In the current era of technological development, e-commerce is required to be more developed, effective, efficient and become the main choice for buyers and sellers. The problem formulation of this journal is the most trending store and the city of origin of the store. This is evidenced by the best-selling products sold based on categories and types of men's fashion products. The purpose of this research is to find out the type of fashion that is loved by male buyers in the Shopee marketplace and get discounts/free shipping and can be a recommendation for what fashion products are suitable for newcomers to Shopee Indonesia. This research was conducted on April 4, 2021, which coincided with the Covid-19 pandemic. This research was carried out in 4 steps in applying Web Scrapping and Exploratory Data Analysis techniques: Observation, Data Collection, Information Extraction and Analysis. this research: (1) from the buyer's point of view it can be a reference for the goods to be purchased and get a discount/free shipping because the city of origin of the shop is the same or closest; (2) from the side of new sellers (new traders) in the Indonesian shopee marketplace, it can be a reference for any men's fashion that will sell well in the future based on the store that sells the most products.

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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.