School dropouts are the problem in education which is the condition of children who do not have the opportunity to complete their education that they couldnt obtain degree certificate due to certain factors. Based on SUSENAS 2013, there is 2.15% of children aged 7-15 years old in West Java who dropped out of school. Three aspects that have great potential on the incidence of school dropouts are characteristic of social, economy, and demography. This study uses logistic regression analysis to determine the effect of school dropouts by the three aspects. The results of logistic regression analysis at 5% significance level indicates that the characteristics of social, economy, and demography that have significant effect on the incidence of school dropouts are the low education of household head, more than four household members, less than the poverty line household expenditure per capita, residence location in urban areas, and boys. The resulting model is sufficientfor estimation with the sensitivity value of 70.20% and the area under the ROC curve of 76.42%.
Keywords: logistic regression, ROC curve, school children, sensitivity.
Abstract. Indonesia is the largest Hollywood movie industry target market in Southeast Asiain 2015. Hollywood movies distributed in Indonesia targeted people in all range of ages including children. Low awareness of guiding children while watching movies make them could watch any rated films even the unsuitable ones for their ages. Even after being translated into Bahasa and passed the censorship phase, words that uncomfortable for children to watch still exist. The purpose of this research is to cluster box office Hollywood movies based on Indonesian subtitle, revenue, IMDb user rating and genres as one of the reference for adults to choose right movies for their children to watch. Text mining is used to extract words from the subtitles and count the frequency for three group of words (bad words, sexual words and terror words), while Partition Around Medoids (PAM) Algorithm with Gower similarity coefficient as proximity matrix is used as clustering method. We clustered 624 movies from 2006 until first half of 2016 from IMDb. Cluster with highest silhouette coefficient value (0.36) is the one with 5 clusters. Animation, Adventure and Comedy movies with high revenue like in cluster 5 is recommended for children to watch, while Comedy movies with high revenue like in cluster 4 should be avoided to watch.
One way to improve the accuracy of predictive modeling is by combining the models. This research tries to study local cascade. It combined one or more base classifier sequentially. In each stage, the probability prediction of the base classifier was inserted to the data. The data then modeled using a decision tree algorithm. This process continued until the data is homogenous. In the original method, the base classifier used was non-ensemble classifier. Our study included bagging, boosting, and random forest as base classifiers. 11 dataset with binary response was used to assess the accuracy of this method. We also compared the accuracy of our method with others that were published between 1996 and 2009. We found that cascading ensemble classifier slightly improve accuracy and performed better for a dataset with numerical predictors.
Southeast Asia is a strategic region in tourism because of its natural and cultural richness. Thus, tourism ministers of ASEAN countries agreed to launch a joint ten-year plan to make Southeast Asia as one destination package for international tourists. The aim of this plan is to increase the share of tourism to the regional economy by 15 percent in 2025. This paper estimates the relationship between tourism and economic development in ten Southeast Asian countries of Brunei Darussalam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam using regression analysis of panel data. It tests the effect of tourism factors such as visitor exports, internal travel and tourism consumption, business tourism spending and capital investment in tourism industry on gross domestic product from 2014 to 2016. The best model obtained is fixed effect model and the tourism aspects that have positive significant impact on gross domestic product improvement in Southeast Asian countries are internal travel and tourism consumption also capital investment in tourism industry. Furthermore, the factors that effect gross domestic product negatively are visitor exports and business tourism spending.
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