Breast cancer is the most important cause of death among women. A prediction of breast cancer in early stage provides a greater possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumor whether it was a harmful malignant tumor or un-harmful benign tumor. In this paper, two algorithms of machine learning, namely Support Vector Machine and Extreme Gradient Boosting technique will be compared for classification purpose. Prior to the classification, the number of data attribute will be reduced from the raw data by extracting features using Principal Component Analysis. A clustering method, namely K-Means is also used for dimensionality reduction besides the Principal Component Analysis. This paper will present a comparison among four models based on two dimensionality reduction methods combined with two classifiers which applied on Wisconsin Breast Cancer Dataset. The comparison will be measured by using accuracy, sensitivity and specificity metrics evaluated from the confusion matrices. The experimental results have indicated that the K-Means method, which is not usually used for dimensionality reduction can perform well compared to the popular Principal Component Analysis.
The market of Fast-Moving Consumer Goods (FMCG) companies in Indonesia is enormous. Unilever has 400 brands in more than 190 countries, making it a global business that is as influential in the consumer product market as it is in Indonesia. Sales forecasting at this company is very useful for planning expenses and the company's total costs on the business strategy. This study uses trend moment method to forecast the sales and earnings of Unilever Indonesia companies at the end of the year. This article aims to test the performance of the trend moment method calculation on the prediction of net sales and profits in FMCG companies. At the end of the analysis process, it can be concluded that forecasting using trend moment method is going very well. This indicator of success is shown by the error level of MAPE, which is below 10%.
Sudarjat dkk. : Keragaman dan kelimpahan arthropoda pada tajuk tanaman cabai merah keriting (Capsicum annuum L.) varietas TM 999 yang diberi aplikasi insektisida klorantraniliprol 35% Sudarjat • A. Handayani • S. Rasiska • W. Kurniawan Keragaman dan kelimpahan arthropoda pada tajuk tanaman cabai merah keriting (Capsicum annuum L.) varietas TM 999 yang diberi aplikasi insektisida klorantraniliprol 35% The diversity and abundance of arthropods in plant canopy of TM 999 variety of curly red chili (Capsicum annuum L.) that was treated by klorantraniliprol 35% insecticide
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.