Forward Chaining Method for Diagnosing Diseases and Pests in Melon Plants

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 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.

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.

Potatoes (Solanum tuberosum L.) are a priority vegetable crop due to their high domestic trade value and export potential. Potatoes are used for various purposes, both as a vegetable and as a carbohydrate substitute. In addition to being used as a vegetable, potatoes are also utilized as raw materials in the food industry, such as chips, potato flour, and potato starch. Due to the relatively low temperature requirement (20-22°C) for tuber formation, potato cultivation areas in Indonesia are generally located in mountainous regions. One of the potato commodity centers is in the city of Batu, particularly in the Bumiaji District. According to vegetable crop potential data from the Batu City extension program in 2022, the area planted with potatoes is 485.2 hectares with a production potential of 968 tons. Since potato plants are more susceptible to pests and diseases, substandard maintenance can lead to low harvest yields, poor sales, and even crop failure. This issue has led to the development of an application for diagnosing potato pests. The expert system uses forward chaining methods and is web-based. The expert system processes facts answered by users of the potato application, diagnoses the symptoms present, and generates diagnostic results in the form of solutions for the diagnosed potato plant diseases or pests. With the availability of an expert system application for diagnosing potato plant diseases and pests, the limitation of expert manpower is no longer a hindrance for potato farmers. Recommendations and information regarding potato diseases and pests can be obtained online without the need to consult a specialist.

A doctor or medical scholar needs a reference book to learn how to diagnose tropical diseases. This reference book is sometimes a hassle if you have to carry it everywhere. This reference book is also impractical if you have to search it first to find the symptoms and diseases you want to study. So that we need a system to make it easier for doctors and medical scholars to study the science of diagnosis and look for symptoms and diseases. Expert systems are knowledge-based programs that provide expert quality solutions to problems in a specific domain. This expert system is used in the fields of medicine, agriculture, business, and others. Expert systems in the field of medicine make it easy to identify diseases suffered by patients through the symptoms present in the patient. This expert system helps doctors make diagnoses to convince doctors about the results of the diagnosis. The expert system in this study uses a combined method. The combined method is forward chaining and backward chaining. The forward chaining method is used to determine specific symptoms that appear, while the backward chaining method is used to trace general symptoms that arise from specific symptoms that have been previously selected. The result of combining these methods provides a diagnostic percentage of 100%. Meanwhile, the user's assessment of the system gives a good response.

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.

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.

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 Morbilivirus virus is the cause of the infectious illness measles, which can be lethal, particularly in young patients. Due to poor vaccination rates and restricted access to medical care in rural regions, the number of measles cases in Indonesia has dramatically grown. An expert system based on Android has been created to solve this issue, enabling people to independently diagnose measles early. Based on the user-selected symptoms, this system use the Forward Chaining method as an inference methodology to assess the likelihood of a diagnosis. In order to guarantee flexibility and speed in software development, Agile and Rapid Application Development (RAD) were the research methodologies employed in the creation of this system. Testing was carried out using the Blackbox Testing and Whitebox Testing methods, which showed that the system had a good level of accuracy in detecting measles. In addition, this system also stores diagnostic history, provides food recommendations, and information related to measles. The results of the study show that this expert system can increase public access to health information, especially in areas with limited medical personnel. With this application, it is hoped that the public can be more aware of measles symptoms and immediately take appropriate medical measures to prevent further complications.

Farmers still do not really understand how to diagnose problems in plants, one example is strawberry farmers. This is proven by several farmers and strawberry plant cultivators, not all of whom can understand the disease where there are various types of diseases that can attack strawberry plants with almost the same symptoms , and if a farmer or cultivator is wrong in handling the type of strawberry plant disease, it is not impossible that it will cause the strawberry plant to die. Strawberry farmers need a tool to diagnose strawberry plant diseases so that they can find out the condition of the strawberry plants. Therefore, an expert system was created to diagnose diseases in strawberry plants and find solutions to deal with the damage that occurs. The system built for diagnosing diseases in strawberry plants uses the Forward Chaining method. Forward chaining is a forward tracking that starts from a set of facts by looking for rules that match the existing hypothesis towards a conclusion. In its implementation, this system has met these objectives by using a database and rule base. The system draws conclusions based on existing facts using the forward chaining method, the search starts from the facts from which new conclusions are obtained, the existing rules are traced one by one until the search is stopped because the last condition has been met.

The choice of major is very important to support the success of students in completing their studies well and on time. However, this is an important issue because, based on research by the Indonesia Career Center Network (ICCN) in 2017, 87% of Indonesian students admit that the majors they chose do not suit their interests. And 71.7% of workers have a profession that is not in accordance with their education. The obstacle faced by prospective students when choosing a major is that they do not know their interests, talents, and potential. Therefore, a system was created that could provide suggestions for majors that match the interests of prospective students. An expert system with the forward chaining method is used in the system. The method used in making this expert system application is the forward chaining method. This expert system process accepts the choices that are already available and have been filled in by the user. And the results of this expert system provide a solution for selecting majors based on the interests of prospective students. So that prospective students can determine the right major. From the results of the implementation and trials that have been carried out by researchers, a probability value is obtained that reaches 100%, indicating that the system is functioning properly, and as many as 94% of respondents stated that they strongly agree that the use of this application can help make it easier to determine majors at Universitas Muhammadiyah Sinjai.

Each tool and part contained in a coffee maker has its own level of importance and may not function optimally. This problem often becomes a problem for users or bartenders on duty at work. To overcome this problem, this can be done by someone who has basic knowledge of Lamarzocco coffee makers. However, sometimes these problems also require a high level of expertise from the components so that special technicians are needed to repair them. How to design an expert system application with the forward chaining method to diagnose failures in Lamarzocco coffee machines. In this research the author used the PHP programming language and MySQL database. The research method used by the author is data collection, analysis and object-oriented programming. The program can then be accessed on the website for ease of use, which will then be black box tested with the manager or supervisor of the relevant point of sale. The results of research on the design and application of an expert system using the forward chaining method can diagnose damage to an espresso coffee maker (Lamarzocco coffee maker), this expert system application can be used to assist employees in handling damage to a Lamarzocco espresso machine very well without any problems occurring, and From the results of trials using black box testing on the Lamarzocco coffee machine damage expert system application, we got good results

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.

In this study contains an expert system design for web-based lecturer scheduling that can facilitate the Chair of the Study Program in scheduling courses for lecturers. This system was built using Notepad ++ software, XAMPP with HTML, CSS, JavaScript, PHP and database programming languages using MYSQL. Subjects in this study are expert system applications as the media schedule lecturer subjects. Data collection in this study uses the interview method and questionnaire method. This system is built with the Waterfall model namely analysis, design, coding and testing. This application in the inference engine used is the forward chaining method. The results of this study are expert system applications to schedule lecturers’ subjects according to the questionnaire data. Based on the results of the trial it can be concluded that this expert system application can facilitate the Chair of the Study Program in accurately scheduling courses, classes, days, hours, space, and lecturers

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