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The purpose of this activity is to socialize the implementation of biosecurity as an effort to prevent disease in cattle in Luhulely Village, Pulau Letti District, Southwest Maluku Regency. This activity was carried out using a socialization method involving beef cattle farmers and all Luhulely Village staff. The implementation of the activity was divided into 2 sessions, namely a material presentation session and an interactive discussion session. Some general steps in implementing biosecurity include the following: access management, animal movement control, cleanliness and sanitation, waste management, animal health monitoring, and vaccination. This socialization activity was attended by approximately 20 participants consisting of beef cattle farmers and several Luhulely Village staff. Several important things that can be concluded from the implementation of this activity are as follows: the majority of farmers showed an increased understanding of the importance of biosecurity after participating in the socialization, the farmers expressed their readiness to implement various biosecurity measures, such as quarantine of new livestock, waste management, and provision of adequate sanitation facilities in the livestock area, and several farmers requested further assistance regarding the implementation of more detailed biosecurity, including correct livestock vaccination techniques. The conclusion of this activity is that the socialization of biosecurity implementation on cattle farms is expected to increase farmers' awareness and understanding of maintaining livestock health and preventing disease transmission. Through proper biosecurity implementation, it is hoped that healthy, more productive, and sustainable livestock conditions will be created.

This study aims to design and develop an expert system to assist in diagnosing diseases in dairy cattle at Cibugary Farm using the Forward Chaining method. The background of this research lies in the limited knowledge of farmers in identifying early symptoms of diseases, which often leads to delays in medical treatment and negatively affects dairy cattle productivity. To address this issue, an expert system was designed to replicate the reasoning process of a human expert through a knowledge base containing diagnostic rules derived from observable symptoms. The Forward Chaining method was chosen because of its capability to trace facts from known symptoms toward a conclusion regarding the type of disease affecting the cattle. The system was developed by incorporating common disease symptoms, inference rules, and a decision-making mechanism that simulates expert analysis. Testing was carried out on several diagnostic scenarios to evaluate the accuracy and efficiency of the system. The results of the study indicate that the expert system can provide an initial diagnosis quickly and accurately, producing outputs consistent with expert assessments. This functionality assists farmers in making timely decisions regarding appropriate medical interventions, thereby reducing treatment delays and minimizing the risk of disease transmission within the herd. Consequently, the Forward Chaining-based expert system is expected to serve as an innovative solution to improve dairy cattle health management and support sustainable livestock productivity at Cibugary Farm.

Diseases in cattle, especially Foot and Mouth Disease (FMD), have a major economic impact on farmers. FMD is a highly contagious viral disease in even-toed ungulates such as cattle, buffalo, goats, and sheep. This virus from the genus Aphthovirus, family Picornaviridae spreads rapidly through direct contact, air, or contaminated objects. The impact of FMD is very detrimental, including decreased milk production in dairy cows, weight loss in beef cattle, and death in severe cases. In addition, high medical costs, restrictions on livestock trade, and reduced consumer confidence in livestock products are also important consequences. The FMD outbreak in Indonesia in 2022, especially in East Java, caused major losses for farmers. Therefore, a fast and accurate diagnostic system is needed for early detection of the disease. Early diagnosis helps farmers take appropriate treatment measures such as isolating infected livestock, administering drugs, and disinfecting pens to prevent the spread. This article discusses the implementation of the forward chaining method in an expert system for diagnosing cattle diseases, including FMD. This method analyzes cattle symptoms and matches them with rules in the knowledge base systematically, from symptoms to diagnosis. With this approach, the expert system can provide a fast and accurate diagnosis and appropriate treatment recommendations. This is expected to help farmers reduce losses, increase productivity, and support the sustainability of cattle farming businesses in Indonesia.