Esti Suryani

Two-stage feature extraction to identify Plasmodium ovale from thin blood smear microphotograph

Malaria is one of the tropical diseases, which is found with high prevalence in East side of Indonesia. To assist the diagnosis of this disease, we have developed a Computer Aided Diagnosis system that analyze the information obtained from thin blood smears microphotograph. Five species of the parasites are found in Indonesia, and each of them has different morphology. Therefore, the morphological characteristics of the parasites should be considered in the feature extraction development to obtain an accurate classifications system. The focus of this study is on the feature extraction development for Plasmodium ovale detection. A novel strategy is proposed by divide the feature extraction into two stages. The first stage focused on the most distinctive features, while the second focused on detail characteristics of the parasite. The proposed algorithm was evaluated using 177 microphotographs, obtained from Malaria observations carried out in various places of Indonesia by Eijkman Institute for Molecular Biology Indonesia, and showed a sensitivity of 0.75 and specificity 1.0.

Classification of Acute Myelogenous Leukemia (AML M2 and AML M3) using Momentum Back Propagation from Watershed Distance Transform Segmented Images

This study uses image processing to analyze white blood cell with leukemia indicated that includes the identification, analysis of shapes and sizes, as well as white blood cell count indicated the symptoms of leukemia. A case study in this research was blood cells, from the type of leukemia Acute Myelogenous Leukemia (AML), M2 and M3 in particular. Image processing operations used for segmentation by utilizing the color conversion from RGB (Red, Green dab Blue) to obtain white blood cell candidates. Furthermore, the white blood cells candidates are separated by other cells with active contour without edge. WBC (White Blood Cell) results still have intersected or overlap condition. Watershed distance transform method can separate overlap of WBC. Furthermore, the separation of the nucleus from the cytoplasm using the HSI (Hue Saturation Intensity). The further characteristic extraction process is done by calculating the area WBC, WBC edge, roundness, the ratio of the nucleus, the mean and standard deviation of pixel intensities. The feature extraction results are used for training and testing in determining the classification of AML: M2 and M3 by using the momentum backpropagation algorithm. The classification process is done by testing the numeric data input from the feature extraction results that have been entered in the database. K-Fold validation is used to divide the amount of training data and to test the classification of AML M2 and M3. The experiment results of eight images trials, the result, was 94.285% per cell accuracy and 75% per image accuracy

Analisis Perbandingan Kinerja Kombinasi Algoritma BWT-RLE-MTF-Huffman Dan BWT-MTF-RLE-Huffman Pada Kompresi File

Kompresi data seringkali digunakan untuk proses transmisi data (data transmission) dan penyimpanan data (storage). Keuntungan data yang terkompresi antara lain dapat mengurangi bottleneck pada transmisi data penyimpanan data lebih hemat ruang, mempersulit pembacaan data oleh pihak yang tidak berkepentingan, dan memudahkan distribusi data. Berdasarkan hal tersebut, paper ini menerapkan algoritma kompresi data dengan pengujian pada beberapa file (.txt, .rtf, .doc, .exe, .dll, .tif, dan .bmp). Algoritma yang digunakan merupakan kombinasi beberapa algori tma lossless compression, yaitu Burrows-Wheeler Transform (BWT), RunLength Encoding (RLE), Move-To-Front (MTF) serta Huffman Coding. Pengujian dilakukan untuk mengetahui besar rasio kompresi, waktu kompresi dan waktu dekompresi . Serta dilakukan perbandingan kinerja pada 2 jenis kombinasi algoritma. Penelitian menghasilkan suatu hasil perbandingan kinerja kompresi file menggunakan kombinasi BMRH (BWT – MTF – RLE – Huffman) dan BRMH (BWT – RLE – MTF – Huffman). Hasil pengujian didapat rata-rata total rasio kompresi, waktu kompresi , dan waktu dekompresi untuk BMRH berturut-turut 70.44 %, 9.6 10 second/byte, dan 22.4 10 , sedangkan BRMH berturut-turut 66.57 %, 9.2 10 second/byte, dan 15.8 10 second/byte.

Pengaruh variasi panjang kunci, ukuran blok, dan mode operasi terhadap waktu eksekusi pada algoritma rijndael

Algoritma Rijndael merupakan salah satu algoritma kriotografi yang berjalan pada mode operasi cipher blok. Rijndael mendukung panjang kunci dan ukuran blok 128-bit sampai 256-bit dengan step 32 bit. Paper ini membahas bagaimana pengaruh variasi panjang kunci, ukuran blok dan mode operasi terhadap waktu eksekusi pada algoritma Rijndael. Eksperimen dilakukan terhadap empat berkas pdf berukuran berbeda, 2.5MB, 5MB, 10MB, dan 20MB. Keempat berkas tersebut dilakukan enkripsi dan dekripsi dengan berbagai kombinasi panjang kunci, ukuran blok, dan mode operasi. Variasi panjang kunci dan ukuran blok adalah 128-bit, 192-bit, dan 256-bit, dan variasi mode operasi adalah ECB, CBC, dan CFB. Tiap kombinasi diulang lima kali untuk mendapatkan waktu eksekusi rata-ratanya. Hasil penelitian menunjukkan waktu eksekusi rata-ratanya. Hasil penelitian menunjukkan bahwa kecepatan eksekusi pada mode ECB dan CBC sangat dipengaruhi olehjumlah putaran, dimana jumlah putaran tergantung pada panjang kunci dan ukuran blok, sedangjan kecepatan eksekusi pada mode CFB relaif dipengaruhi oleh ukuran blok. Keywords: algoritma Rijndael, cipher blok, kriptografi, mode operasi