Determining possible technology used in calculating customer buying interest is important for the company as it relates to marketing strategies and policies. The Naïve Bayes Classifier is a possible method to measure customer buying interest. This study aims to analyze the accuracy of this method in calculating customer buying interest of internet token. Data were obtained from past sales and then analyzed using the Naïve Bayes Classifier to predict future opportunities by defining the class of attributes. The classifications used were operator, internet quota, token active period, and product price. This study found that the Naïve Bayes Classifier was provably accurate in calculating customer buying interest as well as comparing the prediction with actual results aside the data training. Results showed that this method successfully classifies 10 products of 858 transactions as data training and 10 products of 115 transactions as data test. Here indicates that all data test and data training analysis showed eight of the tenth meaning that eight of ten predictions were correct or it had 80% accuracy. This study is expected becoming a reference in analyzing data sales and predicting future sales conditions, so it will help to determine appropriate strategy of product development.
- Over time, technology for disability aids is also growing rapidly around the world. One of the technologies that has a function like the eye is the camera. While in other technologies, now has been created a small PC / computer that can be used as a microcontroller, called a Mini PC modeled Raspberry Pi, but if only using these two tools simple object detection system becomes less complete because the system can only detect objects without knowing the distance, therefore, in order for the distance from an object to be read by the system, the HC SR04 sensor is used which is compatible with raspberry pi, to make this system also needed a data image processing system so that the system can detect objects, in this final task using a pre-trained model of mobilenet, mobilnet is one of the convolutional neural network (CNN) architectures that can be used to overcome the need for excessive computing resources. In this final task the system can detect as many as 80 objects, but in the system test used 5 objects with 50 types of variants, namely bottle objects, glass objects, book objects, people objects, and mobile phone objects, testing was conducted as much as 3 times from each variant of the object with 3 different distances as a determining factor of accuracy, namely distance 100cm, distance 150cm, and distance 200cm, From the results of object detection tests that have been done obtained the average percentage of object detection by 67%, the most accurate objects that can be read by the system at all distances are people's objects, while for other objects only read accurately at a distance of 100cm only.
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