  Smart systems have been massively developed to help humans in various tasks.
Deep Learning technologies push even further in creating accurate assistant
systems due to the explosion of data lakes. One of the smart system tasks is to
disseminate users needed information. This is crucial in the tourism sector to
promote local tourism destinations. In this research, we design a model of
local tourism specific image captioning, which later will support the
development of AI-powered systems that assist various users. The model is
developed using a visual Attention mechanism and uses the state-of-the-art
feature extractor architecture EfficientNet. A local tourism dataset is
collected and is used in the research, along with two different kinds of
captions. Captions that describe the image literally and captions that
represent human logical responses when seeing the image. This is done to make
the captioning model more humane when implemented in the assistance system. We
compared the performance of two different models using EfficientNet
architectures (B0 and B4) with other well known VGG16 and InceptionV3. The best
BLEU scores we get are 73.39 and 24.51 for the training set and the validation
set respectively, using EfficientNetB0. The captioning result using the
developed model shows that the model can produce logical caption for local
tourism-related images
