JAVA programming language assisted by the Android SDK Platform. The results of the disease obtained a confidence value indication of Dry Eyes, Shortness of Breath due to Lung Inflammation, Continuous Chest Pain for Lupus is 0.968815.

Depression is a growing mental health concern among university students, often fueled by academic pressure, social demands, and personal stress. This study presents the development of an expert system using the Certainty Factor (CF) method to diagnose depression specifically among students at Ngudi Waluyo University. The system categorizes depression into mild, moderate, and severe levels based on 12 validated symptom statements and expert-defined diagnostic rules. Implemented with PHP, JavaScript, and CSS, the system offers a user-friendly, accessible, and anonymous platform for self-assessment. Testing yielded an accuracy rate of up to 79% in diagnosing depression severity and a 71.7% user satisfaction rate based on a User Acceptance Test (UAT) involving 32 students. Results demonstrate that the system can effectively support early detection and mental health awareness within academic environments. Despite some limitations in UI and feedback depth, the expert system shows strong potential for broader application and further enhancement.

The increasing global demand for strawberries as a high-value crop requires effective management strategies for pests and diseases that can significantly reduce yields. This research develops an expert system to diagnose strawberry pests and diseases using the Certainty Factor (CF) method, utilizing data from previous studies to improve the accuracy and reliability of the diagnosis. The system integrates symptoms and CF values from two main studies and provides treatment recommendations that can be applied to the identified problems. The results show that in the trial on powdery mildew fungus, which has 4 symptoms, it can have the highest confidence value of 0.99977848. For other diseases themselves will have different confidence values according to the calculation of the CF value of each symptom. This approach provides a valuable tool for farmers with limited access to expert consultation, helping to improve decision-making and reduce economic losses. Performance analysis of the system shows its potential to support sustainable strawberry cultivation.

Motorcycles are the main means of transportation that is widely used to support the activities of their societies. Prior to the development of technology, motorcycles still use carburetors. But motorcycles are still using carburetor system is still quite wasteful. With the development of technology, there is now applying the Yamaha brand motorcycles capable of supplying petrol injection engine needs. Motorcycles injection technology is much more efficient compared to motorcycle carburetor technology, in addition to economical motorcycle of this type has a performance engine and a reliable power. But frequent obstacles that cause damage to the injection Yamaha motorcycle, disrupting users.An expert system is a system that adopts the expertise of an expert an expert in a particular field into the system or a computer program is presented with a display that can be used by a user who is not an expert so that system users can make a decision or set policy like an expert in finishing problem. Certainty factor method used to obtain the value of the certainty of damage from an expert motorcycle injection, so that the user also gets the value of the damage certainty selected symptoms of the experience of experts. The results of diagnostics in expert system that has been made that if the user mencheklis symptoms such as clogged when rotated gas engine, electric distater difficult / lift, lights up when the engine is difficult to use the kick starter, often emit exhaust sound of an explosion, it can be concluded from the analysis that the damage found on the spark plug with a value of MB = 0.99, MD = 0:22 and Certainty Factor value of 76.66%. The diagnosis ever made by a visitor or mechanical accessible again by entering an email to the database.

Dengue Fever is still a serious health problem and a major problem in the health sector in various parts of the world and also in Indonesia. This disease is an infectious disease and can infect all ages because there are still many people who still lack awareness in maintaining cleanliness to anticipate transmission. Data from the Ministry of Health of the Republic of Indonesia (Kemenkes RI) Indonesia recorded the number of people infected with Dengue Fever disease as many as 95,893 throughout 2020 and as many as 661 people died from this disease. The method used is using the certainty factor method where if it is interpreted this method will be used to help make a decision to diagnose the category of Dengue Fever level with a level of confidence in the form of a percentage of how much someone is infected with Dengue Fever in the existing category. The results obtained in the calculation of certainty factor get a result in the category of grade 1 of 98%, grade 2 of 98%, grade 3 of 99%, grade 4 of 99%. These results are enough to prove that this certainty factor method has a fairly high percentage level result.

