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Nowadays, security and safety are big concerns in this modern and cyberwar era. Many countries invest some safety infrastructure to ensure their inhabitants for keeping their lives safely. Indonesia is the country with many problems because of urbanization and other challenges. This problem should be solved with smart city solution and it must be able to face the challenge of ensuring the safety and improving the quality of life regarding network centric warfare era. This problem also should be tackled with CCTV analytics with the ability to implement an automatic weapon detection system. It also can provide the early detection of potentially violent situations that is of paramount importance for citizens security. This paper is using deep Learning techniques based on Convolutional Neural Networks (CNN) can be trained to detect this type of object with YOLOv4 model and it proposes to implement CCTV analytics as a platform to process real-time data for monitoring weapon detection into knowledge displayed in a dashboard with accuracy 0.89, precision 0.82, recall 0.96 dan F1 Score 0.90 result on weapon detection with a real time speed of processing with NVIDIA 2080 Ti around of 35 FPS. It will send an early warning notification if the system detects the weapon detection such as a knife, gun etc.

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Technological advances, especially in the field of remote control, both automatic and non-automatic, are very rapid. This can be seen from the technological capabilities that can work on the land, air, and water. Indonesia as one of the largest archipelagic countries in the world that have borders such as land, air, and vast seas must have this technology to anticipate the potential for problems that endanger citizens and the state. So far, Indonesia has reached this technology, for example, drone technology as a border monitoring mission through the air and aerial photography purposes. Ground robots are used to defuse remote-controlled bombs. To complete this, the researchers conducted research related to a remotely controlled prototype of an unmanned water vehicle. The research conducted discusses the manufacture of prototypes of unmanned surface vehicles and control stations that can communicate with each other. This prototype is controlled by the Arduino Nano microcontroller module, while the main control system uses a desktop-based application that is run via a laptop. Based on the test results, sending sensor data to Arduino and to the control station via the RF-module media went well. The transmission distance of transmitting sensor data and navigation control reaches approximately 250 meters, while the transmission distance of IP camera images reaches approximately 9 meters.

In carrying out their duties, TNI soldiers often experience pressure and threats that attack both physically and psychologically. This can trigger stress. Uncontrolled stress will cause disease disorders such as arrhythmias and hypoxemia. We offer a solution by building an Internet of Military Things (IoMT) based military oximeter for soldier stress monitoring. The proposed tool is real-time and portable, can monitor heart rate (BPM) and blood oxygen saturation (SpO2) when soldiers are on duty in conflict areas. This military oximeter is equipped with notifications and alarms that are integrated with applications installed on smartphones, so commanders can monitor the condition of their soldiers directly and view their health history. Based on the test results, obtained an accuracy of 99.7% and 99.88% for measuring heart rate and oxygen saturation in the blood. This military oximeter can be used as a medical aid to monitor the health condition of soldiers while on duty.