PENERAPAN DEEP LEARNING UNTUK KLASIFIKASI KUALITAS TAHU PUTIH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CV BERKAH LESTARI

Salma Ashiila Rabbani, . (2025) PENERAPAN DEEP LEARNING UNTUK KLASIFIKASI KUALITAS TAHU PUTIH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CV BERKAH LESTARI. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

The quality of white tofu is crucial for food industries like CV Berkah Lestari. This study develops and evaluates a Convolutional Neural Network (CNN) model using the VGG16 architecture to classify tofu images into two classes: “Good” and “Low”. A dataset of 1000 images was collected directly from the production process and split 80:10:10 into training, validation, and testing sets. Two training scenarios were applied: one using the SGD optimizer and another using Adam. The model was evaluated using accuracy, precision, recall, and F1-score. Results showed the Adam-optimized model performed better (98% for all metrics), compared to the SGD-based model (94% each). This confirms Adam’s effectiveness in achieving faster convergence and greater stability in recognizing tofu’s visual characteristics. The study provides a foundation for future AI-based quality inspection systems in the food processing industry.

Item Type:	Thesis (Skripsi)
Additional Information:	[No.Panggil: 2110511141] [Pembimbing 1: Musthofa Galih Pradana] [Pembimbing 2: Muhammad Adrezo] [Penguji 1: Widya Cholil] [Penguji 2: Muhammad Panji Muslim]
Uncontrolled Keywords:	VGG16, image classification, tofu, SGD, Adam optimizer
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User:	SALMA ASHIILA RABBANI
Date Deposited:	26 Aug 2025 09:52
Last Modified:	26 Aug 2025 09:52
URI:	http://repository.upnvj.ac.id/id/eprint/37516

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