Model human resources scorecard untuk pengukuran kinerja pada departemen human resources di PT. Xyz Cikarang
P T. XYZ Cikarang is a company that produces cooking spices such as flour,seasonings, seasoning flour, and sauces. PT. XYZ Cikarang has not yetimplemented the performance measurement of the Human Resources Department.By using the Human Resources Scorecard method at the Human ResourcesDepartment, the results obtained from the implementation of the Human ResourcesDepartment strategy at the company PT. XYZ Cikarang which can be measuredthrough HR performance indicators is equipped with weights from strategicobjectives in each perspective. The design of the Balanced Scorecard starts from aSWOT analysis to obtain 8 strategic objectives from 4 perspectives, namely:finance, customers, internal business processes, learning and growth. Then it wasrevealed to the Human Resource Scorecard with 4 perspectives and 7 StrategicGoals complemented by KPIs, targets and strategic initiatives. The results of themeasurement of the Human Resources Scorecard in the financial perspective get ascore of 2, the customer perspective gets a score of 3.77, the internal businessperspective gets a score of 2, and the learning and growth perspective gets a scoreof 1.27. The values from the four perspectives are accumulated and compared usinga Likert scale, resulting in a weighted value of 2.26 or it can be concluded that theperformance of the Human Resources Department is still "poor".
T echnological developments are increasing very rapidly nowadays, the latest technologies such as Face Detection and Face Recognition in public facilities have been widely applied, such as cameras for absences, face detection devices as cell phone security and various other new innovations in technology. With the outbreak of COVID-19 it is felt necessary to create a system that is more than just single recognition, but it is better if you can use CCTV with multi recognition at once. Therefore, in this research, we will create a system that allows for multi recognition using several algorithm methods. Detect objects and detect distances to be distinGUIshed by using the Tiny-YOLOV4 algorithm combined with the Viola Jones algorithm for detecting faces and the CNN algorithm for recognizing faces according to the dataset. The purpose of making this model is to find out the distance, and the use of masks in public places and to find out the performance when the two algorithms between Tiny-YOLOV4 and Viola Jones are combined. The test was used using a CPU clocked at 2.9 GHz and a GPU clocked at 1590 MHz with an accuracy rate of 92.6% for mask object detection and 90.67% for facial recognition with a maximum distance from the front of the camera for human detection of 830 centimeters, mask detection of 730 centimeters, and 530 centimeters for face recognition.