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Agus Ponco
Politeknik Astra
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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PENGEMBANGAN SISTEM MONITORING KINERJA ALAT BERAT BERBASIS GSM DENGAN WEB DASHBOARD PADA KOMATSU GD825 Agus Ponco; Zefanya Ramayudha; Syahril Ardi
Technologic Vol 17 No 1 (2026): TECHNOLOGIC
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/technologic.v17i1.540

Abstract

Perkembangan teknologi pada industri alat berat menuntut sistem monitoring kinerja yang andal, real-time, dan mampu beroperasi pada kondisi jaringan terbatas guna meningkatkan efisiensi operasional serta meminimalkan downtime. Namun, sebagian besar sistem monitoring masih bergantung pada konektivitas jaringan stabil dan belum memiliki mekanisme penyimpanan data yang adaptif saat terjadi gangguan komunikasi. Penelitian ini bertujuan merancang sistem monitoring kinerja alat berat berbasis GSM pada unit Komatsu GD825 dengan pendekatan dual-mode data handling, yaitu kombinasi transmisi data real-time dan penyimpanan lokal sebagai mekanisme fail-safe. Sistem dikembangkan menggunakan ESP32, Arduino Nano, modul SIM800L, GPS, dan SD Card, serta divisualisasikan melalui platform ThingsBoard. Hasil pengujian menunjukkan sistem mampu beroperasi dengan latency komunikasi sebesar 850–1200 ms, packet loss di bawah 3%, dan reliability mencapai 96,5%. Sensor suhu PT100 memiliki deviasi pengukuran antara −0,6°C hingga +0,5°C, menunjukkan tingkat akurasi yang baik. Implementasi dual-mode data handling terbukti mampu menjaga kontinuitas data saat terjadi gangguan jaringan. Dibandingkan sistem konvensional, sistem ini lebih adaptif terhadap kondisi jaringan terbatas serta mendukung deteksi dini kondisi abnormal sebagai bagian dari predictive maintenance.
Design and Implementation of an IoT-Enabled Deep Learning Vision System for Automated Dimensional Measurement in Smart Manufacturing Waluyo Nugroho; Afianto; Agus Ponco
Jurnal E-Komtek Vol 9 No 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2855

Abstract

The rapid advancement of Industry 4.0 has brought the convergence of Internet of Things (IoT), computer vision, and deep learning to enhance automation and precision in manufacturing. This paper presents the design and implementation of an IoT-enabled deep learning vision system for automated dimensional measurement, integrated with programmable logic controller (PLC) control and real-time monitoring. The system employs a Raspberry Pi 5 as an edge computing unit, Logitech C270 camera for visual data acquisition, and an Omron CP2E PLC for process control. A YOLOv5 deep learning model is trained to detect and measure object dimensions with sub-millimeter accuracy. The Node-RED platform is utilized for dashboard visualization and communication, interfaced through Omron FINS protocol, with MySQL as the database for data logging. Experimental results show a high detection accuracy of 98.6% and an average measurement error of less than 0.5 mm, demonstrating the system’s effectiveness for smart manufacturing applications.
Co-Authors Afianto Syahril Ardi Waluyo Nugroho Zefanya Ramayudha