A System Architecture Design: Integrating Random Forest, Natural Language Processing, and Internet of Things to Predict Technical Carnapping in the Philippines

Abstract

Technical carnapping, or rent-tangay, is a new deceptive scheme of stealing a car by virtue of a rental contract. Typical car anti-theft systems can only provide the location and status of the car. This means that by the time the vehicle is stolen, it is often too late. Even with the use of technology, this makes it hard for car owners and operators to prevent this new illegal scheme. This study features an architecture design for a car anti-theft system that integrates the use of natural language processing (NLP), random forest (RF) model, and internet of things (IoT) in predicting technical carnapping or rent-tangay in the Philippines. It highlights three major components, which are the black box or the hardware module, the mobile application, and the website application. The black box is responsible for gathering data inputs, including geographical location, recorded audio, and sensor outputs. The NLP pipeline is responsible for mining and processing text-based data from the audio recording. Whereas the RF model is responsible for scoring all of the inputs and using them to predict technical carnapping. The model developed in the study scored 100% in recall, 96.30% in accuracy, and 85.71% in F1 score. This implies the success and effectiveness of the design in predicting technical carnapping. This achievement significantly contributes to the body of work that focuses on developing security systems for cars, especially by effectively and efficiently implementing NLP and machine learning to the system. The study pushes the technological boundaries that can be explored in designing and developing car security systems.