Judul: Vektor Kuantisasi Berbasis Blob Citra Untuk Temu Kembali Citra Dengan Hierarchy Clustering
Abstrak: This research proposed vector quantization using hierarchy clustering to overcome semantic problem on image retrieval. This approach considers to find templates or codebooks which arrange image in database. Images were segmented into blobs using grid segmentation. Each blob was represented by color feature using HSV-162 and texture feature using co-occurrence matrix. Output of feature extraction then clustered using hierarchy clustering to generated optimally codebook. Data experiment in this research was taken from http://www.stat.psu.edu/~jiali. The data consist of 1.000 photography image with various of theme, object, and dimension of images were 384×256 pixel. R-precision, recall, and precision were useds to evaluate the result of image retrieval. The experiment showed that segmentation using grid 12×8 have achieved best result than other grid. Based on experience, the result of R-precision showed that grid 12×8 is about 0,32. The advantage of this research that vector quantization with hierarchy clustering can be used to overcome semantic problem on image retrieval based on blob.
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Judul: Algoritme Genetika untuk Optimasi Pembobotan Fitur pada Temu Kembali Citra
Abstrak: Content based image retrieval is developed to retrieve image based on color, shape, and texture feature. One of the problems to be solved in content based image retrieval is how to combine these features to get the optimum retrieval result. Combining multiple features in image retrieval process can be implemented using feature weight assignment. One of feature weight assignment methods is each feature weight is assigned by hand. However this approach is subjective and lack of theory foundation. In this research an automatic feature weight assignment approach based on genetic algorithm is proposed. Genetic algorithm is used for optimizing feature weight in order to get the optimum retrieval result. Image features is used in this research are color, shape, and texture feature. In genetic algorithm, a chromosom represented by features weight. These chromosomes get into genetic operator process such as selection, crossover, and mutation until maximum generation is reached. Result of genetic algorithm process get an optimum feature weight. It used for image retrieval process. The retrieval evaluation used recall precision graphic. The experiment showed feature weight assignment based on genetic algorithm can improve average precision in image retrieval.
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Judul: Analisis pengaruh Foreign Direct Investment (FDI) terhadap industri besi baja di Indonesia
Abstrak: Indonesia sebagai salah satu negara berkembang di dunia membutuhkan dana yang cukup besar dalam melaksanakan pembangunan nasional, ditengah maraknya globalisasi dan liberalisasi pada perekonomian dunia. Ketersediaan dana pembangunan di Indonesia masih sangat minim karena masih terbatasnya tabungan dalam negeri di Indonesia sebagai salah satu sumber pembiayaan investasi. Dana dari dalam negeri (investasi) dirasakan tidak mencukupi untuk melakukan pembangunan sehingga pemerintah berupaya untuk menarik dana yang berasal dari dalam maupun luar negeri. Investasi merupakan syarat mutlak untuk memacu pertumbuhan ekonomi nasional bagi Indonesia. Aliran modal yang masuk dapat berasal dari Penanaman Modal Dalam Negeri (PMDN), atau juga sumber pembiayaan luar negeri/Foreign Direct Investment (FDI).
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