Determining Tuna Grade Quality Based on Color Using Convolutional Neural Network and k-Nearest Neighbors

Tue, 12 Aug 2025 00:00:00 +0000

One of the main commodities that Indonesia exports is tuna. Indonesia's inadequate handling of food safety is demonstrated by a number of instances when the United States has rejected Indonesian fishery goods and food poisoning incidences. Fish quality grade is currently determined by manual inspection which has risk human mistake. According to Robert DiGregorio, four tuna grade classifications exist: grade 1, 2+, 2, and 3. The purpose of this study is to assess the tuna meat's quality according to its color. The procedure involves pre-processing images, training datasets, and classifying them using the Convolutional Neural Network (CNN) and k-Nearest Neighbors algorithms. CNN pre-processing involves converting the image into HSV color space and training the CNN model using 240 training datasets and 74 testing datasets. CNN’s accuracy was 84% higher than k-Nearest Neighbors' which was 54%. Additionally, a comparison of the classification accuracy of CNN, VGG (Visual Geometry Group) 16, and AlexNet revealed that CNN outperformed the others with an accuracy of 84%, followed by VGG16 with 70% and AlexNet with 66%.

Strategizing Economic Revival in Mandalika through A One-Stop Digital Collaborative Marketing Platform Upholding Local Wisdom

Tue, 12 Aug 2025 00:00:00 +0000

In the era of digital transformation, information technology plays a pivotal role in shaping economic landscapes and cultural preservation strategies. This research introduces an innovative approach to economic revitalization in Mandalika, Indonesia, through a One-Stop Digital Collaborative Marketing Platform, fully integrated with the NTBmall app, a key digital initiative by Dinas Perdagangan NTB for promoting local products. This symbiotic blend of cutting-edge digital solutions and Mandalika's rich local wisdom forms the core of our strategy, aiming to rejuvenate the region's economic vitality while safeguarding its cultural heritage. Utilizing the System Usability Scale (SUS) for evaluation, the study involved 23 street vendors and 7 Tourism Awareness Group (Pokdarwis: Kelompok Sadar Wisata). The results were compelling: street vendors recorded a usability score of 72.8, indicating good system functionality, while Pokdarwis achieved an outstanding score of 85, reflecting excellent usability. These outcomes highlight the platform's versatility in meeting diverse user needs and its significant potential in boosting Mandalika's economy. By merging innovative IT solutions with traditional values, this research not only propels economic growth in Mandalika but also sets a scalable and replicable model for similar developments globally, showcasing the transformative power of information technology in regional development.

Lontar Komputer : Jurnal Ilmiah Teknologi Informasi

Determining Tuna Grade Quality Based on Color Using Convolutional Neural Network and k-Nearest Neighbors

Strategizing Economic Revival in Mandalika through A One-Stop Digital Collaborative Marketing Platform Upholding Local Wisdom