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All Journal Sainteks: Jurnal Sain dan Teknik
Fahmi Fawzy Rusman
Akademi Komunitas Industri Tekstil dan Produk Tekstil Surakarta
Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Development of an Internet of Things–based fabric defect recording system with automatic length measurement using a rotary encoder Andrian Wijayono; Fahmi Fawzy Rusman; Nurfadilah Ikhsani; Reffli Ghandara
SAINTEKS : Jurnal Sain dan Teknik Vol. 8 No. 01 (2026): Maret
Publisher : Universitas Insan Cendekia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37577/sainteks.v8i01.1079

Abstract

The textile industry requires efficient, accurate, and real-time quality control systems to replace paper-based inspection methods prone to errors and delays. This study develops and evaluates an Internet of Things (IoT)-based fabric defect recording system integrating a rotary encoder for length measurement, Arduino Uno as the controller, ESP32 as the communication module, a keypad for operator input, and a web-based MySQL database. The system automatically measures fabric length, records defect types, and transmits data wirelessly for real-time monitoring and management. Validation was conducted by comparing the proposed system with manual measurement and industrial inspection machines. One-Way ANOVA results show no significant difference in measurement accuracy (p = 0.865 > 0.05), with a Mean Absolute Error (MAE) of 0.0074 m, indicating high precision. Efficiency testing using a paired sample t-test shows a 79.2% reduction in recording time, from 16.8 seconds to 3.5 seconds (using digital recording system), with a significant difference (p < 0.001). The system also demonstrates reliable performance with low latency (120–150 ms), high repeatability, and zero data loss through buffering during network disruptions. These results indicate that the system improves operational efficiency while maintaining accuracy comparable to conventional methods and supports real-time integrated data management for textile industry digital transformation.
Co-Authors Nurfadilah Ikhsani Reffli Ghandara Wijayono, Andrian