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
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Judul: Penggabungan Fitur Fuzzy Local Binary Pattern dan Fuzzy Color Histogram untuk Aplikasi Mobile Identifikasi Citra Tumbuhan Obat.
Abstrak: This research develops a mobile application based on Android operating system for identifying medicinal plant images. The identification is conducted based on texture and color feature. This research uses medicinal plants leaf images. The total medicinal plants used in this research is 51 species taken from Biofarmaka IPB, Cikabayan Farm, Green house Center Ex-Situ Conservation of Medicinal Plant Indonesia Tropical Forest and Kebun Raya Bogor. Each species consist of 48 images, thus the total image used in this research is 2448. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting medicinal plants texture. This method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic for representing the local patterns of texture images. Fuzzification allows the distribution of the LBP values is used as a feature vector. Moreover, the FCH method is used for extracting medicinal plants color. This method considers the color similarity of each color pixel associated with all the histogram bins through fuzzy-set membership function. Next, Fuzzy C-Means (FCM) algorithm is used to compute the membership values. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research uses Probabilistic Neural Network (PNN) method to classify the FLBP and FCH feature vector. The experimental results show that the fusion between FLBP and FCH can improve the accuracy of medicinal plants identification (FLBP: 59.61%, FCH: 50.78%, fusion of FLBP and FCH: 74.51%).
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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.
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