Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Mathematics and Technology (MATECH)
Widodo, Dian
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IDENTIFICATION OF BANANA FRUIT USING BACKPROPAGATION METHOD Widodo, Dian; Fauzi, Achmad; Sembiring, Arnes
Journal of Mathematics and Technology (MATECH) Vol. 2 No. 2 (2023): Journal MATECH (November 2023)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v2i2.155

Abstract

Identifikasi jenis buah pisang dan penilaian tingkat kematangannya merupakan proses yang penting dalam industri pertanian dan distribusi. Dalam upaya untuk mengotomatisasi proses ini, penulis menyarankan pendekatan pemaparan buah pisang dan tingkat kematangannya menggunakan jaringan saraf tiruan Backpropagation . Melalui proses pengolahan citra digital, citra atau gambar dari buah pisang akan dilakukan ekstraksi ciri-ciri seperti RGB ( red green blue ), metrik dan eksentrisitas(ciri bentuk). Hasil proses training data citra sebanyak 55 data citra yang diinputkan, diperoleh proses training data jenis pisang dengan 11 iterasi dari inputan maksimum epoch 10000, target error atau performance 0.00642 dengan nilai rata-rata sebesar 80%. Selanjutnya diperoleh proses data pelatihan tingkat kematangan pisang dengan 4 iterasi dari input maksimum epoch 10000, target error atau performance 0.00606 dengan nilai akurasi sebesar 90%. Dari proses uji citra yang telah dilakukan bahwa sistem dapat mengidentifikasi jenis buah pisang beserta tingkat kematangannya berdasarkan inputan ekstraksi fitur dari citra buah pisang. Penelitian ini juga bertujuan untuk menguji dan mengetahui tingkat akurasi penerapan metode Backpropagationdalam mengidentifikasi jenis buah pisang dan tingkat kematangannya.
Identification of Banana Fruit Types Using the Backpropagation Method Widodo, Dian; Fauzi, Achmad; Sembiring, Arnes
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.314

Abstract

Identification of types of bananas and assessment of their maturity level is an important process in the agricultural and distribution industries. In an effort to automate this process, the authors propose an approach to identify bananas and their level of ripeness using a Backpropagation neural network. Through digital image processing, images or pictures of bananas will be extracted with images such as RGB (red green blue), metric and eccentricity (shape features). The results of the image data training process are as many as 55 image data input, obtained by the training process data on banana types with 11 iterations from the maximum input epoch 10000, target error or performance 0.00642 with an accuracy value of 80%. Furthermore, the training process obtained data on the maturity level of bananas with 4 iterations from the maximum input epoch 10000, the target error or performance is 0.00606 with an accuracy value of 90%. From the test image process that has been carried out, the system can identify the type of banana and its maturity level based on the feature extraction input from the image of the banana. This study also aims to test and determine the accuracy of the application of the Backpropagation method in identifying the types of bananas and their level of maturity.
Co-Authors Achmad Fauzi Sembiring, Arnes