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

doi:10.55681/sentri.v5i3.5986

Authors

Yusril Ihza Tachriri Institut Teknologi dan Sains Nahldatul Ulama Pekalongan
Elvinda Bendra Agustina Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan
Dian Arif Rachman Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan
Atika Windra Sari Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan
Muhammad Rofiqul A’la Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan
Muhammad Lutfhi Al Hisyam Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan
Muhammad Irfan Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan

DOI:

https://doi.org/10.55681/sentri.v5i3.5986

Keywords:

MSMEs, IoT, Image Classification, t-shirt, screen printing

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.

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