Expert System for Diagnosing Strawberry Plant Diseases Using the Forward Chaining Method

Hydroponics systems require tremendous attention both in nutrition, plant growth, the risk of pest and disease attacks. Thus, this hydroponic farming system requires high experience and expertise that is the main obstacle for business actors who want to start a vegetable cultivation business with a hydroponic system, especially for business actors who do not have a background in agriculture. The hydroponic cultivation expert system in this study aims to detect plant diseases and pests and provide solutions to these diseases. In addition, this expert system application also aims to monitor plant needs, so this application can help farmers grow up hydroponic vegetable cultivation businesses. The combination of the forward and backward chaining methods is applied to detect plant diseases and pests. Whereas the forward chaining method use to monitor plant needs. The combination of the forward and backward chaining methods in identifying plant diseases and pests can make it easier for users to determine the suffered by plants and provide information about these diseases. The application of the forward chaining method in monitoring plant needs can identify the level of fulfilment of entails of each component of plant needs according to the type of plant and the day after planting. The application of this hydroponic expert system can overcome the limited number of hydroponic experts in helping farmers identify plant diseases and pests and monitor plant needs.

The aglaonema plant is quite in demand by the public because it is a current trend and can be a business field for ornamental plant lovers. However, the growth of aglaonema plants can be hampered by disease, so a system is needed. This Aglaonema plant disease diagnosis expert system was created to determine the type of disease so that it can be overcome as early as possible. The expert system method used is the Forward Chaining method and the Naive Bayes method. The Forward Chaining method is used to make rules based on symptoms and the Naïve Bayes method to determine the results of the diagnosis. This study used 25 symptoms for 10 diseases and obtained an accuracy value of 76% using 50 data.

The agriculture department of Solok West Sumatra district especially Agricultural Extension Agency (BPP) of Lembang Jaya Sub-district, has decreased the productivity of onion every year. This plant has decreased because it often attacked by pests or diseases, and lack of knowledge about the symptoms of onion’s pests or diseases. To fix this problem, made an application to facilitate the farmers, to know the type of pest or disease from symptoms on the onion and solution of each type of diseases. The method used is Forward Chaining. Forward Chaining Method is collection of data from each symptom that appear on the Allium Cepa Var Aggregatum. Then, made a decision to get the results about the solution and type of disease on the Allium Cepa Var Aggregatum. The results of this study is to show the type of disease and solution that has been detected based on symptoms on the Allium Cepa Var Aggregatum.

Expert systems are a branch of artificial intelligence designed to mimic the ability of an expert to make decisions or solve problems in a particular field. This study aims to develop a web-based expert system capable of performing diagnoses using the forward chaining method as an inference mechanism. This system is intended to help the public recognize specific problems, particularly in the field of health. Unfortunately, people often lack access to professional psychological services. This study aims to build web-based expert system that can help make an initial diagnosis of mental disorders using forward chaining method. This system is designed to collect symptoms from users, process them using a rule base determined by experts, and provide automatic diagnosis results. In its development, the system uses the PHP programming language and MySQL database to store symptom data, disease information, and consultation results. The inference process is conducted sequentially (forward chaining) based on the symptoms selected by the user until a conclusion is reached regarding the type of mental disorder and its likelihood. Testing results indicate that the system can provide reasonably accurate results in identifying users' mental conditions based on the symptoms entered. This system is expected to serve as a useful initial consultation tool, particularly in communities with limited access to mental health services. However, this system is not intended to replace the role of professionals but rather as a form of technological support to aid in early detection.

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.

Disorders of the eye become a disease that is often experienced. There is a new symptom model found, causing confusion that occurs. Diseases of the eye often attack humans, especially in the current era of digital technology, because they spend more time staring at cell phone or computer screens. So the application of this expert system is made to provide solutions for the community to diagnose the symptoms that are often experienced. In making this application, the forward chaining method and certainty factor are used in the application built and comparison with the combined forward chaining and naive Bayes method. The test is carried out by testing 40 data samples based on existing symptoms, carried out by the system and also manually to see the accuracy of the values given. The results given are that there is a fairly high level of certainty in the diagnosis of hypermetropia with a certainty value of 88.43% and the smallest certainty value in the diagnosis of presbyopia with a value of 61.702% of the total testing obtained a probability level of certainty ranging from 51%-79%.

