Aplikasi Penentuan Kesegaran Ikan Bandeng Menggunakan Metode Convolution Neural Network

Authors

  • Suprianto STMIK PPKIA Tarakanita Rahmawati
  • Dwi Shintya Lestari STMIK PPKIA Tarakanita Rahmawati
  • Okky Herodion Simung STMIK PPKIA Tarakanita Rahmawati

DOI:

https://doi.org/10.33506/insect.v8i2.2196

Keywords:

citra digi, ikan bandeng, convolutional neural network, android

Abstract

Milkfish is a consumption fish, one of which is the most sold in Tarakan City. Fish is included in the food that quickly decreases the quality of its freshness. One way to see the level of freshness of fish is to look at the eyes. For people who are used to recognizing the freshness of fish, of course there will be no obstacles, but for ordinary people it will be a problem in itself. This problem can be solved by utilizing digital image processing techniques, therefore an application is needed to determine the freshness level of milkfish through fish eye images using the CNN (Convolutional Neural Network) method. The way the method works is to take the RGB value of the photo image on the eye of the milkfish, then perform a convolution by moving a 3 x 3 carnel filter on the image, so that the computer will find a new representative information, namely from the result of multiplying the part of the image with the filter used, until finally got 2 values for classification. The results of the analysis using 24 test data, where the accuracy of detection of milkfish freshness with a flashlight camera detection menu and a distance of ± 5 cm is 87.5%, detection with a camera without a flashlight and a distance of ± 5 cm is 66.67%, detection with a camera flashlight and distance of ± 10 cm is 29.17%, detection with camera without flashlight and distance of ± 10 cm is 66.67%, and detection from gallery is 91.67%. Each test uses 24 test data.

References

Halim, C., Prasetyo, H. (2018). Penerapan Artificial Intelligence dalam Computer Aided Instructure (CAI). Jurnal Sistem Cerdas, 1(1), 50-57. https://doi : 10.37396/jsc.v1i1.6

Suartika, W., Putra, E. (2017). Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS Vol.5 No.1. https://doi: 10.12962/j23373539. v5i1.15696

Supii, A. I., Widyastuti, Z., dkk. (2021). Pendederan Ikan Bandeng pada Keramba Jaring Apung Sebagai Alternatif Pemanfaatan Waduk Palasari, kabupaten Jembrana, Bali. Samakia: Jurnal Ilmu Perikanan, 12(2), 96-102.https://doi: 10.35316/jsapi.v12i2.1001

Ramadhan, M. D., Setiyono, B. (2019). Pengolahan Citra untuk Mengetahui Tingkat Kesegaran Ikan Menggunakan Metode Transformasi Wavelet Diskrit. J. Sains dan Seni ITS Vol.8 No.1.https://doi: 10.12962/j23373520. v8i1.37715

NURQADERIANIE, A. S., METUSALACH, M., dkk. (2017). Tingkat Kesegaran Ikan Kembung Lelaki (Rastrelliger Kanagurta) yang Dijual Eceran Keliling di Kota Makassar. Jurnal IPTEKS Pemanfaat. Sumber Perikan Vol.3 No.6. https://doi: 10.20956/jipsp.v3i6.3062

RATNA, S. (2020). Pengolahan Citra Digital Dan Histogram Dengan Python Dan Text Editor Phycrarm. Jurnal Ilmiah Technologia Vol.11 No.3. https://doi: 10.31602/tji.v11i3.3294

Saprudin, Amalia, R., dkk. (2021). Klasifikasi Citra Menggunakan Metode Random Forest dan Sequential Minimal Optimization (SMO). Jurnal Sitem dan Teknologi Informasi. Vol.9 No.2. https://doi :10.26418/justin. v9i2.44120

Nugroho, P. A., Fenriana, I. (2020). Implementasi Deep Learning Mengggunakan Convolutional Neural Network (CNN) Pada Ekspresi Manusia. Jurnal Algor Vol.2 No.1

Sugiarto, W., Kristian, Y., dkk. (2018). Estimasi Arah Tatapan Mata Menggunakan Ensemble Convolutional Neural Network. Jurnal Teknologi Informasi dan Komunikasi, 7(2), 94-101. https://doi: 10.34148/teknika.v7i2.126

Geraldy, C., Lubis, C. (2017). Pendeteksian dan Pengenalan Jenis Mobil Menggunakan Algoritma You Only Look Once Dan CNN. Jurnal Ilmu Komputer dan Sistem Informasi Vol.2 No.1.https://doi: 10.24912/jiksi. v8i2.11495

Prof. Bhairnallykar, S., Prajapati, A., dkk. (2020). Convolutional Neural Network (CNN) untuk Deteksi Gambar. Jurnal Rekayasa dan Teknologi Vol.7 No.11

Pangestu, M. A., Bunyamin, H. (2018). Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model. Jurnal Teknik Informatika dan Sistem Informasi Vol.4 No.2. https://doi: 10.28932/jutisi.v4i2.828

Published

2023-03-31

How to Cite

Suprianto, Lestari, D. S., & Simung, O. H. (2023). Aplikasi Penentuan Kesegaran Ikan Bandeng Menggunakan Metode Convolution Neural Network. Insect (Informatics and Security): Jurnal Teknik Informatika, 8(2), 77–86. https://doi.org/10.33506/insect.v8i2.2196

Issue

Section

Articles