Portable Construction Maps (PCM) using location fingerprint positioning algorithm for construction worker safety

Annurdien Rasyid; Ahsan Firdaus; Dwi Setiawan; Hajidah Salsabila Allissa Fitri; Pradana Setialana

doi:10.21831/jeatech.v3i1.43666

Portable Construction Maps (PCM) using location fingerprint positioning algorithm for construction worker safety

Annurdien Rasyid, Universitas Negeri Yogyakarta, Indonesia
Ahsan Firdaus, Universitas Negeri Yogyakarta, Indonesia
Dwi Setiawan, Universitas Negeri Yogyakarta, Indonesia
Hajidah Salsabila Allissa Fitri, Universitas Negeri Yogyakarta, Indonesia
Pradana Setialana, Universitas Negeri Yogyakarta, Indonesia

10.21831/jeatech.v3i1.43666

Abstract

The existence of a work from home policy does not seem to have a substantial impact on construction workers. Cases of work accidents on infrastructure projects during the Covid-19 pandemic experienced a significant increase. As reported by BPJamsostek data that the number of work accident insurance claims in the first semester (January-June) 2020 reached 108,573 cases. An increase of 128% over the same period in the previous year. One of the steps from the construction side to minimize the occurrence of work accidents is by marking dangerous locations or limiting them with a yellow line. The supervision is carried out by the Occupational Health and Safety division which supervises every worker by using hearing and sight senses. However, this supervision is deemed less effective and efficient considering the number of work accidents that continue to increase over time. Therefore, a worker location monitoring system based on An indoor positioning system called Portable Construction Maps (PCM) Using Location Fingerprint Positioning Algorithm for Construction Worker Safety was developed. The development of this system uses the algorithm Location Fingerprint as a method for estimating the location of workers in a construction building. Each worker will bring a device called the Worker Tag, in which there is a microcontroller (Espressif 32) with WiFi and Bluetooth module, which is used to capture wifi and bluetooth signals and calculate the Received signal strength indication(RSSI) which will be sent to the server to be processed using the location fingerprint algorithm. In addition, in the worker tag there is also a Passive Buzzer that is used to alert workers if they enter a dangerous area.

Keywords

Worker Safety; Location Fingerprint; Machine Learning; K Nearest Neighbor; Indoor Positioning

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DOI: https://doi.org/10.21831/jeatech.v3i1.43666