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Background Deep vein thrombosis (DVT) is a frequent complication in cancer patients and is the second leading cause of death. The high level of C-reactive protein (CRP) is an acute phase reactant that induces tissue factor (TF) expression in monocytes, smooth muscle cells, and endothelial cells. The CRP level positively correlates with the incidence, extension, and volume of thrombus. TF expression triggers the coagulation system including the formation of thrombin and circulating fibrin such as prothrombin fragment 1+2 (F 1 + 2 ) and D-dimer. Objective To determine the diagnostic value of high-sensitivity (hs)-CRP, D-dimer, and Wells score combination to predict the incidence of DVT on clinically suspected DVT (Wells score ≥2) cancer patients. Subjects and Methods This study was a cross-sectional study on a diagnostic test to determine the diagnostic value of hs-CRP and D-dimer for early detection of DVT on clinically suspected DVT (Wells score ≥2) cancer patients. It was conducted in Dr. Kariadi Hospital, Semarang Indonesia on 35 subjects. The diagnosis of DVT was confirmed by color duplex sonography. The diagnostic accuracy of combination of hs-CRP, D-dimer, and Wells score was analyzed by logistic regression. Results DVT was confirmed in 10 subjects (28,6%). The cut-off point of hs-CRP levels for probable DVT was ≥51.05 mg/L and for D-dimer was ≥5030 µg/L. The median levels of both variables were higher in the subjects with DVT compared with the subjects without DVT, but it was not statistically significant. The combination of hs-CRP (≥51.05 mg/L), D-dimer (≥5030 µg/L), and Wells score ≥3 had the high accuracy (94.1%) to predict the incidence of DVT compared with hs-CRP (65.0%), D-dimer (54.7%), and combination of hs-CRP and D-dimer (71.0%). Conclusion The combination of hs-CRP (≥51.05 mg/L), D-dimer (≥5030 µg/L), and Wells score ≥3 can predict the incidence of DVT in cancer.

This paper implements static hand gesture recognition in recognizing the alphabetical sign from “A” to “Z”, number from “0” to “9”, and additional punctuation mark such as “Period”, “Question Mark”, and “Space” in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contourrepresentation from image segmentation of the glove wore by user. Then, it is classified using Artificial Neural Network (ANN) based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Moreover, speech translation recognizes NATO phonetic letter as the speech input for translation.

Saat ini banyak orang tua mulai mengajarkan anaknya membaca sejak dini di rumah, ataupun menyekolahkannya di Taman Kanak – Kanak (TK) yang mengajarkan membaca.  Metode yang sesuai untuk Pendidikan Anak Usia Dini (PAUD) adalah belajar sambil bermain. Metode ini tidak membebani dan tidak ada unsur paksaan. Pemanfaatan Augmented Reality untuk media pengenalan membantu anak belajar membaca dengan menggunakan cara yang menyenangkan, yaitu dengan memindai marker berisi huruf atau suku kata menggunakan kamera perangkat Android. Nantinya akan muncul video animasi, audio pelafalan, dan hewan 3D sesuai huruf atau suku kata dan gambar dalam marker yang dipindai. Desain ini kemudian diimplementasikan dalam sebuah aplikasi untuk sistem operasi Android. Uji coba aplikasi ini dilakukan dengan kedua mitra dari Lab School Permata UHAMKA dan Baitul Ulum Al Isfahani Manfaluthi yang dihadiri oleh para Guru dan Orang Tua Wali murid. Peserta mencoba langsung aplikasi dan memberikan feedback untuk mengetahui sejauh mana aplikasi dapat membantu proses Pembelajaran. Berdasarkan hasil evalusi aplikasi ini terbukti dapat membantu Guru dan orang tua wali murid untuk mempraktekan model belajar Alfabet yang asik dan menyenangkan untuk anak dan muridnya.