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Chilli farming is an important sector in Ende District, but plant disease problems are often an obstacle that hampers productivity. Farmers often have difficulty recognizing the symptoms of disease in chili plants and determining appropriate treatment steps. To overcome this problem, this research aims to design and build a web-based expert system that can help diagnose chili plant diseases using the Forward Chaining method. This expert system was developed by collecting knowledge from agricultural experts and literature related to chili plant diseases, as well as applying the Forward Chaining method for the reasoning process. Users, especially farmers, can enter the symptoms experienced by chili plants into the system, then the system will produce a disease diagnosis and appropriate treatment recommendations based on these symptoms. This research uses a case study at Maju Tani Agriculture in Ende District to ensure that the expert system developed is relevant to local conditions. This system is implemented in the form of a web-based application so that it can be accessed easily by farmers using devices connected to the internet. The test results show that this expert system can provide accurate and efficient diagnoses, as well as assist farmers in making decisions to overcome chili plant disease problems. It is hoped that this system can increase agricultural productivity and reduce the risk of losses due to plant diseases.

This research aims to develop a web-based expert system with the Forward Chaining method that can diagnose and provide treatment for people with stroke disease. The expert system will be used to help the diagnosis process by analyzing the symptoms reported by the patient. The Forward Chaining method will be used as an inference approach in this expert system. The system will use a knowledge base that contains medical rules obtained from experts. The inference process will be carried out by linking the symptoms reported by the patient with the information in the knowledge base to determine possible diagnoses and appropriate treatments. This expert system will be implemented in the form of a web-based application that can be accessed by the general public. The results of this study show that the expert system to diagnose and provide treatment using the forward chaining method is successfully built and the application can be used to help the general public diagnose by entering the symptoms felt and the expert system will provide diagnostic results and provide treatment.

Kucing adalah hewan peliharaan populer yang sering kali rentan terhadap berbagai penyakit. Ketidakmampuan pemilik untuk mendiagnosa penyakit kucing secara tepat waktu sering kali mengharuskan konsultasi dengan dokter hewan yang sebagian besar hanya tersedia di perkotaan. Penelitian ini bertujuan mengembangkan sistem pakar berbasis web menggunakan metode Forward Chaining dan Certainty Factor untuk membantu mendiagnosa penyakit pada kucing. Metodologi pengembangan sistem ini mencakup tahap persiapan, prototyping, dan penyelesaian. Pada tahap persiapan, masalah dirumuskan dan wawancara dengan dokter hewan dilakukan untuk membangun knowledge base. Tahap prototyping melibatkan pembuatan mesin inferensi dan knowledge base, sementara tahap penyelesaian meliputi implementasi sistem ke dalam antarmuka pengguna berbasis web menggunakan PHP dan framework Laravel. Hasil uji sistem menunjukkan konsistensi tinggi dengan hasil diagnosa manual menggunakan Excel. Sebagai contoh, diagnosa penyakit Virus Panleukopenia memiliki nilai certainty factor sebesar 94,62% pada aplikasi, sementara perhitungan manual menunjukkan 95,21%, dengan perbedaan hanya 0,41%. Hal ini menunjukkan bahwa sistem pakar yang dikembangkan mampu memberikan diagnosa yang cepat dan akurat. Kesimpulannya, sistem pakar berbasis web ini dapat diandalkan untuk mendiagnosa penyakit kucing dan membantu dokter hewan serta pemilik kucing melakukan diagnosa awal dengan lebih efisien.

Abstract -  Corn plants are one of the plants that are easy to grow and relatively fast to get results but also a plant that is vulnerable to pests and diseases. The symptoms that arise require an expert or agricultural expert to be able to diagnose. Limited time and expert labor is one of the causes of delayed diagnosis, causing a decrease in farmers' yields. This is the basis for making this expert system by adopting knowledge from agricultural experts about the symptoms of corn disease using the Forward Chaining method. To support the research conducted, researchers used the Research and Development (R&D) research method and the design used the Object Oriented Programming (OOP) method using the Unified Modeling Language (UML). Software is built using the PHP programming language, MySQL as a database, and Bootstrap as an interface design with a web-based system.