Search

A Studi on α -mangostin distribution profile using High Performance Liquid Chromatography (HPLC) method was conducted on several organs (heart, liver, kidneys, lungs, and intestines) and secretion (feces and urine) of mice. The compound was administered orally to male mice with the dose of 20 mg/kg body-weight. The validation of the HPLC method employed C-18 column, methanol moving phase and ultiaviolet detector with λ 319 nm. The tested parameters were system aptness test, linearity test, detection and quantitative limit test, precision test, accuration test of the α-mangostin quantity in urine, feces, heart, liver, kidneys, intestines and lungs. Results showed that method validation met the requirements. The α-mangostin distribution profile at the 6th hour showed that highest amount was in the heart (9,12 µg/g while the lowest amount was in the intestines (2.23 µg/g. The α-mangostin elimination profile in the urine at the 12th hour was 0.75 bpj, while there was no trace of α-mangostin in the urine at the 24th, 36 th and 48 th hour. At the 12 th hour the quantity of α-mangostin in the feces was 3.21 bpj,, then it rose to 7 .77 bpj at the 24 th hour. It decreased to 2.74 bpj at the 36th hour and there was no trace at the 48th hour. Key words: α-mangostin, distribution profile, elimination profile.

Snake is an active contour model that catches and locks image edges, then localizes them accurately. The simplest Snake consists of a set of control points that are connected by straight lines to form a closed loop. This paper discusses the application of Snake to find the visual feature of lip shapes. In most previous papers, visual feature of lip shapes is represented by Snake’s contour. In this paper, the feature of lip shapes is represented by six control points on lip Snake’s contours. By simply utilizing six control points representing one lip Snake’s contour, it is expected to reduce the burden on pattern recognition stage. To demonstrate the performance of this method, some analysis has been conducted on the effect of lip conditions and illumination. The results shows that the overall lip feature extraction using the proposed method is better for lips that have more contrast to the surrounding skin, optimum room illumination that gives the best result is in the range of 330-340 lux.

A speech therapeutic tool had been developed to help Indonesian deaf kids learn how to pronounce words correctly. The applied technique utilized lip movement frames captured by a camera and inputted them in to a pattern recognition module which can differentiate between different vowel phonemes pronunciation in Indonesian language. In this paper, we used one dimensional Hidden Markov Model (HMM) method for pattern recognition module. The feature used for the training and test data were composed of six key-points of 20 sequential frames representing certain phonemes. Seventeen Indonesian phonemes were chosen from the words usually used by deaf kid special school teachers for speech therapy. The results showed that the recognition rates varied on different phonemes articulation, ie. 78% for bilabial/palatal phonemes and 63% for palatal only phonemes. The condition of the lips also had effect on the result, where female with red lips has 0.77 correlation coefficient, compare to 0.68 for pale lips and 0.38 for male with mustaches.

The level of satisfaction settled homeowner satisfaction rated based on the condition of the residence and its surroundings are influenced by socio-economic characteristics of residential homeowners. This study was conducted to determine the relationship of socio-economic characteristics with the level of satisfaction in living a simple homeowner in Perumahan Tamansari Bukit Mutiara Balikpapan. The results of the study showed that a simple house is not owned by the people of the lower middle. This is shown on the socioeconomic characteristics of a simple homeowners aged between 35-40 years, S1 educated, worked as private employees with an income of 5 to 7.5 million dollars permonth. Meanwhile, homeowners who are elderly feel less satisfied living in a residential neighborhood because of the lack of activity that involves local people. Simple homeowner satisfaction with their home have been met because the home is already renovation with addition of space that is based on the level of need. Socioeconomic characteristics that influence satisfaction settle the residence include age, education, occupation and number of occupants. Satisfaction simple homeowners against residential neighborhood is met because the residental location of flood -free, guaranteed security and quiet environment. Socioeconomic characteristics that influence satisfaction settled on the environment include residential status, age, education, income and long lived.