Judul: Solvent Extraction Optimization of Bungur Leaf and Bark and Their Inhibitory Activity against Cyclooxygenase 2.
Abstrak: Inflamasi merupakan respons perlindungan normal terhadap cedera jaringan. Pengujian terhadap aktivitas antiinflamasi dapat dilakukan secara in vitro dengan metode inhibisi terhadap enzim siklooksigenase 2 (COX-2) menggunakan ekstrak daun dan kulit batang bungur (Langerstroemia speciosa (L.) Pers). Penelitian ini bertujuan melakukan optimisasi pelarut akuades dan etanol 30% untuk mendapatkan rendemen terbaik dan melihat potensi daun dan kulit batang bungur sebagai antiinflamasi. Rendemen tertinggi diperoleh pada ekstrak etanol 30% kulit batang, yaitu sebesar 18,13%. Pengujian daya inhibisi terhadap COX-2 dari ekstrak akuades dan etanol 30% daun dan kulit batang bungur dibuat pada rentang konsentrasi 10–100 µg/mL lalu dibandingkan dengan obat sintetis yang biasa digunakan sebagai antiinflamasi, yaitu diklofenak. Daya inhibisi terhadap COX-2 tertinggi ditunjukkan oleh ekstrak akuades kulit batang, yaitu sebesar 51,30%, sedangkan daya inhibisi diklofenak mencapai 93,43%. Nilai IC50 yang diperoleh dari ekstrak akuades kulit batang tergolong aktif, yaitu sebesar 96,51 µg/mL. Hasil pengujian menunjukkan bahwa tanaman bungur memiliki potensi sebagai antiinflamasi.
Keyword: antiinflammatory, bungur, COX-2, inhibitor, Langerstroemia speciosa

Judul: Prediction of Anti-inflammatory Compounds in Okra Pods (Abelmoschus esculentus L.) in Inhibition of Cyclooxygenase-2
Abstrak: Siklooksigenase-2 (COX-2) mengkatalisis pengubahan asam arakidonat menjadi senyawa mediator inflamasi, seperti prostaglandin (PG). Buah okra memiliki aktivitas antiinflamasi berdasarkan studi in vitro dan in vivo, namun belum diketahui senyawanya yang dapat menginhibisi COX-2. Penelitian ini bertujuan memprediksi aktivitas antiinflamasi senyawa aktif buah okra yang dapat menghambat COX-2 berdasarkan interaksinya dengan residu asam amino sisi aktif, katalitik dan selektif. Perangkat lunak YASARA Structure digunakan untuk penambatan molekuler, sementara Discovery Studio Visualizer dan Pymol digunakan untuk analisis interaksi ligan-reseptor. Penambatan dilakukan pada ukuran gridbox 2,0 Å. Penapisan virtual menghasilkan 16 senyawa aktif buah okra hasil studi pustaka menghambat COX-2 dengan nilai energi bebas pengikatan (ΔG) di atas -6,0 kkal/mol. Hasil ini masih lebih rendah dari asam mefenamat sebagai ligan eksperimental. Kuersetin dan α-cubebene paling potensial menghambat COX-2, dengan nilai ΔG -7,887 dan -7,502 kkal/mol serta Ki 1,63 dan 3,13 μM, namun kelimpahannya rendah di buah okra. Senyawa yang paling melimpah adalah kuersetin-3-O-glukosida (-6,668 kkal/mol), berinteraksi selektif dengan His90, Val523 dan Arg513, sama halnya dengan celecoxib sebagai ligan pembanding., Cyclooxygenase-2 (COX-2) catalyzes the conversion of arachidonic acid into inflammatory mediator compounds, such as prostaglandins (PG). Okra pods has anti-inflammatory activity based on in vitro and in vivo studies, but the compounds that can inhibit COX-2 is not yet known. This study aimed to predict the anti-inflammatory activity of the active compounds of okra pods as COX-2 inhibitor based on their interactions with amino acid residues on the active, catalytic and selective sites. YASARA Structure software was used for molecular docking, while Discovery Studio Visualizer and Pymol were used for ligand-receptor interaction analysis. Molecular docking is done on a gridbox size of 2.0 Å. Virtual screening resulted in 16 active compounds of okra pods from literature study inhibiting COX-2 with binding free energy (ΔG) values above -6.0 kcal/mol. These values is still lower than mefenamic acid as an experimental ligand. Quercetin and α-cubebene were the most potent to inhibit COX-2, with ΔG -7.887 and -7.502 kcal/mol and Ki 1.63 and 3.13 μM, however their abundance was low in okra. The most abundant compound was quercetin-3-O-glucoside (-6,668 kcal/mol) and selectively bind to His90, Val523 and Arg513, similar to celecoxib as a reference ligand.
Keyword: anti-inflammation, COX-2, molecular docking, okra pods, selective

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