Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Bulletin of Computer Science Research JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Utama Putra, Kharisma
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Klasifikasi Kematangan Buah Pepaya Berdasarkan Warna Menggunakan Convolutional Neural Network Utama Putra, Kharisma; Yosfand, Windra; Ramadhanu, Agung
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 1 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.1.283

Abstract

Dengan kemajuan teknologi di bidang pengolahan citra digital (digital image processing) menjadi daya tarik tersendiri dalam mempermudah kehidupan manusia sehingga memunculkan banyak aplikasi yang dapat menerapkannya dalam berbagai bidang. Metode digital image processing dapat mentransformasikan citra masukan menjadi citra keluaran yang dapat dimanfaatkan untuk mengidentifikasi dan mengklasifikasi objek dalam kehidupan. Buah pepaya merupakan buah yang sering dikonsumsi manusia. Proses pemanenan buah pepaya dapat dilakukan menggunakan metode visual dengan memperhatikan warna dan ukuran buah. Pada penelitian ini beberapa sampel buah pepaya diambil nilai RGB dan dilakukan pengolahan dengan metode convutional neural network untuk mendapatkan tingkat kematangan dari buah pepaya. Hasil akhir dari penelitian ini bertujuan untuk membuat aplikasi yang dapat melakukan identifikasi dan mengklasifikasikan objek. Dalam pengujian sistem diperoleh persentase tingkat keberhasilan sebanyak 95%.
Identifikasi Varietas Kopi Berdasarkan Analisis Warna dan Tekstur Menggunakan Metode Convolutional Neural Network Utama Putra, Kharisma; Ramadhanu, Agung; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.759

Abstract

Coffee is a plantation commodity with high economic value in Indonesia, with various varieties such as Arabica, Robusta, and Liberica. Differences in coffee varieties can generally be identified through the physical characteristics of the beans, especially color and texture. Based on this, this study aims to develop a digital image-based coffee variety identification system using the Convolutional Neural Network (CNN) method with color and texture analysis as the main features. The research stages include coffee bean image acquisition, pre-processing including color segmentation and image conversion to grayscale, and color and texture feature extraction. This research dataset comes from images of unroasted coffee beans, commonly called green beans, taken using a high-resolution smartphone camera and also using secondary data taken from the Kaggle site. Both types of datasets have the same characteristics and resolution to maintain data consistency. The image dataset is divided into training data and test data, then used to train and test the Convolutional Neural Network (CNN) model. Based on this study, the Convolutional Neural Network (CNN) method can identify coffee varieties based on color and texture analysis. By using 210 training data and 90 test data of coffee bean images, the CNN method can produce an accuracy rate of 94,44%. This research contribution has the potential to be a supporting solution in the process of identifying coffee varieties quickly, accurately, and consistently, so that it can help the coffee industry in the sorting and quality control process.
Co-Authors Ramadhanu, Agung Syafri Arlis Yosfand, Windra