Mammography is the best approach in early detection of breast cancer. In mammography classification, accuracy is determined by feature extraction methods and classifier. In this study, we propose a mammogram classification using Law's Texture Energy Measure (LAWS) as texture feature extraction method. Artificial Neural Network (ANN) is used as classifier for normalabnormal and benign-malignant images. Training data for the mammogram classification model is retrieved from MIAS database. Result shows that LAWS provides better accuracy than other similar method such as GLCM. LAWS provide93.90% accuracy for normal-abnormal and 83.30% for benign-malignant classification, while GLCM only provides 72.20% accuracy for normal-abnormal and 53.06% for benign-malignant classification.

3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

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This research aims to create a web-based application for sharing questionnaires. The developed features are creating questionnaires, sharing questionnaire in the dashboard, filter the questionnaire as the requested criterias, exchange the rewarded coins for gifts, export questionnaire data to a document, set a limit for the questionnaire for each device. The development will be using data collecting using questionnaire and literature study. Then, software development life cycle (SDLC) waterfall research methodology will be used for the website system development. Result of this research will be a website application that will be used for questionnaire maker so that they can reach the respondent count target, have a suitable respondent (minimize respondent who does not meet the criteria), and also can collect more validated data.