Judul: Analisis penekanan kunci dinamik untuk verifikasi biometrik berbasis jaringan syaraf tiruan propagasi balik
Abstrak: ARIF BUDIMAN. Dynamic keystroke analysis for biometric verification based on artificial neural network backpropagation. Under the direction of SUGI GURITMAN and HERU T. NATALISA. Biometric keystroke analysis studies the typing pattern of human behavior. Literature journals show a good prospect for a verification system and commonly use static method (where training data is similar with the verification data). This research uses dynamic analysis method. The data will be analyzed into certain dominant character pairs so that it is possible for training data to be different than the verification data (using random data display). The dominant character pairs were obtained from preliminary research which was conducted to specifically analyze 756 words in the Indonesian language. It is based on left-right finger movement. The character pairs are an, ng, la, en, ka while the cluster of close character pairs are (ng,ba,ma,na,nd), (an,am,ab), (su,au,ay,gu,di,du,di,ai), (pa,is,ia,ya,ua,us), and (ri,ti,ru,tu,ro,to,ep). The system prototype uses three variables, namely duration (d), interkey (i) and total time (T). Each variable has its own separated Artificial Neural network (ANN) by pre-processing input into five fuzzy classes (SC, C, S, L, SL). The training algorithm is backpropagation and uses two types of training models: A (crispy) and B (ambiguity). The training data were obtained by statistical analysis (mean, median, modus, standard deviation, minimum, maximum) and from fuzzy class pattern complementary data for each character pairs. The research was conducted to study the effect of keyboard layout on False Rejection Rate (FRR) percentage, the effect of data re-taken on % FRR, the effect of ANN verification test on the responses to the new exclusive data (which had never been included in training process). It also studies the effects of identification tests in terms of False Acceptance Rate (FAR) percentage on ANN training model A and B, and the effect of overall response in % FRR on intrusion detection. The conclusions are that model A gives lower % FRR and higher % FAR than model B while model B has a more flexible value of the decision threshold to be used as fuzzy output model in future research. The system stability is affected significantly by keyboard layout and environment conditions during typing. However, the data re-take process carried out more than 56 days afterwards still gave consistent results. ANN has the capability to recognize exclusive data with relatively lower % FRR results. Intrusion can be detected easily by spotting the existence of the % FRR spike. This model has a good capability for Biometric keystroke verification with dynamic text typing but still requires more research for Biometric identification.
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Judul: Pengenalan karakter tulisan tangan dengan menggunakan jaringan syaraf tiruan propagasi balik resilient
Abstrak: Teknologi komputer telah banyak diterapkan untuk membantu menyelesaikan berbagai masalah. Salah satu permasalahan tersebut adalah pengenalan karakter tulisan tangan, yang telah lama diidentifikasi sebagai salah satu permasalahan yang sulit. Untuk memecahkan masalah tersebut dirancang suatu perangkat lunak yang menerapkan sistem yang menyerupai sistem kerja otak manusia yang dikenal dengan jaringan syaraf tiruan (JST). Dalam penelitian ini dibangun suatu sistem yang dapat melakukan pengenalan citra-citra karakter tulisan tangan menggunakan JST resilient backpropagation (RPROP). Data yang diambil berupa karakter numeral (0,1,2,…,9) dari 13 orang dengan menggunakan media kertas. Dari 13 orang tersebut 10 orang menuliskan karakter sebanyak 2 kali, di mana penulisan pertama digunakan untuk data pelatihan dan penulisan kedua digunakan untuk data pengujian. Tiga orang sisanya hanya menuliskan karakter sebanyak 1 kali, karakter tulisan tangan dari 2 orang digunakan untuk menambah data pelatihan sedangkan karakter tulisan tangan dari 1 orang lainnya digunakan untuk menambah data pengujian. Karakter-karakter tulisan tangan tersebut kemudian diubah menjadi citra digital dengan menggunakan scanner dan setiap karakter disimpan sebagai satu file citra, sehingga didapat 230 buah file citra. Dari data tersebut 120 citra digunakan sebagai data pelatihan dan 110 citra lainnya digunakan sebagai data pengujian. Sistem dibangun dengan graphical user interface (GUI) sehingga sistem mudah digunakan dan user-friendly. Dari hasil percobaan didapatkan parameter-parameter optimal yaitu JST dengan 80 neuron hidden, toleransi galat 10-5, Faktor Naik sebesar 1.2 dan Faktor Turun sebesar 0.7. Dengan penggunaan parameter-parameter tersebut sistem dapat mengenali karakter tulisan tangan dengan tingkat generalisasi tertinggi sebesar 93.64%. Kata kunci : pengenalan karakter tulisan tangan, jaringan syaraf tiruan, propagasi balik resilient
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Judul: Growth, Concentration Chlorophyll-a, and Structure of Macroalgae Gracilaria edulis in the Culture Media Containing Copper
Abstrak: The physiological responses (specific growth rate, concentration of chlorophyll-a, morphological and anatomical structure of the vegetative cells) of the marine macroalga Gracilaria edulis at the different concentrations of copper (0.01 as control, 0.04, 0.06 and 0,5 ppm) delivered continuously for 28 days under controlled laboratory conditions were investigated. Result showed that the degree of copper toxicity depends on both factor the concentration and exposure time. Copper concentration and day of exposure decreased significantly the specific growth rate (SGR) of the Gracilaria edulis but only 0,5 ppm Cu decreased the chlorophyll-a content when compared to control. Of the various physiological end points measured, growth proved to be the most sensitive response than chlorophyll-a with reductions in specific growth rate (SGR). The reason for this could be related to a greater sensitivity of the enzymes involved in growth than those related to chlorophyll-a or a greater energy needs to maintain cellular integrity and metabolic processes under sublethal Cu stress. In all cases, the main effect on vegetative cells were structure changes in cortex and subcortex cells, compared to control, macroalgae exposed to copper showed depigmentation and blending in the lateral branches, heavy staining of the cell wall and severe damage of cell membrane, while the mucilage and the cell walls was densely fibrillar, thick and heavily stained, the cortex cell was clearly vacuolated, the presence of endophytic on the cell wall and endophytic spores in the mucilage. The presence vacuoles should be related to a metal immobilization mechanism, minimizing the Cu toxicity for cells. The epiphytic bacteria can act as a biofilter diminishing the availability of Cu for macroalgae accumulation.
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