Concept drift refers to an alteration in the relations between input and output data in the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual change in these relations. It generates a model obsolescence and quality decrease in predictions. Besides, there is a challenging task: the extreme verification latency to certify the labels. For batch scenarios, state-of-the-art methods do not properly tackle the problems aforementioned due to their high computational time, lack of representing samples of the drift or even for having several hyperparameters for tuning. Therefore, we propose AMANDA, a semi-supervised density-based adaptive model for non-stationary data. It has two variations: AMANDA-FCP, which selects a fixed number of samples; and AMANDA-DCP, which, in turn, dynamically selects samples from data. Our results indicate that these two variations outperform the state-of-the-art methods for almost all synthetic and real datasets, with an improvement up to 27.98% regarding the average error. AMANDA-FCP improved the results for a gradual concept drift, even with a small size of initial labeled data. Moreover, our results indicate that semi-supervised classifiers are improved when they work along with our density-based methods. Therefore, we emphasize the importance of research directions based on this approach.