Judul: Analisis Grafologi Berdasarkan Huruf a dan t Menggunakan Algoritme K-Nearest Neighbor
Abstrak: Graphology or handwriting analysis is a method of identifying, analyzing, evaluating, and understanding personality through the strokes and patterns revealed by handwriting. People who can analyze handwriting are called graphologist. Graphologist can have a subjective assesment. Different graphologists can analyze the same handwriting but give different results. In addition, the accuracy of handwriting analysis depends on the graphologist’s ability. A system that can recognize handwrititng patterns is required to overcome these problems. The identification system which is implemented in this research uses k-Nearest Neighbor Algorithm. This research used 135 images of the letter a and 150 images of the letter t. This research is divided into five parts, three for the letter t and two for the letter a. We use a value of k from 1 to 20 for searching the best. Identifying personality based on the letter a, and identifying personality based on the letter t then clasifying them into one of the three character classes available. The results of this research is that the system has 88.89% accuracy for the letter a and 70% accuracy for the letter t.
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Judul: Analisis Grafologi Berdasarkan Huruf a dan t Menggunakan Jaringan Syaraf Tiruan Propagasi Balik Standar
Abstrak: Graphology or handwriting analysis is a method of identifying, analysing, evaluating, and understanding personality through the strokes and patterns revealed by handwriting. People who can analyze handwriting called graphologist. Graphologist can have a subjective assesment. Different graphologist can analyze the same handwriting but give different results. In addition, the accuracy of handwriting analysis depend on the graphologists ability. A system that can recognize handwrititng patterns is required to overcome these problems. Identification system which is implemented in this research uses Backpropagation Neural Network (BPNN). This research used 135 images of letter a and 150 images of letter t. This research is divided into three parts which are determining optimal combination of BPNN parameter, identifying personality based on letter a, and identifying personality based on letter t then classifies them into one of three character classes available. The results of this research is that the system has 98.15% accuracy for letter a and 73.33% for letter t. The result shows that Backpropagation Neural Network can be used to classify the personality
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Judul: Co-infection of Infectious Myonecrosis Virus (IMNV) and Vibrio harveyi in Pacific White Shrimp (Litopenaeus vannamei)
Abstrak: L. vannamei production in Indonesia was growing intensively. In 2006, IMN (infectious myonecrosis) disease was confirmed spread to Indonesia shrimp farm and declined shrimp production. The study was conducted to investigate effect of dose of V. harveyi on co-infection with IMNV in L. vannamei as well as development of viral infection. Shrimps juvenile were oral infected with IMNV infected shrimps 10 % feeding rate during 3 days and co-infected with 106, 107 and 108 cfu/ml V. harveyi. Mortality rate was 0 % in control and single infection of V. harveyi except 108 cfu/ml treatment. Mortality pattern demonstrated on co-infection was faster and higher than single IMNV infection in 14 days observation. The density of green colony Vibrio in hepatopancreas of co-infected shrimps collected in 2, 4, 6, 8 and 10 days post infection were higher than V. harveyi single infected (significantly in 10 days post infection). There were no difference of IMN disease development between co-infection and IMNV single infection. It was confirmed by visual gross sign appeared, tissue and lymphoid organ histophatology, organ abnormality, and PCR test. In conclusion, IMN disease caused higher and faster mortality on co-infection with V. harveyi, but not affect to IMN disease development.
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