This Study aims to describe role of Information Communication Technology (ICT) as a Library Service Media began to be carried out by the Diniyyah Lampung Institute of Technology and Science (INSTIDLA) as a form of adjustment to the development and needs of students in the current era of Society 5.0. This study aims to determine the influence of information and communication technology on INSTIDLA library services. This research uses a qualitative method with an analytical descriptive approach where the role of ICT is explained comprehensively in the INSTIDLA Library as a Library Service Media. Data collection techniques use observation and interviews. The object of this study is instidla's library and social media services. INSTIDLA Library Services is a combination of manual services and information and communication technology (ICT) based services that support library professionalism in improving INSTIDLA library services. The result of this study is a significant influence between the development of ICT and library services through social media, including helping and facilitating the work of library staff, attracting visitors to the library, and facilitating the communication of librarians with library visitors and finally, the library information base via social media
This research 0aims to design a Real-time ID card detection based on Optical Character Recognition (OCR). OCR detects and records information into CSV files using a camera. Hopefully, it can become one of the administrative solutions in Indonesia by using existing identity cards using OCR in real time. This research method was carried out independently in August 2021 using ID cards as objects. The tool involved was a 320x320 pixel webcam camera on an HP Intel Core i5 7th Gen notebook. The software used by Easy OCR was Pytorch-based. ID cards were detected using an algorithm by TensorFlow object detection with SSD MobileNet V2 FPNLite 320x320 as the pre-trained model of Tensorflow. The researchers collected ID card images using a webcam with various light conditions and orientations and label them using labeling. The researchers trained it with only 20 photos. After 3000 training steps, the researchers obtained about 0.17 loss and 0.95. Thus, the ID card detection tool using OCR runs well.
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