Sistem Pencatatan Barang Masuk Gudang Berbasis Identifikasi Frekuensi Radio dan Pengenalan Visual Kondisional - Zefanya Rivaldy Fattihandel Machmud; Ali Akbar Steven Ramschie; Ronny Evert Katuuk (TIN: Terapan Informatika Nusantara, 2026)
The process of recording incoming goods at the Multimart Tomohon warehouse is currently entirely manual using paper and spreadsheets, resulting in four systemic problems: data entry errors due to human fatigue, delays in stock updates that hinder real-time decision-making, the inability to reliably track item history, and the absence of a cross-verification mechanism between supplier-claimed quantities and physical quantities received. This research aims to design and test a prototype of an Internet of Things-based incoming goods recording system that integrates radio frequency identification (RFID), infrared sensors, Optical Character Recognition (OCR), and a Convolutional Neural Network (CNN) in a two-box architecture connected by a belt conveyor. The development methodology follows the SDLC Prototyping model consisting of five iterative stages: requirements gathering, rapid design, prototype construction, evaluation and testing, and refinement. The MobileNetV2 architecture was chosen as the CNN backbone because it uses depthwise separable convolution that produces a lightweight model with competitive accuracy, suitable for servers that also process RFID data simultaneously. RFID testing demonstrated accurate tag reading at distances of 1-4 cm with slow to moderate conveyor speeds (∼3-8 cm/s), while at distances of 5-7 cm or high speeds (∼15 cm/s) readings became unreliable. Training a MobileNetV2 CNN model with 30 photos per class on the Multimart Tomohon product dataset demonstrated convergence to 100% validation accuracy at epoch 4-5 under small, controlled dataset conditions; these results are condition-specific for Box 1 LED strip lighting and require full retraining each time a new product class is added. Real-time identification testing confirmed that the CNN dominates the identification of products with unobstructed packaging (94% confidence level), while OCR serves as a fallback for products with visible SKU codes or label-obscured packaging, with a system throughput of 66.7 FPS. The key contributions of this research are a two-box architecture that simultaneously eliminates sensor interference and enables automatic fraud detection through RFID-versus-infrared cross-verification, a conditional OCR/CNN pipeline capable of handling all product types regardless of SKU availability, and empirical evidence of system feasibility generalizable to small and medium retail warehouse contexts.