ANALISIS KERUSAKAN MIKROSTRUKTUR ELEKTRODA ALUMINIUM AKIBAT PEMANASAN OHMIK PADA PROSES PASTEURISASI MADU MENGGUNAKAN METODE DETEKSI TEPI ANALYSIS OF ALUMINUM ELECTRODE MICROSTRUCTURE DAMAGE DUE TO OHMIC HEATING IN HONEY PASTEURIZATION PROCESS USING EDGE DETECTION METHOD

Pratama, Puspita (2025) ANALISIS KERUSAKAN MIKROSTRUKTUR ELEKTRODA ALUMINIUM AKIBAT PEMANASAN OHMIK PADA PROSES PASTEURISASI MADU MENGGUNAKAN METODE DETEKSI TEPI ANALYSIS OF ALUMINUM ELECTRODE MICROSTRUCTURE DAMAGE DUE TO OHMIC HEATING IN HONEY PASTEURIZATION PROCESS USING EDGE DETECTION METHOD. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Food safety and the effective application of pasteurization technology are key concerns in the modern food industry, particularly for high-value products like honey. One challenge in the application of ohmic heating technology is the degradation of aluminum electrodes due to electrochemical reactions, which can cause corrosion, release metal ions into the final product, and reduce process effectiveness and honey quality. This study aims to analyze microstructural damage to aluminum electrodes during the honey pasteurization process using ohmic heating at various frequencies: 50 Hz, 300 Hz, 1 kHz, and 3 kHz. This study specifically compares the effectiveness of the hybrid Canny-Sobel edge detection method and Holistically Nested Edge Detection (HED), where HED is a deep learning method based on Convolutional Neural Networks (CNNs) capable of detecting damage patterns with greater detail and accuracy. This study used a dataset of 16 images of aluminum electrodes with a resolution of 1920 × 1080 pixels, taken before and after heating under standard room lighting conditions. Data analysis was performed using MATLAB and Python software. Edge detection performance was evaluated using quantitative metrics such as MSE, PSNR, SSIM, FSIM, and entropy, and the results were compared with aluminum metal contamination values ​​from ICP-OES testing of honey. The results showed that the HED method consistently provided more accurate and detailed edge detection than traditional methods such as Canny and Sobel. At 3 kHz for the left electrode after ohmic heating, the HED method produced an MSE of 108.557, a PSNR of 27.774 dB, an SSIM of 0.824, an FSIM of 0.001, and an entropy of 2.282. Significant changes in image quality metrics indicate degradation of the aluminum electrode surface microstructure due to the ohmic heating process.This research provides a novel approach to developing a deep learning-based electrode quality monitoring system focused on ohmic heating in the food industry. These findings can serve as a basis for the development of adaptive and precise automated monitoring systems capable of continuously improving product safety and quality.

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