Judul: Daya Inhibisi Ekstrak Rimpang Jahe Merah dan Kulit Kayu Manis terhadap Aktivitas Enzim Siklooksigenase 2 dan Enzim Xantin Oksidase Secara In Vitro
Abstrak: Red ginger and cinnamon are medicine plants which can be used as anti-inflammation and lowering uric acid level in blood. In this study, water and ethanol extracts of red ginger root and cinnamon bark have been assayed for phytochemical constituent, toxicity by brine shrimp lethality test, and in vitro inhibition of cyclooxygenase 2 and xanthine oxidase activities. Diclofenac, ibuprofen and allopurinol were used as positive control. Based on phytochemical assay, water and ethanol extracts of red ginger root contained alkaloids, steroids, and flavonoids, while its water extract of cinnamon bark contained kaloids, triterpenoids, saponins, flavonoids, and tannins. The result of probit analyses of water and ethanol extracts of red ginger root and cinnamon bark showed that their LC50 values were 144 ppm, 108 ppm, 125 ppm, and 107 ppm, respectively. Ethanol extract of red ginger root showed very low inhibitory effect to cyclooxygenase 2 activity (23.81%) as compared to diclofenac (95,43%) and ibuprofen (93,59%). Water and ethanol extracts of cinnamon bark that have inhibitory effect more than 50% to xanthine oxidase were, 54.64% and 56.80%, with their IC50 values of 70 ppm and 63 ppm, respectively. Allopurinol exhibited the highest inhibitory effect (97.91%). The value of inhibitory effect showed that ethanol extract of red ginger root have low potential as inhibitor to cyclooxygenase 2 and rheumatoid drugs; water and ethanol extracts of cinnamon bark can be considered as potential as inhibitor of xanthine oxidase and can be used for uric acid drugs.
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Judul: Daya Hambat Siklooksigenase-2 oleh Campuran Ekstrak Suruhan (Peperomia pellucida) dan Jahe Merah (Zingiber officinale) dalam Inflamasi
Abstrak: Peperomia pellucida and Zingiber officinale were known to have anti-inflammatory activity, yet there was no further research to verify their inhibition in inflammatory process. The aim of this research is to test the potency of mixture extract of suruhan (Peperomia pellucida) and red ginger (Zingiber officinale) as anti-inflammation through in vitro inhibition of cyclooxygenase-2 enzyme. Anti-inflammatory effect was analyzed with ELISA (Enzyme Linked Immunosorbent Assay) and spectrophotometry method at wavelenghth 412 nm. Maximum inhibition of Peperomia pellucida extract against cyclooxygenase-2 was 48% at 100 μg/mL, Zingiber officinale extract inhibition was 44% at 300 μg/mL, and for mixture extract inhibition was 15% at 175 μg/mL. Mixture extract of Peperomia pellucida and Zingiber officinale showed lower inhibitory potency to cyclooxygenase-2 compared to their single extracts. Cytotoxicity test with Brine Shrimp Lethality Test method showed that Peperomia pellucida extract, Zingiber officinale extract, and mixture extract of both plants have a cytotoxity effect with each LC50 values are 339, 591, and 728 μg/mL. Phytochemical test showed that each extract contains flavonoid, phenolic, alkaloid, and tannin. Saponin and steroid were contained only in Peperomia pellucida extract and terpenoid was contained only in Zingiber officinale extract. Rendement extract of Peperomia pellucida is 24% and Zingiber officinale is 21%.
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Judul: Pengembangan model probabilistic neural network (PNN) pada pengenalan kisaran usia dan jenis kelamin berbasis suara
Abstrak: Voice signal can be used to represent a speaker. Voice can also be used to recognize the age range and the gender of the speaker, because of the difference in frequency of the voice signal for different age and gender. Age range for this experiment is divided into children (8-10 years old), teenagers (12-17 years old) and adults (30-50 years old). We performed feature extraction to obtain a representation of the speaker using Mel-Frequency Cepstrum Coefficient (MFCC). MFCC is used with 13, 20, 26 coefficients. Probabilistic Neural Network (PNN) is used as the feature matching model. The PNN model is constructed from three different proportions of TRD (Training data) : TSD (Testing data) (25%:75%, 50%:50%, 75%:25%). MFCC with 26 coefficient gives the best accuracy of 93.47%. The proportion of 75% (TRD) : 25% (TSD) also gives the best accuracy of 94.22%. PNN-age and PNN-gender identification result in an accuracy of 91.26%. Specifically, PNN accuracy percentage for male and female are 92.26%, and 95.20%, respectively, while for children, teenagers and adults the accuracies are 99.85%, 92.28% and 95.57%, respectively. In conclusion, the average of accuracy for all categories is 91.26%. The experiment using the voice of male-teenagers gives the worst accuracy, which is caused by the overlapping frequencies of voice in children, teenagers and adults.
Keyword: Voice identification, PNN model, MFCC, Age-identification, Gender-identification