The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

The outpatient care unit is one of hospital services visited first by patients for treatment. More and more patients are coming, the data will also increase. However, the patients' data in the form of the patients' arrival historical data has not been fully utilized by the hospital to improve the re-registration services that are still running conventional. This study aimed to analyze the arrival of patients by predicting their arrival. Patients' data processed by one of data mining techniques called prediction. This research is to predict the arrival of patients for a week for the next visit based on historical data of patients' arrival. Data used to build prediction models are data from XYZ Hospital. These data are the data of outpatients in 2011 amounted 17 814 records. These will be processed using Markov Chain Model· Markov Chain Model is a method of studying the movement of a variable in the future based on the movement of the variable in the present. The result obtained in the form of the greatest probability of the next patient's arrival which is the second day to the sixth based on the first arrival day of the patient to the hospital. Based on the test it was concluded that the Markov Chain Model could be a method to predict the arrival day of patients for a week.