Sistem Monitoring dan Klasifikasi Tingkat Keamanan Beban pada Overhead Crane Menggunakan Metode Fuzzy Logic

Prakoso, Dimas Bagus (2026) Sistem Monitoring dan Klasifikasi Tingkat Keamanan Beban pada Overhead Crane Menggunakan Metode Fuzzy Logic. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Overhead crane merupakan peralatan pengangkat yang banyak digunakan di industri dan rentan mengalami kondisi overload sehingga berpotensi menimbulkan kecelakaan kerja. Penelitian ini bertujuan merancang dan mengimplementasikan sistem monitoring dan klasifikasi tingkat keamanan beban pada overhead crane berbasis Internet of Things (IoT) dengan metode fuzzy logic Mamdani. Data beban diakuisisi menggunakan sensor shear-pin loadcell tipe DBEP melalui controller MYPIN LM8-RRD, sedangkan data arus motor dibaca dari inverter Schneider ATV320. Kedua data dikirim melalui protokol Modbus RS485 menuju gateway, diteruskan ke Virtual Private Server melalui jaringan 4G dengan protokol TCP/IP, disimpan pada basis data time-series InfluxDB, kemudian divisualisasikan pada antarmuka website. Logika fuzzy Mamdani dengan variabel masukan beban dan arus mengklasifikasikan kondisi crane menjadi empat kategori, yaitu Sangat Aman, Aman, Peringatan, dan Overload, yang ditampilkan sebagai indikator tingkat keamanan beban pada website. Hasil pengujian menunjukkan seluruh subsistem berfungsi baik: pengiriman data ke VPS tanpa packet loss, pembacaan loadcell dan arus akurat dengan selisih rata-rata 1,87 kg dan 0,1 A terhadap alat ukur pembanding, serta waktu tunda penyimpanan data pada InfluxDB 0 hingga 4 detik. Validasi logika fuzzy pada tujuh data uji dengan beban 7 hingga 1116 kg dan arus 1,6 hingga 3,5 A melalui perhitungan manual metode centroid, simulasi Matlab, dan program Python menghasilkan selisih maksimum 1,21% dengan klasifikasi kondisi yang konsisten. Sistem terbukti andal dan layak diterapkan untuk pemantauan keselamatan overhead crane.
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Overhead cranes are widely used in industry and prone to overload conditions that may cause workplace accidents. This study designs and implements an Internet of Things (IoT) based load safety level monitoring and classification system for an overhead crane using the Mamdani fuzzy logic method. Load data are acquired from a DBEP-type shear-pin load cell through a MYPIN LM8-RRD controller, while motor current data are read from a Schneider ATV320 inverter. Both data are transmitted via the Modbus RS485 protocol to a gateway, forwarded to a Virtual Private Server over a 4G network using TCP/IP, stored in an InfluxDB time-series database, and visualized through a website interface. The Mamdani fuzzy logic, with load and current as input variables, classifies the crane condition into four categories, namely Very Safe, Safe, Warning, and Overload, which are displayed as load safety level indicators on the website. All subsystems function properly: data transmission to the VPS occurs without packet loss, load cell and current readings are accurate with average deviations of 1.87 kg and 0.1 A against reference instruments, and the data-storage latency in InfluxDB ranges from 0 to 4 seconds. Validation on seven test data with loads from 7 to 1116 kg and currents from 1.6 to 3.5 A, using manual centroid calculation, Matlab simulation, and a Python program, yields a maximum deviation of 1.21% with consistent classification. The system is proven reliable and feasible for overhead crane safety monitoring.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Overhead Crane, Fuzzy Logic, Klasifikasi Tingkat Keamanan Beban, Load Cell, Internet of Things, Monitoring Real-time, Overhead Crane, Fuzzy Logic, Load Safety Level Classification, Load Cell, Internet of Things, Real-time Monitoring
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.546 Computer algorithms T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.585 TCP/IP (Computer network protocol) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Dimas Bagus Prakoso
Date Deposited:	05 Aug 2026 08:39
Last Modified:	05 Aug 2026 08:39
URI:	http://repository.its.ac.id/id/eprint/143214

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