Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

The Covid-19 pandemic has had a significant impact on various lines of life, including the education sector. The government's policy to minimize the transmission of the virus is carried out by implementing physical distancing rules. Therefore, the government in this case the Ministry of Education and Culture (Kemendikbud) provides rules for conducting distance learning. One of the schools affected by this policy is SDI (Islamic elementary school) Losari Lor. Teaching and Learning Activities (KBM) at SDI Losari Lor, before the pandemic were completely carried out offline with a lecture model. However, during the pandemic, activities were carried out with limited face-to-face and online in accordance with the instructions of the Brebes Education Office. The weakness of KBM is that learning is less than optimal, the teacher explains the material in class in a limited way, students are required to study independently. Likewise with the subject of Islamic Religious Education (PAI). Salat material is the material taught in class II semester 2. This study aims to create an android-based learning application, in order to increase students' understanding and understanding of the material to be discussed, namely the salat material. In addition, this research is also to overcome learning that is constrained by the internet network. Data collection methods used are interview methods, direct observation methods and literature methods. While the method used in building the application is a 4D model. The steps taken are Define, Design, Develop, and Dessiminate. The software used is Microsoft Powerpoint, iSpring Suite, and Web2Apk Builder. The result of the research is an Android-based Salat application. The result of this research is a mobile learning of salat application that can be used to make teaching and learning activities more interesting and interactive. Besides the fact that the material presented to the student will be easier to understand, the application can also be accessed even without an internet network.

New student registration system manually is still used by some schools in Indonesia. But the problem is that prospective students are still many who feel difficulty with the system of the manual registration and of course they need registration system which more quick and efficient. Moreover, the conditions of the pandemic at this time, where there are rules to keep the protocol of the Health. Such as keeping physical distancing and limiting public service. The research method used in this research is the design and manufacture of the system, namely to design web-based system of a new student registration  that can be accessed through a web browser. The online registration system of new students is built with PHP programming language  and utilizing a MySQL database as a database server, and using the Codeigniter Framework. The result of this research is the  web-based  online registration system of new students  already have the ability to provide convenience for prospective students to obtain all the information about the acceptance of new students and do the online registration process.

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