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Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

Monitoring realizes efforts to improve the quality of health services. To obtain information on patient condition data during treatment. The monitoring process is done manually. So that it has an impact on the service and condition of the patient during treatment. The design of monitoring the patient's body temperature and heart during treatment with IoT can be controlled through the NodeMCU sensor ESP8266, MLX9014, MAX30100 sensor and Arduino IDE software program. Furthermore, it can detect the patient's temperature and the patient's heart rate during treatment. And processed by the NodeMCU ESP8266, the data from the two sensors is displayed on the SSD1306 OLED LCD and also to the smartphone of the medical officer on duty via Blynk. In order for the tool to work properly and optimally, it is necessary to adjust the pin placement so that it can work optimally. The problem of internet connection interference causes delays, resulting in a mismatch between the measurement of the test equipment and the standard tool. Data values are taken with an accuracy of 70%-93% for the MAX30100 sensor, for the temperature sensor it is close to optimal with a value that is read on a standard tool with an accuracy of 97%-99%.