Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android | Jurnal Informatika Ekonomi Bisnis

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Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android

Yoga Pangestu Universitas Islam Negeri Sultan Syarif Kasim Riau Suwanto Sanjaya Universitas Islam Negeri Sultan Syarif Kasim Riau Jasril Universitas Islam Negeri Sultan Syarif Kasim Riau Surya Agustian Universitas Islam Negeri Sultan Syarif Kasim Riau Nazruddin Safaat Universitas Islam Negeri Sultan Syarif Kasim Riau DOI: https://doi.org/10.37034/infeb.v7i2.1195 Keywords: Deep Learning, Efficient Net-B6, Android, eXtreme Programming, Meat Detection Abstract 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. Downloads Download data is not yet available. References Nida, L., Pisestyani, H., & Basri, C. (2020). Studi Kasus: Pemalsuan Daging Sapi dengan Daging Babi Hutan di Kota Bogor. Jurnal Kajian Veteriner, 8(2), 121–130. 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A., Khelifi, A., Abran, A., & Ormandjieva, O. (2010). Techniques for quantitative analysis of software quality throughout the SDLC: The SWEBOK guide coverage. In 8th ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2010 (pp. 321–328). DOI: https://doi.org/10.1109/SERA.2010.47	Download Published 2025-06-30 Issue Vol. 7, No. 2 (June 2025) Section Articles How to Cite Pangestu, Y., Sanjaya, S., Jasril, Agustian, S., & Safaat, N. (2025). Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android. Jurnal Informatika Ekonomi Bisnis, 7(2), 298-303. https://doi.org/10.37034/infeb.v7i2.1195 ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX This work is licensed under a Creative Commons Attribution 4.0 International License.

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