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As area surveillance technology, the camera is still suboptimal because it cannot detect suspicious human movement and there is no real-time security alert. Although motion detection is implemented, it is only activated when a person passes the PIR sensor, triggering the camera to capture the object. Due to its lengthy process, it is less effective. This study aims to develop a home surveillance system that uses object detection technology to detect unusual human movements. The system is also equipped with real-time early warning through a Telegram Messenger application. The system is then tested using various parameters that may impact the precision of detection results, including object poses, camera height, and camera distance. The system can detect objects that make unusual movements in 69 images or 57.5% of the tests, based on the analysis of 120 test data. Through the integration of object detection technology and real-time Telegram-based alerts, this home surveillance system significantly demonstrates the capability to accurately identify suspicious human motions thereby enhancing area surveillance effectiveness and adaptability to various environmental conditions.

Chatbot technology uses natural language processing with artificial intelligence that can interact quickly in answering a question and producing relevant answer. ChatGPT is the latest chatbot platform developed by Open AI which allows users to interact with text-based engines. This platform uses the GPT-3 (Generative Pre-trained Transformer) algorithm to help understand the response humans want and generate relevant responses. Using the platform, users can find answers to their questions quickly and relevantly. The method used for OpenAI's research on ChatGPT integrated through Telegram chatbot is using a waterfall method which utilizes open API tokens from Telegram. In this research we develop OpenAI application connected with telegram bot. This application can help provide a wide range of information, especially information related to the Semarang State Polytechnic. By using Telegram chatbot in the program, users can find it easy to ask because it is integrated with OpenAI using the API. Telegram chatbot, which has a chat feature, allows easy communication between users and chatbots. Thus, it may reduce system errors on the bot.

Ramie (Boehmeria nivea) is believed to be one of the strongest natural fibers, but it still remains behind synthetic materials in terms of tensile strength. In this study, ramie materials were prepared to evaluate the modification crosslinking effect of natural fiber. The aim is to optimize various concentrations of citric acid (CA) crosslinking by adding Sodium hypophosphite (NaPO2H2), which is activated at different temperatures, to obtain the highest tensile mechanical strength. This crosslinking effect has been confirmed by FTIR to show the esterification process in the molecular structure of cellulose. The changes in the character of the fiber surface were analyzed by SEM. The tensile strength increased from 62.33 MPa for 0% CA to 124–172.86 MPa for decorticated fiber with a CA concentration of 0.75–1.875% (w/w). A significant increase in tensile strength was observed more than 19 times when CA/SHP 1% was treated at an activation temperature of 110 °C with a superior tensile strength of 1290.63. The fiber crosslinked with CA/SHP should be recommended for application of Natural Fiber Reinforced Polymer Composite (NFRPC), which has the potential to use in functional textile and industrial sector automotive or construction.

The vehicle's anti-theft system is a module used to protect motor vehicles from theft by using three layers of security. The first layer uses a registered fingerprint for user authentication that can enable the machine on. The second one uses a GPS tracker to recognize where the position of the vehicle. The third one of security uses a remote engine cut-off system to shut down the machine remotely using the Android app. The system consists of two separate modules that are connected using a mobile data network. The first module is attached to the motorcycle body consists of fingerprints for biometric verification, GPS tracker for vehicle tracking and a microcontroller that controls the entire onboard system, including turning on and off the machine remotely. The second module is an application on the Android system used to detect where the location of the vehicle and applications that can be used to shut down the machine remotely. The method used in this research is the waterfall, it consists of systems and software design, module creation, module testing, module integration, system testing, operation and repair. The results shows this system is trusted to secure the motor vehicle by using a fingerprint as user authentication and GPS Tracker in which accurately detect the location of the motor vehicle, and then the system can shut down the motor power from a distance so that it can discover the theft of the vehicle. The conclusions: 1) The motorized vehicle safety system that is created to ease the owner of motor vehicle to control (On/Off) the vehicle remotely; 2) Si Kemot tool has been created to provide and increase the sense of security for vehicle owners because it can provide information on the position of the coordinates and speed of the vehicle; 3) the system can be controlled simply by using Android-based gadget that is very simple and ease of operation.

The main focus of this research is to develop a real-time forest fire monitoring system using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with sensors, a mini processor (Raspberry Pi) and Ardu Pilot Mega (APM) for the flight controller. This system used five sensors. The first is a temperature sensor that served to measure the temperature in the monitored forest area. The others sensors are embedded in the APM. There are a barometer, Global Positioning Sensor (GPS), inertial measurement unit (IMU) and compass sensor. GPS and compass are used in the navigation system. The barometer measured the air pressure that is used as a reference to maintain the height of the UAV. The IMU consists of accelerometer and gyroscope sensors that are used to estimate the vehicle position. The temperature data from the sensor and the data from GPS are processed by the Raspberry Pi 3, which serves as a mini processor. The results of the data processing are sent to the server to be accessible online and real-time on the website. The data transmission used the Transmission Control Protocol (TCP) system. The experimental setup was carried out in an area of 40 meters × 40 meters with 10 hotspots. The diameter of the hotspots is 0.4 meters with a height of 0.5 meters. The UAV is flown at a constant speed of 5 m/s at an altitude of 20 meters above the ground. The flight path is set by using a mission planner so the UAV can fly autonomously. The experimental results show that the system could detect seven hotspots in the first trial and nine hotspots in the second trial. This happened because there is some data loss in the transmission process. Other results indicate that the coordinates of hotspots detected by the UAV have a deviation error of approximately 1 meter from the actual fire point coordinates. This is still within the standard GPS accuracy as this system uses GPS with a range accuracy of 2.5 meters.