Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android - Pangestu, Yoga; Sanjaya, Suwanto; Jasril; Agustian, Surya; Safaat, Nazruddin (Jurnal Informatika Ekonomi Bisnis, 2025)
The advancement of digital technology has generated a demand for applications that assist the public in ensuring the halal status of food products, particularly in distinguishing between beef and pork. This study aims to develop an Android-based application for detecting beef and pork using Deep Learning methods with the EfficientNet-B6 architecture, employing the eXtreme Programming software development approach. The image classification model utilizes a Convolutional Neural Network architecture integrated into a Python-based server, while the user interface is developed with Java in Android Studio. System testing was conducted using black-box methods on several Android devices, with varying room conditions and meat types. The results show that the application can classify meat with an accuracy of 66.7%, considering room conditions such as light and dark environments, and meat types including fatty and non-fatty. This application provides fast response times and a user-friendly interface. This application is expected to enable users to independently and efficiently verify the halal status of meat, thereby supporting the needs of Muslim consumers in the digital era.