AplikasI sistem deteksi penyakit osteoarthritis pasa ossa manus menggunakan metode algoritma self organizing maps
O steoarthritis merupakan penyakit banyak ditemukan didunia, termasuk di Indonesia. Tujuan dari penelitian ini adalah Mendeteksi penyakit Osteoarthritis dengan menggunakan Self Organizing Map (SOM), dan mengetahui prosedur dari kecerdasan buatan pada metode Self Organizing Mapping ( SOM ).Pada sistem deteksi penyakit ini, ada beberapa tahap yang dilakukan untuk mendeteksi penyakit Osteoarthritis menggunakan metode Self Organizing maps, yaitu hasil gambar foto ronsen Ossa manus normal dan sakit dengan resolusi (150 x 200 pixel) melakukan tahap perbaikan kontras, melakukan proses thresholding, melakukan proses Histogram pada citra yang telah di thresholding, dan melakukan proses terakhir, dimana proses tersebut akan melakukan proses pelatihan (Training) dan proses testing pada citra yang telah disimpan berbentuk data (.text).Kesimpulan yang dapat di ambit adalah Total basil pengujian Self Organizing Maps sebanyak 42 data citra, dimana 12 data citra Normal dan 30 data citra Sakit. Pada hasil proses training menurut prdiksi dokter ada 8 data citra X-ray normal dan 20 data citra X-ray sakit, kemudian pada proses testing diperoleh 3 citra normal benar dinyatakan Normal, 10 data sakit dinyatakan benar sakit dan 1 data citra Normal dinyatakan salah. Maka akurasi yang diperoleh dan hasil pengujian testing adalah 92,85%.
O steoarthritis is a disease that is often found in the world, including in Indonesia. The purpose of this research is to detect Osteoarthritis by using Self Organizing Map (SOM), and to know the procedure of artificial intelligence in the Self Organizing Mapping (SOM) method.In this disease detection system, there are several steps carried out to detect Osteoarthritis using the Self Organizing maps method, namely the results of normal and sick Ossa manus X-rays with a resolution (150 x 200 pixels) performing contrast improvement stages, thresholding processes, processing Histogram on the image that has been thresholded, and performs the last process, where the process will carry out the training process and the testing process on the stored image in the form of data (.text).The conclusion that can be drawn is that the total results of the Self Organizing Maps test are 42 image data, of which 12 are Normal image data and 30 are Sick image data. In the results of the training process, according to the doctor's prediction, there were 8 normal X-ray image data and 20 sick X-ray image data, then in the testing process 3 normal images were found to be true, normal, 10 sick data were declared sick and 1 Normal image data was false. Then the accuracy obtained and the results of testing testing is 92.85%.