Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Teknik Informatika (JUTIF)
Nugraha, Dwi Cahya Astria
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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

Found 2 Documents
Search

 1 
Pengembangan Aplikasi Pendukung Pengolahan dan Pelayanan Kopi: Kasus di KTH Wonosantri Yulianto, Muhammad Luqman Fajar; Aknuranda, Ismiarta; Nugraha, Dwi Cahya Astria
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 7 (2025): Juli 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

KTH Wonosantri Abadi merupakan salah satu pelaku usaha mikro yang bergerak dalam bidang pengolahan dan distribusi kopi di Kabupaten Malang. Sistem pencatatan operasional yang masih bersifat manual menimbulkan sejumlah permasalahan, seperti kesalahan input data, keterlambatan dalam merespons permintaan pelanggan, serta kesulitan dalam melacak riwayat produksi kopi. Penelitian ini bertujuan untuk mengembangkan aplikasi mobile berbasis Android yang mendukung digitalisasi pencatatan stok dan pemantauan proses pengolahan kopi. Proses pengembangan dilakukan dengan menggunakan model Waterfall, yang mencakup tahapan berurutan mulai dari analisis kebutuhan, perancangan sistem, implementasi, hingga pengujian dan pemeliharaan. Hasil pengujian sistem dilakukan melalui pendekatan black-box, white-box, usability testing, serta pengujian efisiensi. Pengujian menunjukkan bahwa aplikasi yang dikembangkan mampu meningkatkan efisiensi waktu kerja hingga lebih dari 70% pada proses penting seperti pencarian preferensi pengolahan dan pelacakan batch produksi. Selain itu, hasil evaluasi usability menggunakan metode System Usability Scale (SUS) menunjukkan skor tinggi, yang mengindikasikan bahwa sistem mudah digunakan dan sesuai dengan kebutuhan pengguna. Penelitian ini diharapkan dapat berkontribusi pada upaya transformasi digital UMKM di sektor kopi, khususnya dalam meningkatkan efisiensi operasional dan kualitas pelayanan kepada pelanggan.
A Comparative Analysis of Color Channel-Based Feature Extraction using Machine Learning versus Deep Learning for Food Recognition Sari, Yuita Arum; Nugraha, Dwi Cahya Astria; Adinugroho, Sigit
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5001

Abstract

Automated Dietary Assessment Accurate food recognition is a big challenge in computer vision which is critical for developing Automated Dietary assessment and health monitoring systems. The key question it answered was whether traditional machine learning with feature engineering by hand can beat modern deep learning approaches? In this Context, this study serves as a comparative analysis of these two paradigms. The baseline method worked by extracting texture (LBP,GLCM) and color information from different channels of five colors spaces (RGB, HSV, LAB, YUV,YCbCr) followed by feeding these features into multiple classifiers such as Nearest Neighbor(NN), Decision Tree and Naïve Bayes. These were then compared to deep learning models (MobileNet_v2, ResNet18, ResNet50, EfficientNet_B0). The best traditional one can reach an accuracy of 93.33%, using texture features extracted from the UV channel and classified with a NN. Nevertheless, the deep learning models consistently presented higher performance and MobileNet_v2 reached up to 94.9% accuracy without requiring manual feature selection. In this paper, we show that end-to-end deep learning models are more powerful and error robust for food recognition. These results highlight their promise for constructing more effective and scalable real-world applications with less need for intricate, domain-specific feature engineering.
Co-Authors Ismiarta Aknuranda Sigit Adinugroho Yuita Arum Sari Yulianto, Muhammad Luqman Fajar