Indonesia is an <span>ASEAN country with the most motorcycle users, where one-third of its population own motorcycles. The automatic motorcycle is the most widely used type due to its agility and fuel-efficient abilities. Sudden motorcycle damage could hamper the users' activities. However, most of these users do not know the reason for the damage. This paper presents the development of expert system for diagnosing the damage of motorcycle using forward chaining method. This system was implemented in mobile application. Through a mobile application, a solution for these users can be obtained. This application immediately discovers the damaged location and repair process. Furthermore, it acts as the first solution before motorcycle repair is carried out in a shop. In this study, the forward chaining method was implemented. It is based on a pattern-matching algorithm whose primary objective is to match facts (input data) with appropriate rules from the rule base. Various test results showed that the diagnostic application used for automatic motorcycle damages 100% worked.</span>

Dyspepsia is a digestive disorder that causes discomfort or pain in the stomach. In expert systems, the forward chaining method is used to diagnose this disease. This method evaluates the facts or symptoms obtained from the patient, then determines the appropriate diagnosis. The application of the forward chaining method in an expert system allows for an efficient and systematic evaluation of patient symptoms, so that patients can make their own detections and can make further decisions so that their disease can be treated immediately. This technology-based approach enhances the diagnostic process and facilitates rapid intervention. The expert system developed to diagnose dyspepsia has been successfully implemented using knowledge that includes 21 symptoms and 3 types of related diseases. System validation was carried out by testing 30 data samples, and the results showed that the system has an accuracy rate of up to 90% in correctly identifying and diagnosing dyspepsia. This shows that the expert system has a high level of expertise in analyzing the symptoms presented and providing a reliable diagnosis

There are many problems encountered in the field of agriculture, for example problems about diseases and pests in melon plants. Experts or experts in the field of agriculture rarely need to build a system that is able to adopt human processes and ways of thinking in the form of an expert system. The purpose of this study is to build an expert system to diagnose diseases and pests in melon plants using the forward chaining method. A reasoning that starts from facts first to test the correctness of a hypothesis. The system was tested using the black box testing method which was tested on experts and melon farmers. Based on the results of testing using the black box testing method tested on experts and farmers, the results of the assessment were obtained that the expert system for diagnosing diseases in melon plants was proven to run well as expected with 100% validation. The end result of this expert system is to make it easier for users to carry out the consultation process by providing a list of indication of diseases and pests experienced. Then the control of the type of disease and pest will be displayed according to the selected indication. In addition, this expert system also makes it easier for admins to update data such as disease data, indication and control. This expert system application program is also not only beneficial for experts but can also be useful for farmers that ordinary people who do not understand a little about melon plant diseases and pests.

Coastal cattle are livestock that have economic value, such as selling beef and cattle breeds. Cow disease can cause the quality of its sales to decrease. This study aims to help cattle breeders to determine the type of cow disease, from the symptoms that exist in these cows. So that the prevention of the risk of cow disease can be avoided. All data used are sourced from experts. In determining the type of disease in cows, the Forward Chaining method is used. The fact-finding technique is then put into the predetermined rules to get a conclusion. Making a website based expert system makes it easy for breeders to access it online. The accuracy of the system has been tested by related parties so as to produce fast and efficient information. From research, it can help breeders in diagnosing the symptoms experienced by cows and the test results can detect the type of disease accurately.

Tuberculosis (TB) is an infectious disease that can affect people of all ages, including children, adolescents, and the elderly, and can cause illness and death in over one million people. The disease is spread through coughs or sneezes by people with pulmonary TB, through contaminated saliva, and inhalation by healthy people with weakened immune systems. Therefore, this study aims to develop an expert system to assist in the diagnosis of pulmonary tuberculosis using the Forward Chaining and Certainty Factor methods. This process begins by identifying symptoms reported by the user and then searching for rules in the knowledge base that match those symptoms. This method allows the system to follow a logical flow of reasoning similar to the way a doctor diagnoses a disease. This study used data from 100 patients from 2023 at the Pariaman Community Health Center. Using the Forward Chaining and Certainty Factor methods, three patient data sets with three types of tuberculosis were tested. The percentage results for each type of disease were 100% positive for pulmonary tuberculosis, 0.91% negative for pulmonary tuberculosis, and 0.92% latent for pulmonary tuberculosis, with a confidence level of Very Confident. This research contributes to increasing knowledge and understanding in the field of expert systems, particularly in the application of the Forward Chaining and Certainty Factor methods for diagnosing tuberculosis.

