Smart lighting systems utilize advanced data, control, and communication technologies and allow users to control lights in new ways. However, achieving user comfort, which should be the focus of smart lighting research, is challenging. One cause is the passive infrared sensor that inaccurately detects human presence to control artificial lighting. We propose a novel classification-integrated moving average model method to solve the problem. The moving average increases the Pearson correlation coefficient of motion sensor features to human presence. The classification model (...) is for a smart lighting intelligent control based on these features. Several classification models are proposed and compared, namely, k-nearest neighbor, support vector machine, decision tree, näive Bayes, and ensemble voting. We build an Internet of things system to collect movement data. It consists of a PIR sensor, a NodeMCU microcontroller, a Raspberry Pi-based platform, a relay, and LED lighting. With a sampling rate of 10 seconds and a collection period of 7 days, the system achieved 56852 data records. In the PC test, movement data from the PIR sensor has a correlation coefficient of 0.36 to attendance, while the MA correlation to attendance can reach 0.56. In an exhaustive search of an optimum classification model, KNN has the best and the most robust performance, with an accuracy of 99.8%. It is more accurate than direct light control decisions based on motion sensors, which are 67.6%. Our proposed method can increase the correlation value of movement features on attendance. At the same time, an accurate and robust KNN classification model is applicable for human presence-based smart lighting control. (shrink)