Rancang Bangun Sistem Ticketing Teknisi Berbasis Raspberry Pi dengan Otentikasi Biometrik.

Abstract

Pengelolaan laporan penyelesaian pekerjaan teknisi di PT Geo Tekno Globalindo yang masih mengandalkan koordinasi manual berbasis pesan instan memicu tingginya waktu proses administratif hingga rata-rata 27,72 menit per tiket serta rentan terhadap rendahnya akuntabilitas identitas pelapor. Penelitian ini merancang dan membangun smart kiosk yang mengintegrasikan verifikasi wajah berbasis ekstraksi fitur embedding pada edge computing Raspberry Pi guna mengotomatisasi validasi laporan penyelesaian pekerjaan teknisi secara mandiri. Sistem dibangun menggunakan Raspberry Pi 4, kamera USB, dan layar sentuh LCD berbasis Python dengan pustaka face_recognition, serta Google Sheets API untuk sinkronisasi data real-time. Pengujian operasional selama dua minggu pada Juni 2026 menunjukkan waktu respons rata-rata 0,52 menit dan peningkatan efisiensi sistem sebesar 98% dibandingkan metode manual, dengan performa optimal pada jarak 30–60 cm dan pencahayaan di atas 300 lux. Sistem terbukti memangkas birokrasi pelaporan manual serta memperkuat akuntabilitas operasional korporat.

The management of technician job completion reports at PT Geo Tekno Globalindo still relies on manual coordination through instant messaging, resulting in a high administrative processing time of an average of 27.72 minutes per ticket and creating risks related to low accountability of reporter identity. This study aims to design and develop a smart kiosk that integrates face verification based on embedding feature extraction using Raspberry Pi-based edge computing to automate the validation of technician job completion reports independently. The system was developed using a Raspberry Pi 4, USB camera, and LCD touchscreen interface programmed in Python with the face_recognition library, along with the Google Sheets API for real-time data synchronization. Operational testing conducted over a two-week period in June 2026 demonstrated an average response time of 0.52 minutes and a 98% increase in system efficiency compared to the manual reporting method. The system achieved optimal performance at a face detection distance of 30–60 cm and illumination levels above 300 lux. The developed system effectively reduces manual reporting bureaucracy and enhances operational accountability within the organization.