There are various ways to detect osteoporosis disease (bone loss). One of them is by observing the osteoporosisimage through rontgen picture or X-ray. Then, it is analyzed manually by Rheumatology experts. Article present the creationof a system which could detect osteoporosis disease on human, by implementing the Rheumatology principles. The main areasidentified were between wrist and hand fingers. The working system in this software included 3 important processing, whichwere process of basic image processing, pixel reduction process, pixel reduction, and artificial neural networks. Initially, thecolor of digital X-ray image (30 x 30 pixels) was converted from RGB to grayscale. Then, it was threshold and its gray levelvalue was taken. These values then were normalized to an interval [0.1, 0.9], then reduced using a PCA (Principal ComponentAnalysis) method. The results were used as input on the process of Backpropagation artificial neural networks to detect thedisease analysis of X-ray being inputted. It can be concluded that from the testing result, with a learning rate of 0.7 andmomentum of 0.4, this system had a success rate of 73 to 100 percent for the non-learning data testing, and 100 percent forlearning data
