PENINGKATAN AKURASI SISTEM PEMISAH BARANG OTOMATIS DENGAN METODE CONVOLUTIONAL NEURAL NETWORK PADA PLC SIEMENS S7-300 - Angki, Larry; Rusli, Moch.; Siswojo, Bambang (Jurnal Mahasiswa TEUB, 2024)
The development of an automatic goods sorting system utilizing Convolutional Neural Network (CNN) technology on the SiemensS7-300 PLC is an innovative step to enhance efficiency and accuracy in the process of goods distribution. This system utilizes acamera sensor, Arduino Uno microcontroller, ESP32 microcontroller, Siemens S7-300 PLC, and actuators to classify and separategoods automatically based on their characteristics. An experimental research method with a quantitative approach was used toevaluate the effectiveness of this system. The implementation of Convolutional Neural Network (CNN) in the Siemens S7-300 PLCusing Ladder Diagram as the programming language can improve the separation criteria by considering relevant features of thegoods data. System testing shows that the use of CNN on the PLC can achieve a classification accuracy of up to 99.35% in 25epochs. These results indicate that the integration of CNN in the Siemens S7-300 PLC can enhance the performance of theautomatic goods sorting system, reduce the risk of errors, and improve operational efficiency in modern supply chains. Keywords: Industrial Automation, Siemens S7-300 PLC, Microcontroller, Arduino Uno, ESP32, Convolutional Neural Network,Machine Learning