Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine - Erico Jochsen; Dameethia Angeline; Dyah Erny Herwindiati; Janson Hendryli (Journal of Computer System and Informatics (JoSYC), 2023)
One area that has a rich cultural heritage is Bali. Bali is very well known as a very beautiful place and is often visited by tourists in Indonesia and outside Indonesia. Temple buildings in Bali have unique characteristics that reflect the richness of Indonesian culture. So many tourists are interested in vacationing there. However, due to the uniqueness of each temple building there, there is a lack of knowledge about the buildings being seen, so the main aim of this design is to develop a system for recognizing historical temple buildings in Indonesia through building images. More broadly, this design contribution can be applied in the development of similar systems for other historical regions in Indonesia, enriching efforts to preserve and promote cultural heritage nationally. Thus, this design not only paves the way for innovation in the field of image recognition, but also has a positive impact in preserving valuable cultural property. The method used for recognition is Local Binary Pattern as texture feature extraction from temple building images, while Support Vector Machine with a polynomial kernel is used to recognize temple buildings. It is hoped that the combination of these two methods can provide good results in recognizing temple buildings with the correct classification level. The accuracy of this design model using 90 percent training data and 10 percent test data was 45.93 percent, while when using 80 percent training data and 20 percent test data, the accuracy dropped slightly to 43.96 percent. When using 90 percent training data, the recognition of historical buildings produces a precision of 59 percent, a recall value of 71 percent, and an f1-score of 57 percent. On the other hand, with 80 percent training data, the recognition of historical buildings produces 62 percent precision, 72 percent recall value, and 57 percent f1-score.