<![CDATA[Osteoporosis salah satu penyakit degeneratif yang berkaitan dengan proses penuaan yang ditunjukkan perubahan struktur trabekula dan penurunan bone mineral density (BMD). Tujuan penelitian adalah mendapatkan metode kuantifikasi citra panoramik pada region of interest (ROI) di mandibula untuk menentukan BMD. Penelitian ini menggunakan ROI (80x80 pixel) pada kondilus mandibula untuk kuantifikasi citra dilakukan di Bagian Radiologi Fakultas Kedokteran Gigi Universitas Padjadjaran bulan Oktober sampai Desember 2013. Pendekatan analisis tekstur menggunakan prinsip gray level co-occurence matrix (GLCM). Desain dari kuantifikasi citra terdiri atas tahapan pelatihan dan pengujian. Tahapan pelatihan melalui 9 data latih terhadap subjek wanita berusia 52–73 tahun pascamenopause. Data BMD vertebra lumbar dari DEXA digunakan sebagai referensi pada tahap klasifikasi dengan support vector machine (SVM) dengan fungsi kernel multilayer perceptron. Pengujian digunakan 14 data uji dari subjek selain yang digunakan untuk data latih. Pengujian untuk klasifikasi kelas normal dan osteoporosis menggunakan SVM memberikan akurasi 85,71%; sensitivitas (tingkat benar positif) 90,91%; dan spesifisitas (tingkat benar negatif) 66,67%. Pengenalan fitur paling baik didapatkan menggunakan kombinasi fitur contrast, correlation, energy, dan homogeneity sebagai input bagi klasifikasi SVM. Simpulan, analisis tekstur trabekula menggunakan metode gray level co-occurence matrix (GLCM) citra panoramik gigi dapat digunakan untuk deteksi dini osteoporosis. Kata kunci: Grey level co-occorance matrix (GLCM), panoramik, osteoporosis Panoramic Radiograph Image using Cooccurence Gray Level Matrix Method (GLCM) for Early Detection of Osteoporosis in Mandibular Bone  Abstract Osteoporosis is one of the degenerative diseases associated with aging, which is apparent from changes in trabecular structure and decreased bone mineral density (BMD) The aim of this study  was to obtain a panoramic image quantification method on a region of interest (ROI) to determine the BMD. This study used an ROI (80x80 pixels) of the mandibular condyle for image quantification. The study was performed at the Department of Radiology, Faculty of Dentistry, Padjadjaran University during the period of October to December 2013. A texture analysis approach was applied using the principles of gray level co-occurence matrix (GLCM). The design of image quantification consisted of training and testing stages. The training stage was performed through 9 training data on the subjects of post-menopausal women between 52–73 years old . Data from the lumbar vertebrae BMD DEXA was used as a reference in the classification stage using a support vector machine (SVM) with kernel function multilayer perceptron. The testing used 14 test data from subjects which were not used for training data. The results showed that for the normal and osteoporotic class classification using SVM the accuracy was 85.71%, sensitivity (true positive rate) was 90.91%, and specificity (true negative rate) was 66.67%.  The best feature recognition was obtained using a combination of feature contrast, correlation, energy, and homogeneity as inputs for SVM classification. In conclusion, analysis of the trabecular texture using dental panoramic image produced by gray level co-occurance matrix (GLCM) method can be useful for early detection of osteoporosis.Key words: Grey level co-occorance matrix (GLCM), panoramic, osteoporosis DOI: 10.15395/mkb.v46n4.338]]>
