Judul: Perdagangan Jenis Kura-kura Darta dan Kura-kura Air Tawar di Jakarta
Abstrak: Tortoises and freshwater turtles, as wildlife resources, have long been used since the beginning of mankind to the modern era as food, medicinal ingredients, pets, handicrafts and religious release. This research aim: (1) to analyze the trade of tortoise and freshwater turtles as pets in Jakarta; (2) to analyze the perception of stakeholders on the conservation og Indonesia's biodiversity.
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Judul: Perdagangan reptilia sebagai binatang peliharaan di DKI Jakarta
Abstrak: DKI Jakarta in the world of wildlife trade is often referred as yellow dots, a city vulnerable to illegal trade. Wildlife trade in DKI Jakarta consists not only on ornamental fish, mammals and birds, but even reptiles. This study aims to describe trade locations, identify species of trade and analyze preferences of pet reptiles. The study was conducted in five traditional markets and 35 pet shops in DKI Jakarta, using interviews and investigative methods. I also carried out survey on cyber-market trade and interviews to pet reptile owners. The study was carried out from August 20th to November 20th, 2010. A total of 52 species (26 species of turtles, 23 snakes and three lizards) were traded on traditional markets, and 46 species (21 species of turtles, 19 species of snakes and six species of lizards) at the pet store. In traditional markets the common species sold are local species, but not on pet stores where most are imported species. In total, survey found five species of protected reptiles according to PP No. 7 of 1999, four species of Appendix I CITES’s reptiles and six species listed as critically endangered by IUCN Red List. Three forums in internet carried out cyber markets of pet reptiles. The most favorite pet reptiles were snakes, especially the ball python (Python regius).
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Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
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