Judul: Identifikasi Varietas Durian Berdasarkan Tekstur Daun dengan Metode Ekstraksi GLCM Menggunakan PNN
Abstrak: The diversity and similarity of durian varieties have caused some difficulties to identify durian. In this research, the identification is performed by durian leaf texture extraction. The leaf is selected because it is a characteristic that can be used to differ the type of plant. Identification is performed by texture extraction to anticipate the difficulty of morphology based identification, since durian varieties have similar leaves shape. In this research, identification based on leaf texture is performed using GLCM and PNN. This experiment is based on 0o, 45o, 90o, and 135o GLCM angle orientation. Each angles are tested at 1 pixels, 2 pixels, and 3 pixels distances. They are also tested for PNN smoothing parameter (σ) ranges from 0.1 to 9. Identification is performed using 90 images from 9 varieties of durian and the dimension of image is 281 x 281 pixel. The highest accuracy achieved is 76.67% in 0o angle experiment with 1 pixels distance and 0.1 smoothing parameter (σ).
Keyword: Probabilistic Neural Network (PNN)., leaf texture, Gray Level Co-occurence Matrix (GLCM), Durian identification

Judul: Identifikasi Varietas Durian Berdasarkan Citra Daun Menggunakan K-Nearest Neighbor dengan Ekstraksi Tekstur Co-occurrence Matrix
Abstrak: Durian (Durio zibethinus Murray) is the name of tropical plant from Southeast Asia. The diversity of durian and the physical similarities between them have caused difficulty to identify durian. This research developed a system to identify durian by using co-occurrence matrix as the feature extraction and K-NN as the classifier based on durian leaf texture. This research used 9 variety of durians and each variety has 10 leaf images. The result of this research showed that the cropping of images and selection of texture features can improve the performance identification. The best accuracy in this research was 74.44% that was obtained when K = 5, angle = 450, and distance = 2 pixels. This accuracy was achieved by using five leaf texture features, namely contrast, entropy, information measures of correlation 1, sum average, and sum entropy.
Keyword: texture, K–Nearest Neighbor (K–NN), durian, co-occurrence matrix

Judul: Impact Of water policy on poverty reduction and women roles in water management In Zimbabwe
Abstrak: Water management is a key factor in the battle to remove the scourge of extreme poverty and to build secure and prosperous lives for people in the developing world. The water sector in Zimbabwe is complicated by the large number of actors; it is interwoven within various ministries. The research used secondary data and review of literature to evaluate how the current water policy has contributed towards marginalising women and the poor. It looked at how actors can also marginalise or put other actors at risk thereby undermining their role through prioritisation of certain projects, stakeholder participation and usage policy. From a descriptive analysis it is found that women are further relegated and marginalised. The poor are further impoverished through marginalisation and vulnerability. The policy environment favours business and state interests against those of grassroots actors and women. It recommends the establishment of home grown pro- poor water management strategies.
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