Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Informatika (TEKINFO)
Faidatul Hanafiyah
Universitas Ibrahimy
Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering Social Sciences Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deteksi Tingkat Kematangan Buah Pisang Berdasarkan Citra Digital Menggunakan Metode Convolutional Neural Network (CNN) Faidatul Hanafiyah; Farihin Lazim; Firman Santoso
Jurnal Ilmiah Teknik Informatika (TEKINFO) Vol. 27 No. 2 (2026): TEKINFO Vol 27 No 2 Oktober 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

color of the fruit's skin. This method often produces different assessment results dueto the influence of subjective human factors. This research aims to create a system thatcan recognize banana ripeness using digital images using a convolutional neuralnetwork (CNN) method. Data were collected by photographing bananas using a mobilephone camera, and the bananas were categorized into four categories: unripe, ripe,overripe, and rotten. The research process consisted of several steps: data collection,data labeling, image processing by resizing the images to 224x224 pixels, normalizingpixel values, converting the color format to RGB, data augmentation, training the CNNmodel, and evaluating model performance. The CNN architecture used consisted ofthree convolutional layers, three max-pooling layers, a flatten layer, a dropout layer, anda dense layer. Tests demonstrated the model's ability to classify banana ripeness with up to 96% accuracy. Good precision, recall, and F1-score values indicate the CNNmethod's success in recognizing banana ripeness from digital images.
Co-Authors Firman Santoso Lazim, Farihin