Pepper Plantation is currently experiencing a significant decline from both the quality as well as the difficult treatment. As for some of the factors affecting the decrease in the form of disease and pests. These problems are difficult to overcome because of the limited number of experts who are able to identify and provide solutions to pepper plants. So it takes an expert system capable to identify diseases and pests as well as handling solutions by leveraging the android-based technology using forward chaining. This research was conducted using the survey directly into the object of research and interviews to experts. The testing is done by testing the accuracy of data involving 35 reached 97,14%. with the accuracy results prove that the method of forward chaining can work well in the expert system of diagnosis of disease and pest of pepper plants.

An expert system is a collection of system engineering devices which have a structured way of thinking from one or more experts regarding certain scientific fields, both specific and complex, which are created based on an instructional element that identifies and also analyzes data at the level of irregular problems or analysis. mathematics regarding problems that cover certain aspects, but are related to the process of detecting printers and repairing them from the school which is carried out independently, then along with the development of website-based information technology which can make it easier to detect printer damage for users, then in detecting printer damage for users there are several factors that influence this, such as, not all staff at the school have knowledge in detecting printer damage and there is no system that supports detecting printer damage so it is difficult to find solutions for staff to repair, based on this problem as a solution, an expert system application design is needed. for detecting printer damage to users who support the website-based printer detection process using the forward chaining method, the concept of forward chaining with search or forward tracking techniques starting with existing information or combining rules to obtain a certain conclusion or goal as a result of the solution.

Edamame grade is determined based on edamame quality parameters with several parameters that are quite a lot. The process of deciding edamame grade is relatively complicated and challenging. To determine the edamame grade, an experienced expert in the field is needed. With limited grading experts, the design of expert systems for grading edamame can help users or companies to determine edamame grade. This study aims to design an expert system for determining edamame grade, which can determine the quality of edamame accurately. The Design of an expert system in this research uses the forward chaining method. The stages of designing an expert system include problem identification, knowledge acquisition, building a data flow diagram, compile the database, building a decision tree diagram, and developing a user interface. The results showed that the expert system has been verified and validated well. Verification test results show that all the menu buttons on the expert system application are functioning correctly. Then, the validation results also show that the edamame quality output produced by the expert system is compatible with the expert solution, meaning that the expert system's output data is by following with the results of the decision given by the expert. In testing the validation of determining the quality is appropriate. The results of the verification and validation of the edamame grading expert system show that the expert system has produced satisfactory outcomes. Expert system output is compatible with the solution provided by the expert.

The issue addressed in this research is how to design and build an expert systemwhich capable of diagnosing dog diseases that are caused by parasites, and how this expertsystem later can be useful for dog owners.This expert system is developed based on website technology in order to ease theaccessibility for the users. The finished product is expected to become useful for dog owners,to help them diagnose their dog disease based on the symptoms in order to give the propertreatment. The software development methodology used to develop this expert system is theWaterfall methodology, while the design is done using Unified Modeling Language (UML).The programming language used to build the expert system is PHP for the logic and MySQLfor the database, and the finished product is then tested using black box testing method. Totest system accuracy, disease diagnosis results from the expert system is then compared withdiagnosis results from the real human expert (veterinarian).This expert system is using forward chaining method and Bayes theorem. The forwardchaining concept, in this system, starting with the symptoms that are set into a reasoning andrules to reach some conclusions which in this case are the possible diseases. While the Bayestheorem is used to draw a conclusion from the possible diseases by calculating the probabilityof what kind of disease suffered based on the symptoms, and the weight of the probabilityitself. This expert system, however, still can be developed not only to diagnose diseases thatare caused by parasites, but also to diagnose all kind of dog diseases.