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Abstrak :
Penelitian skripsi ini meneliti dan menguji penggunaan web-application sebagai sarana antar-muka pengguna untuk menggunakan model segmentasi citra deteksi mikrokalsifikasi Tensorflow dari Hakim, et al. Penelitian ini dilakukan dengan memanfaatkan Flask sebagai backend dari web-application dan Bootstrap untuk mengatur tampilan website yang ditunjukkan ke pengguna. Selain itu, dimanfaatkan beberapa library Python seperti CV2 dan PIL dalam pemrosesan citra di web-application. Digunakan juga layanan komputasi awan Amazon Web Services (AWS) untuk mendeploy web-application agar dapat digunakan secara masif. Hasil penelitian adalah sebuah web-application untuk memprediksi letak, jumlah, dan kecenderungan persebaran mikrokalsifikasi. Dari penggunaan dan evaluasi web-applicationdiperoleh hasil evaluasi model segmentasi citra Hakim, et al. memiliki sensitivitas (86,27%), spesifitas (14%), dan F1-Score (64,23%) untuk keseluruhan citra format TIFF. Hal ini menunjukkan model memiliki kemungkinan besar memberi hasil false positive; waktu rata-rata yang dibutuhkan web-application untuk memprediksi citra berformat TIFF adalah 3 menit 20 detik, citra format DICOM berkisar 55 detik, dan citra format JPG berkisar 16 detik. ......This study is discussed about web-application utilization as user interface to make use Tensorflow image segmentation model for microcalcification detection from Hakim, et al. Study is done by using Flask as website backend also Bootstrap to manage website interface that will be shown. Besides that, some Python libraries such as CV2 and PIL are also applied for image processing in web-application. This study also uses cloud computing platform, which is  Amazon Web Services (AWS) for web-application deployment in order to massive usage. Results of this study are a web application to predict location, sum, and spreads tendency for microcalcification. From web-application utilization and evaluation, obtained Hakim et,al, image segmentation model has sensitivity (86,27%), specificity (14%), and F1-Score (64,23%) for all image in TIFF format. This result shows the model has high probability to return false positive; the average time that web application needs to predict TIFF image is 3 minutes 20 seconds, DICOM image is around 55 seconds, and JPG images is around 16 seconds.

Abstrak :
ABSTRACT
A cross sectional study on cardiovascular disease risk profile was carried out in North Jakarta, Indonesia. One hundred six Chinese individuals (47 men and 59 women) aged 25 years and over were recruited to collect baseline information related to dietary determinants of cardiovascular risk profile.

Result showed high prevalence of overweight /obesity and hypertension, especially in men (43% and 48.8%, respectively). The prevalence of current smoker was 12.2% of men and 3.9% of women. The prevalence of hyperlipidemia was 14.6% of men and 9.6% of women. Mean values of BMI, WHR, and blood pressure were significantly higher in men than in women. Body fatness and blood pressure in women were significantly increased with age. In women, total cholesterol and LDL cholesterol were associated with BMI, while triglyceride was associated with WHR.

Intake of fresh fish, crabs/lobster/shrimp, sea weed, tomato and spinach decreased with advancing age of men. No food components that significantly correlated with age of women. There were no significant relationship between food intake and other socio-demographic characteristics. In men, lamb and chocolate intake were significantly associated with BMI while rice vermicelli and sweet bread were significantly correlated with blood pressure. Total cholesterol had significant correlation with pork sausage intake of men. In women, HDL cholesterol were significantly associated with ham and beef sausage, while LDL cholesterol were associated with duck intake.

These findings suggested that women had more favorable cardiovascular risk profile than men and age was likely to be an important determinant factor for women. Men were more prevalent smoking habit and poorer attitude to health status than women.