ANALISIS EKSTRAKSI FITUR DAN ROI (REGION OF INTEREST) DALAM MENENTUKAN KESEGARAN IKAN BANDENG MENGGUNAKAN GRAY LEVEL CO-OCCURANCE MATRIX FEATURE EXTRACTION AND REGION OF INTEREST (ROI) ANALYSIS IN DETERMINING MILKFISH FRESHNESS USING THE GRAY LEVEL CO-OCCURANCE MATRIX

Crisna Yudha Priyanto, Crisna Yudha Priyanto ANALISIS EKSTRAKSI FITUR DAN ROI (REGION OF INTEREST) DALAM MENENTUKAN KESEGARAN IKAN BANDENG MENGGUNAKAN GRAY LEVEL CO-OCCURANCE MATRIX FEATURE EXTRACTION AND REGION OF INTEREST (ROI) ANALYSIS IN DETERMINING MILKFISH FRESHNESS USING THE GRAY LEVEL CO-OCCURANCE MATRIX. UNSPECIFIED thesis, UNSPECIFIED.

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Abstract

The fishing industry faces challenges in maintaining the quality of its catch, particularly fish freshness. This study develops a freshness assessment method using Gray-Level Co-occurrence Matrix (GLCM)-based digital image analysis. Milkfish images were captured in three different spectra: RGB, UV, and thermal. Texture features such as homogeneity, contrast, energy, and entropy were extracted from the RGB and UV images, while temperature data was processed using a Region of Interest (ROI) approach on thermal images. The analysis, conducted using MATLAB, showed a significant correlation between image features and fish freshness. This method provides accurate and consistent predictions and improves the efficiency of quality control and supply chain processes in the fishing industry, addressing the limitations of traditional methods, which are often subjective and time-consuming.

Item Type: Thesis (UNSPECIFIED)
Uncontrolled Keywords: Digital Image Analysis, Chanos Chanos, GLCM, Fish Freshness, MATLAB, Quality Control
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Teknik > Program Studi Teknik Informatika
Depositing User: 1462100231 Crisna Yudha Priyanto
Date Deposited: 12 Jun 2026 07:47
Last Modified: 12 Jun 2026 07:47
URI: http://repository.untag-sby.ac.id/id/eprint/38844

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