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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer BERNAS: Jurnal Pengabdian Kepada Masyarakat Mechanical Engineering for Society and Industry Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Ramatekno Cendikia: Jurnal Pendidikan dan Pengajaran Jurnal Polimesin Journal of Applied Science and Advanced Engineering
Deni Kurnia
Politeknik Enjinering Indorama
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Pelatihan PLC Untuk Peningkatan Kompetensi Guru SMK di Purwakarta dan Karawang Nugroho, Emmanuel Agung; Kurnia, Deni; Afzeri; supriyanto, Adolf Asih; Riyadi, Slamet; Wibowo, Nanang Roni; Rahayu, Widya Andayani
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.11590

Abstract

Kegiatan pengabdian kepada masyarakat ini diselenggarakan oleh Program Studi Teknologi Rekayasa Mekatronika, Politeknik Enjinering Indorama, dan diikuti oleh 10 orang guru SMK dari tiga sekolah di Purwakarta dan Karawang. Kegiatan ini bertujuan untuk meningkatkan kompetensi guru dalam bidang teknologi Programmable Logic Controller (PLC) melalui serangkaian pelatihan yang meliputi pemrograman menggunakan CX Programmer, desain menggunakan CX Designer, serta menghubungkan program dengan hardware PLC Omron CP1E. Melalui kuesioner yang dibuat oleh panitia, para guru menyatakan bahwa pelatihan ini penting untuk meningkatkan kompetensi mereka dalam menyiapkan lulusan SMK yang siap menghadapi tantangan kebutuhan teknologi di industri. Pelatihan ini diharapkan dapat membantu para guru mengimplementasikan teknologi PLC di sekolah mereka, sehingga dapat meningkatkan kualitas pendidikan teknologi bagi siswa-siswi SMK.
A hybrid pareto–fishbone and IoT-based monitoring framework for reducing DTY yarn defects Deni Kurnia; Hanif Fakhrurroja; Marno Marno; Joniko Joniko
Jurnal Polimesin Vol 23, No 6 (2025): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i6.7678

Abstract

Quality Control (QC) challenges in the textile industry increasingly require data-driven and real-time solutions to reduce critical production defects. This research aims to develop a hybrid Pareto-Fishbone analysis integrated with an IoT-based monitoring framework to reduce the incidence of dominant defects in Draw Textured Yarn (DTY) yarns (X-stitch and Broken Filament). Defect data collected in 2024 (n=2,396) and early 2025 (n=1,177) were analyzed using Pareto charts, which identified X-stitch (40.15%) and Broken Filament (37.15%) as contributing 77.3% of total defects in 2024. Fishbone diagrams traced root causes to machine vibration and yarn tension anomalies. An IoT prototype was designed using ADXL345 vibration sensors (200 Hz sampling), tension monitoring, and MQTT communication to a Node-RED dashboard to enable real-time alerts. Preliminary testing achieved 95% MQTT transmission success and detected vibration anomalies correlating with 85% of X-stitch incidents. The proposed hybrid framework combines the diagnostic strength of Pareto–Fishbone analysis with the preventive capability of IoT monitoring, offering a scalable Industry 4.0-oriented solution for textile QC and predictive maintenance.
Smart vibration sensing and predictive analytics for intelligent textile manufacturing: An IoT-edge and machine learning method Kurnia, Deni; Sutanto, Agus; Fakhrurroja, Hanif; Son, Lovely
Mechanical Engineering for Society and Industry Vol. 5 No. 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.13588

Abstract

This study aimed to propose an IoT-Edge method for detecting vibration abnormalities in Textile Manufacturing, specifically on Draw Texturing Yarn (DTY) machines using an ADXL345 sensor and a Machine Learning Algorithm. The proposed system incorporated wireless sensor nodes, the MQTT protocol, Fast Fourier Transform (FFT) analysis, and a tuned Random Forest (RF) classifier to enable real-time monitoring as well as predictive maintenance. During the analysis, vibration data were collected from 13 spindles, with features extracted in both time as well as frequency domains to distinguish between normal and abnormal machine conditions. The RF model, optimized through hyperparameter tuning, achieved an accuracy of 97%, significantly outperforming the Support Vector Machine (SVM) baseline, which reached 71%. Major results showed the effectiveness of energy and centroid features in fault detection, with the Z-axis vibration proving to be a good indicator of yarn defects. The system presented low latency (average 20.37 ms) in data transmission using the MQTT protocol, ensuring practical deployability. This study offered a scalable and cost-effective solution for industrial vibration monitoring, bridging gaps in real-time processing and seamless IoT incorporation to support predictive maintenance in textile manufacturing.
Co-Authors Adolf Asih Supriyanto Afzeri Afzeri Afzeri Afzeri Tamsir Agus Sutanto Dwi Kartini, Dwi Emmanuel Agung Nugroho Fakhrurroja, Hanif Friska Abadi Hadisantoso, Feri Siswoyo Hakim, Muhammad Nur Imam Muis H Irmani, Irmani Itqan Mazdadi, Muhammad Jalil, Lisya Auliyah Joniko Joniko Kiki Pratiwi Koto, Muthya Khairunnisa Lovely Son, Lovely Marno Marno Nurmala , Indriani Siti Radityo Adi Nugroho Rahayu, Widya Andayani Sabrina Aisyah Putri Sinaga, M. Hendri Sugara Slamet Riyadi Slamet Riyadi Supriyanto, Adolf Asih Syahril, Ahmad Tiawan Wibowo, Nanang Roni Widiawati, Windi