Judul: Penggabungan Ciri Citra dan Teks untuk Sistem Pencarian Tumbuhan Obat Indonesia Berbasis Web
Abstrak: This research investigate the effectiveness of image and text fusion for Indonesian medicinal plant search engine based on web application. This research used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2448 images. WeightedSUM is general linear combination formula to combine image and text features. Further research was conducted on the combination of features to get a better result in medicinal plants search engine. Fuzzy Local Binary Pattern (FLBP) is used to extract texture in image processing. This research uses the Probabilistic Neural Network (PNN) to improve image similarity that used in the fusion process. BM25 weighting in document search engine is used to get text similarity. Image and text similarity are combined using WeightedSUM to get the retrieval results. The experimental results show that the fusion of image and text features can improve the performance of retrieval results. In particular, the Average Precision (AVP) has increased from 0.31 to 0.71
Keyword: 

Judul: Fusion of Local Binary Patterns Features for Medicinal Plants Identification.
Abstrak: Identification plants automatically still be problem in obtaining a robust features. Local Binary Patterns (LBP) is an excellent descriptor for texture feature due to efficiency and robustness, but limited in the size of sampling point. In this research we propose fusion of LBP features, which incorporates additional information without sacrificing the robustnes of LBP features. Fusion of LBP features was done by two ways. In the first way, we perform a straightforward fusion by calculating histogram of multiple LBP features separately, then concatenating the multiple histograms together. In the first way we combine information provided by multiple operators by varying the size of sampling points and radius. In the second way, each histogram of LBP features are classified, and the feature fusion can be accomplished by classifier combination. Both ways, we employ probabilistic neural network (PNN) to classify LBP features. The experiment performed on medicinal plants and house plants. The fusion of LBP features are shown to be very powerful tools for improving accuracy.
Keyword: 

Judul: Analysis of Unequal Income Distribution with Cobb-Douglas Production Function
Abstrak: This paper studies the economic growth model by Moav (2002). Moav model shows the existence of unequal income distribution between low-income with high-income community. That kind of unequal income distribution is caused by uneven distribution of income. On this Moav model, there is one area called the threshold. Threshold becomes a benchmark to determining whether a society is categorized into low- or high- income community. The production function used in this paper is the Cobb-Douglas function. The dynamics of this model is described as difference equation of low-income, threshold, and high-income community. Simulation study on the model is carried out using Maple 13 and Mathematica 7.0 software. Overall, from the observed case, high-income community will reach a stable condition earlier, followed by the threshold, and then the low-income community.
Keyword: