Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi SENTRI: Jurnal Riset Ilmiah
Muhammad Rofiqul A’la
Institut Teknologi Dan Sains Nahdlatul Ulama Pekalongan
Aerospace Engineering Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice

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

Found 2 Documents
Search

 1 
System Design Of Printer Machine Using Image Classification Method Based On Iot To Optimize Production Output Yusril Ihza Tachriri; Elvinda Bendra Agustina; Dian Arif Rachman; Atika Windra Sari; Muhammad Rofiqul A’la; Muhammad Lutfhi Al Hisyam; Muhammad Irfan
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 3 (2026): SENTRI : Jurnal Riset Ilmiah, Maret 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i3.5986

Abstract

The micro, small, and medium enterprises (MSMEs) industry plays a crucial role in supporting the Indonesian economy, yet faces challenges in production efficiency and cost management. This study presents the design and development of an IoT-based automated screen-printing system that integrates edge-based image classification using a CNN model with microcontroller-driven print actuation, specifically tailored for MSME-scale garment production. The system employs an ESP32/Raspberry Pi as the edge device, enabling local inference without cloud dependency, and utilizes MQTT protocol for IoT connectivity. Quantitative evaluation across 50 test cycles demonstrated a 96% printer success rate, a 60% reduction in production time from 45 to 18 minutes per 10 shirts, a 90% reduction in labor from 10 operators to 1, and an approximately 50% reduction in per-unit production cost from Rp65,000–80,000 to Rp30,000–40,000 per shirt. IoT connectivity testing over 48 continuous hours recorded an average MQTT latency of 120 ms and a system uptime of 98.5%, confirming the reliability of the communication layer for sustained production operations. Grounded in Industry 5.0 principles, this research advances human-machine collaboration in small-scale manufacturing contexts. The proposed system offers a cost-effective, remotely controlled, and semi-autonomous production solution, representing a novel contribution to the field of IoT-based garment manufacturing in Indonesia.
Image Histogram Analysis in a Webcam-Based Non-Ionizing Tomographic System for Early Shrimp Disease Detection Yusril Ihza Tachriri; Dian Arif Rachman; Yuni Lestiani; Elvinda Bendra Agustina; Atika Windra Sari; Muhammad Rofiqul A’la; Rif’atun Romadhoni; Azka Nazhan Ristiani
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16203

Abstract

This study presents the development of a non-ionizing tomography imaging system using a webcam to detect diabetes-like metabolic disorders in Litopenaeus vannamei through digital image histogram analysis. The method employs controlled glucose injections (0–10 mL) to simulate metabolic variation, followed by optical image acquisition and RGB histogram characterization using Python-based GUI software. Calibration with a white-screen baseline ensures consistent optical response. Results show significant RGB histogram shifts correlating with glucose concentration changes, indicating optical density variations in shrimp tissues. The RGB mean intensity decreased by approximately 20–35% between 0 and 6 mL glucose concentration. Linear regression analysis within the 0–6 mL range yielded a sensitivity of 11.48 intensity units/mL with R² = 0.987, and an estimated detection limit of 0.4 mL equivalent glucose change. The method demonstrates potential as a non-invasive, low-cost diagnostic tool for early metabolic disorder detection in aquaculture.   Keywords - non-ionizing tomography, RGB histogram analysis, Litopenaeus vannamei, glucose detection, digital image processing
Co-Authors Atika Windra Sari Azka Nazhan Ristiani Dian Arif Rachman Elvinda Bendra Agustina Muhammad Irfan Muhammad Lutfhi Al Hisyam Rif’atun Romadhoni Yuni Lestiani Yusril Ihza Tachriri