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Journal : Jupiter

simulasi RFID dari supply chain management menggunakan blockchain Ahmad Fali Oklilas; Arif Tumpal Leonardo Sianturi; Huda Ubaya; Rossi Passarella; iman saladin b. azhar
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6502/15.jupiter.2023.04

Abstract

Blockchain is a collection of blocks that are placed in a sequential order and can hold data containing past transactions. The blockchain has various qualities and benefits, including the fact that it is decentralized and immutable. Blockchain is widely employed in different sectors due to its advantages; one example is supply chain management. In this study, supply chain management is performed with two RFID simulation scenarios, where RFID antennas operate as supply chain management nodes grouped in such a manner as to establish a flow of goods delivery trips, and then products travel is carried out with RFID tags as product IDs. This study creates a simulation tool that can include supply chain management data into the blockchain, having transparency, traceability, and data security.Keywords: Blockchain, Supply chain management, RFID, Data Security, Transparency, Traceability, Immutable.
Akurasi Pengujian Model Hasil Training menggunakan YOLOv4 untuk Pengenalan Kendaraan di Jalan Raya Ahmad Fali Oklilas; sukemi; Dinda Dwinta; Ghinadhia Shofi; Nanda Putri Mariza; Sri Arum Kinanti; Yulia Amanda Sari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6537/15.jupiter.2023.04

Abstract

Traffic congestion is currently the main problem that occurs in big cities in Indonesia.Traffic flow analysis is an important basis for urban planning. Management of IntelligentTransportation System (ITS) has become a necessity today to manage heavy traffic problems.Intelligent transportation systems using computer vision techniques are increasingly attractingattention for traffic density detection. This research uses the You Only Look Once (YOLO version4 object detection method for vehicle classification and detection to obtain an optimal model.Testing the YOLOv4 model results in a mean average precision (mAP) of 80.12%. In video testingto detect motorcycles and cars, the total vehicle accuracy is 70.6% and the vehicle confidencelevel is 78.7%.