Judul: Identifikasi Varietas Durian berdasarkan Tekstur Daun menggunakan K-Nearest Neighbor dengan ciri Statistical Textures
Abstrak: Durian (Durio zibethinus Murray) adalah salah satu komoditas yang memiliki nilai ekonomi yang tinggi di Indonesia. Nilai ekonomi durian dipengaruhi oleh keunggulan yang dimiliki setiap varietasnya. Oleh sebab itu identifikasi tanaman durian merupakan hal yang sangat penting. Pada penelitian ini, identifikasi dilakukan berdasarkan citra daun dari empat varietas durian. Metode klasifikasi yang digunakan adalah K-Nearest Neighbor dengan ekstraksi ciri statistical textures. Penelitian ini menghasilkan akurasi tertinggi sebesar 67,5% menggunakan 6 ciri statistical textures. Pada penelitian ini paling sulit teridentifikasi varietas Sukun. Varietas Sukun mempunyai kemiripan ke varietas Bakul dan Matahari.
Keyword: daun, Statistical textures, durian, identifikasi, K-Nearest Neighbor

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: Mathematical model of Hematopoietic Stem Cell in Chronic Myelogenous Leukemia with and without Therapy
Abstrak: Leukemia or blood cancer is a diverse group of neoplastic disease, characterized by the multiplication of abnormal leukocytes or malignant transformation of blood-forming cells in bone marrow and lymphoid tissues. One of the most common types of leukemia is Chronic Myelogenous Leukemia (CML), which is usually diagnosed by the presence of certain chromosomal abnormalities, the so-called Philadelphia chromosome (Ph). One of the treatments of CML is Granulocyte Colony-Stimulating Factor (G-CSF). In this paper, we discuss two Hematopoietic Stem Cell (HSC) models, i.e. HSC without treatment and HSC with G-CSF treatment. The model of HSC without treatment included three equations representing the dynamics of HSC, leukocyte and platelet. Whereas, the model of HSC with G-CSF treatment includes the previous three equations and adds two more equations representing G-CSF injection and G-CSF circulation. The stability behavior of the system approaching the equilibrium points in the simulation study is carried out using Mathematica 7.0. The simulation results show that the G-CSF treatment can successfully decrease the number of HSC, which means reducing the CML.
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