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All Journal Journal of Deep Learning, Computer Vision and Digital Image Processing JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Tenriajeng, Andi Afrah
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Computer Science & IT

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Sistem Penentuan Produksi Makanan Pada Warung Sate Madura Malengkeri Menggunakan Fuzzy Logic Metode Tsukamoto Amatullah, Mutia; Sumarnie; Tenriajeng, Andi Afrah
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 2 Issue 1 Maret 2024
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v2i1.301

Abstract

The production determination system using the Tsukamoto fuzzy method can be used to determine the optimum quantity in satay production. However, if the quantity of products produced by Warung Sate Madura Malengkeri is less than the customer demand, the eatery will miss the opportunity to maximize profits, and vice versa. Therefore, planning the production quantity in Warung Sate Madura Malengkeri is crucial to meet customer demand with the appropriate quantity.
Klasifikasi Tingkat Kualitas Terung dengan Algoritma Backpropagation Berbasis Fitur Warna dan Tekstur R, Muh Raflyawan; Arifky, Reza; Tenriajeng, Andi Afrah; Kaswar, Andi Baso; Andayani, Dyah Darma; Azis, Putri Alysia
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10655

Abstract

Manual quality assessment of eggplant is often inconsistent, takes a long time, and is prone to errors due to worker fatigue. This research aims to develop an automated system based on digital image processing to assess eggplant quality efficiently and accurately. The stages begin with image capture using a mobile phone device designed to ensure stable lighting and uniform background. The acquired image is then processed through segmentation using the Otsu thresholding method as well as morphological operations to separate the main object from the background. Color and texture features are extracted through Gray-Level Co-occurrence Matrix (GLCM) analysis and RGB, HSV, and LAB color spaces. Training data amounting to 90% of the total dataset was used to train an artificial neural network-based classification model with a backpropagation algorithm, while the remaining 10% was used for testing. Experimental results showed that the combination of LAB, RGB, HSV, and texture features gave the best results, with a testing accuracy of 86%, recall of 85%, and precision of 92%. This model is very effective in detecting poor quality eggplants with 100% accuracy. This system can support the application of technology in the horticultural sector.
Co-Authors Amatullah, Mutia Andi Baso Kaswar Andi Baso Kaswar Arifky, Reza Azis, Putri Alysia R, Muh Raflyawan Sumarnie