In the digital era, anti-corruption law enforcement faces new challenges and opportunities. With the development of information technology, corrupt practices are increasingly complex and hidden, committing fraud and embezzlement by utilizing digital space. The use of advanced technologies such as big data analysis, artificial intelligence, and blockchain are some examples of how law enforcement can tackle corruption. In addition, this study investigates various ways in which the government, law enforcement agencies, and civil society can work together to improve transparency and accountability. It is expected to improve anti-corruption efforts by utilizing digital platforms to report and detect suspicious activities. The study found that adapting law enforcement to technological changes is important to uncover and stop corrupt practices in the digital era. In addition, public education is essential to building a stronger anti-corruption culture. To create a transparent government, the role of the media and public participation is very important to increase the accountability of officials for corrupt practices. This study aims to see how public participation and the media monitor corruption and to find methods that can improve such supervision. The methods used include literature research, reports, and policy articles. The results of the study indicate that taking an active part in reporting and monitoring can increase the accountability of public officials.

The application of information technology has been widely used in medicine. This application provides convenience and smoothness in the medical world to detect symptoms of various diseases, especially malaria. Malaria is still included in the endemic diseases suffered by the community in Binjai City, the more malaria patients, of course the more doctors are needed/work to diagnose patients. Artificial intelligence is one solution and helps doctors in supporting decision making for certain diseases. Building a system to diagnose malaria using the Case-Based Reasoning (CBR) method offers various significant advantages. CBR utilizes experience and knowledge from previous cases, allowing the system to provide a more accurate diagnosis based on patterns and symptoms that have occurred in the past.