Cancer is the uncontrolled growth of abnormal cell that need a proper treatment. Cancer is second leading cause of death according to the World Health Organization in 2018. There are more than 120 types of cancer, one of them is lung cancer. Cancer classification has been able to maximize diagnosis, treatment, and management of cancer. Many studies have examined the classification of cancer using microarrays data. Microarray data consists of thousands of features (genes) but only has dozens or hundreds of samples. This can reduce the accuracy of classification so that the selection of features is needed before the classification process. In this research, the feature selection methods are Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Kernel Function and the classification method is Support Vector Machine (SVM). The results showed SVM using SVM-RFE as feature selection is better than SVM method without using feature selection and Gaussian Kernel Function.

Mathematical literacy is one of the components that use mathematical concepts and applies them to an everyday situation. Mathematical literacy helps people to identify and understand the role that mathematics plays in the world, and to make the well-founded judgments and decisions required in life by constructive, engaged, and reflective citizens. By contrast, not everybody acquires sufficiently. This study aims to describe the ability of mathematical literacy especially geometry literacy in terms of student metacognition on conic section material. The problems in this study were: 1) How is the ability of mathematical literacy in terms of student metacognition on conic section material 2) What types of mistakes do students make in solving the problem of cone section and 3) What factors cause students to make mistakes in solving conic section question. This research is a qualitative descriptive study. The subjects in this study were 36 mathematics education students who took courses in Analytical Geometry. The data collected using observation, written test, and interview. Before the researcher conducted the analysis, the researcher examined the validity of the data using validity and reliability tests in order to obtain valid data. Furthermore, the valid data were analysed descriptively through identification, grouping, clarification, explanation and conclusion.

A World Health Organization reported that the mortality rate due to brain cancer is the highest in the Asian continent. It is critical importance that brain cancer can be detected earlier so that the treatment process can be carried out more precisely and will be able to extend the life expectancy of brain cancer patients. Taking advantage of microarray data, machine learning methods can be applied to help brain cancer prediction according to its type. This problem can be referred to as a multiclass classification problem. Using the one versus one approach, there will be as many as k ( k − 1 ) 2 two-class problems, where k indicates the number of classes. In this paper, Multiple Multiclass Artificial Bee Colony (MMABC) implemented as a feature selection method and Support Vector Machine (SVM) as a classification method. ABC algorithm proved successful in solving optimisation problems with high dimensionality, and SVM can produce accurate and robust classification results. The data obtained from Broad Institute data. The data consist of 7129 features and 42 samples. From the experiment, the accuracy of Multiple SVM using a feature selection based MMABC method reached 95.24% accuracy in usage 300 best features; this percentage slightly more superior than SVM method without feature selection.

No abstract

Mathematical literacy concentrates on students' ability to analyze, prove, and express ideas completely, solve, and interpret mathematically. The objective of the study was to distinguish between the generative learning model and the conventional learning model on students' mathematical literacy. This study adopted the quasi-experimental research design. The performance continued for four weeks and at the end of the program, post-test compositions were obtained from the nominees. The sample for the study was performed of 72 undergraduate students represented from two classes. Purposive sampling method was applied in selecting the sample. The results of the research presented that there was a significant discrepancy among the t-test score points of the generative learning and conventional group (P<0.05), namely = 4.044 > = 1.667. Therefore, it can be concluded that the average of students' mathematical literacy skill scores with the generative learning group was higher than the conventional learning group.