In today's rapidly evolving digital age, mobile applications have become an essential aspect of our daily lives. The JAKI app, specifically designed to facilitate Jakarta residents in reporting various city-related issues, has emerged as a powerful tool for civic engagement. A key feature of this application is the ability for users to include photos in their reports. However, concerns regarding privacy and data security have surfaced. Frequently, user-uploaded photos contain identifiable faces or vehicle number plates, raising significant privacy concerns. The imperative to protect individual privacy in these photos is not just about respecting personal rights but also about complying with data protection regulations. Addressing such privacy concerns is critical. Therefore, the "Blurring Faces and Number Plate using JakLapor Data in JAKI App" project aims to tackle this issue head-on. By implementing blurring techniques for faces and number plates in photos submitted through JakLapor, the app seeks to safeguard individual privacy while ensuring the shared information remains secure. Hence, an automated system is needed that can accurately detect and blur faces and number plates in photos uploaded to the JAKI app. This research aims to develop a model capable of automatically detecting and blurring faces and vehicle number plates in photos uploaded through the JAKI app. Utilizing machine learning technology and the Detectron2 library, we aim to create an efficient and effective solution that can be integrated seamlessly with the JakLapor system within the JAKI app. Problem Statements: Privacy and Data Security: Photos uploaded by users in the JakLapor feature of the JAKI app often contain identifiable faces and vehicle number plates, raising serious privacy and data security concerns. Lack of Automated Privacy Protection: The JAKI app currently lacks an automated mechanism to ensure the privacy of individuals and vehicles in user-uploaded images, making it necessary to develop a reliable solution. Efficiency and Accuracy Challenges: Developing an automated system that can accurately detect and blur sensitive information in a diverse range of images presents significant technical challenges in terms of efficiency and accuracy.