Warehouse is a place used to store and put items owned by company. There are many activities carried out in the warehouse, starting from data collection process to management of the items. However, there are still companies that have difficulty in collecting data and management of available items. As the process of collecting goods is still done manually, it takes a long time to find information from the item. Therefore, a Warehouse Management System (WMS) was built by implementing the ABC classification method to help the company in managing items at the warehouse. ABC classification method is a method used to group items into certain classes based on the annual demand of the item. This method is used to regulate the placement of items in the warehouse. After the ABC method is implemented on the system, the company is able to to arrange the items and to obtain the information of the most needed items by consumers easier. The result of User Acceptance Test (UAT), which was conducted 3 times, states that all of user needs have been fulfilled and accepted entirely. Then, based on the results of the questionnaire recapitulation distributed to employees as many as 30 respondents, it is obtained that as many as 91.33% of respondents state that the system is easy to use and provides complete and accurate information (strongly agree).
Theft and intrusion are crimes that often occur in neighborhoods when there is opportunity or negligence by owners and security personnel. Many studies have been carried out to improve environmental security by applying cameras as a surveillance medium. However, the camera is not optimal in detecting objects when the lighting conditions are lacking. Therefore, in this study, a monitoring and object detection system was built by applying the Illumination Invariant model. This model is used to improve the appearance of the image from light and shadow reflections. The process of detecting and identifying objects is done by using human facial features (face detection) captured by the camera. The camera used is a Logitec C270 Webcam 720p which is connected via a USB port on the Raspberry Pi 4. The Raspberry Pi 4 processes human face image data and sends the processing results to a MySQL database using the HTTP protocol. Data transmission is done using the Python Flask web framework. The system was successfully run 100% by using black box testing of all functional requirements. Tests on the object detection feature were carried out based on different lighting conditions 15 times by comparing the original image and the results of the Illumination Invariant implementation. Based on the test results obtained object detection accuracy of 86.7%.
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