Diagnosing eye diseases is crucial for early identification of eye health problems, allowing for appropriate and effective treatment before the condition develops into a more serious one. Diagnose eye diseases to holistically understand the patient's eye condition, provide more specific treatment, and improve the patient's quality of life and well-being in the long term. This study aims to diagnose eye diseases using the certainty factor method so that the accuracy and efficiency of the eye disease diagnosis process. By utilizing certainty factors, this system aims to combine expert knowledge with symptom data inputted by users to provide a more precise and reliable diagnosis. Based on the results of the diagnosis of eye disease with rule 1 or Episcleritis disease with the final value of the certainty factor method of 22.63%, for rule 2 or Hordeolum disease with the final value of the certainty factor method of 23.45%, for rule 3 or Cataract disease with the final value of the certainty factor method of 26.79%, for rule 4 or Pterygium disease with the final value of the certainty factor method of 48.89%. Based on the final value of 4 disease rules, it was concluded that patient A was diagnosed with eye disease using the certainty factor method, namely Pterygium disease with a confidence level of 48.89%.

This study aims to create an expert system at Rafflesia Hospital of Bengkulu City, based on a website and using the PHP programming language, in diagnosing bronchitis using the certainty factor method, therefore the results of the diagnosis can be first aid when a user or patient suffers from bronchitis. The research method used is the certainty factor method, namely the certainty factor, thus the user or patient can choose. conditions according to the symptoms experienced from bronchitis. The program created is an expert system for diagnosing bronchitis using a website-based certainty factor method so that it can be accessed on laptops, computers, or mobile phones with the condition that it must be connected to the internet. This program can generate a percentage level of the possibility of how many percent of patients suffer from bronchitis, and this program also produces solutions to bronchitis therefore, it can be first aid when the user or patient suffers from bronchitis. Suggestions from the author are for agencies to be able to maintain and always update this expert system program, so then this system can run and be maintained properly.

Plant disease is one of the reasons that cause the destruction of plant. It affects plant productivity and quality. Most of the farmers made mistake in cope with this problem because of the lack of knowledge. Expert system is a solution that has been widely used for identifying disease. This paper presents an Android-based expert system to help identifying tomato diseases. Data used in this expert system consist of 16 data of tomato diseases, 53 data of symptoms, and 20 variety of rules. This paper implements forward chaining and certainty factor method. Forward chaining is used as a reasoning method to get the result of disease identification. Certainty factor is used as a calculation method to obtain accuracy degree of identification results. Testing has been done through two stages, internal and external. The result from internal testing shows that tomato expert system works properly and fit perfectly in various android devices. External testing is done by giving questionnaire to 44 respondents. The result of questionnaires shows that tomato expert system is categorized as “good” by them.

Preeclampsia is a disease often suffered by pregnant women caused by several factors such as a history of heredity, blood pressure, urine protein, and diabetes. The data sample used in this study is data on pregnant women in the 2020 time period recorded at health services in the former Cilacap Regency. This study was conducted to compare the final results of the Naive Bayes method and the certainty factor method in providing the results of a diagnosis of preeclampsia seen from the symptoms experienced by these pregnant women. The naïve Bayes approach provides decisions by managing statistical data and probabilities taken from the prediction of the likelihood of a pregnant woman showing symptoms of preeclampsia. Symptoms of preeclampsia, while the certainty factor method determines the certainty value of the diagnosis of preeclampsia in pregnant women based on the calculation of the CF value. The research output compares the two methods, showing that the certainty factor method provides more accurate diagnostic results than the Naive Bayes method. It happens because the CF method requires a minimum value of 0.2 and a maximum of 1 for each rule on the factors/symptoms involved, while the Naive Bayes method only requires values of 0 and 1 for each factor causing preeclampsia in pregnant women.

Lack of blood or anemia is a condition when the body lacks healthy red blood cells or when red blood cells do not function properly. As a result, the organs of the body do not get enough oxygen, making people with anemia pale and tired easily. Anemia is often ignored because it is considered just an ordinary disease. But if left untreated, this disease can be dangerous. So that people are expected to also have knowledge about this disease and its symptoms and how to handle and prevent it so that the risk can be reduced. An expert system is a form of utilizing computer technology that adopts human knowledge to computers, so that computers can solve problems as is usually done by experts. An expert system is a computer system that is able to imitate the reasoning of an expert with expertise in a particular knowledge. An expert system can replace the role of an expert by utilizing a computer which in the end can provide a definite solution as done by an expert. The expert system for diagnosing anemia is built using the certainty factor method which can provide results in the form of the possibility of anemia suffered, the percentage of belief, symptoms, and treatment. The results of this study indicate that the expert system that was built can determine the value of the degree of confidence or the certainty factor of the data obtained from the results of consultations with patients through the certainty factor method.