PENGARUH PERTUMBUHAN EKONOMI, JUMLAH PENDUDUK, DAN SHARE SEKTOR INDUSTRI MANUFAKTUR TERHADAP KETIMPANGAN PENDAPATAN DI PROVINSI JAWA TENGAH TAHUN 2004-2014

The issue of income inequality has become a global phenomenon. Inequality of income has become a common problem in a country, both in developing and developed countries. A common problem often faced by developing countries including Indonesia is the inequality of income distribution or inequality in income among high-income groups and low-income groups of society. Gini Ratio (Gini Ratio) is the most commonly used measure to measure overall income inequality. In Central Java, there is still income inequality. This is due to the differences in economic growth, population, and share of the manufacturing sector in the Province of Central Java is relatively different. This study aimed to analyze the effect of economic growth, population, and the share of the manufacturing industry in the province of Central Java in 2004-2014 against inequality in the province of Central Java in 2004-2014. The data used is secondary data gini ratio, the rate of economic growth, population, and the share of the manufacturing sector to the total GDP (Gross Domestic Product) in Central Java province. Analyzer used in this research is regression analysis with fixed effect method. The result shows that the variable of economic growth, population, and share of manufacturing industry sector have a positive and significant effect to income inequality in Central Java province 2004-2014

IMPLEMENTATION OF FACE RECOGNITION USING GEOMETRIC FEATURES EXTRACTION

The face is among the biometric objects used to recognize one’s identity. There are various face recognition system methods that can be applied, one of which is geometric features-based face recognition. Geometric features are unique features extraction of one’s facial components. These features are obtained by calculating the comparison values of the distance measurement between facial components served as a reference like eyes, nose, and mouth. This research implemented a face recognition system using the geometric features method on a significantly low-spec computer system. This implementation was carried out by building a system, installing it on a computer system, and then testing it using laptops or computer devices and the camera web. The face recognition system would process the facial input images, extract their geometric features, and match the results with the data stored in the database. The research results were a low-spec computer system that could recognize its users by providing real-time feedback in the form of users’ names with an accuracy of 98%