Judul: Profil Kimia dan Aktivitas Formula Jamu Antidiabetes Berbasis Sambiloto pada Tikus Sprague-Dawley yang Diinduksi Streptozotosin
Abstrak: Formula jamu antidiabetes berbasis sambiloto (Andrographis paniculata) telah diprediksi menggunakan sistem pendekatan yang dikenal dengan SI-IJAH (Sistem Informasi Jamu Herbal Indonesia). Tujuan penelitian ini adalah menentukan profil kimia, toksisitas, dan aktivitas formula jamu pada tikus diabetes yang diinduksi streptozotosin. Simplisia diekstraksi menggunakan pelarut etanol 70%, profil kimia menggunakan Kromatografi Lapis Tipis (KLT), toksisitasnya dengan metode Brine Shrimp Lethality Test (BSLT), dan efektifitas jamu antidiabetes yang diujikan secara in vivo pada tikus dewasa jantan Sprague-Dawley secara oral selama 28 hari. Hasil penelitian menunjukkan pemrofilan menggunakan KLT menghasilkan 11 pita yang terpisah. Toksisitas pada jamu terkategori toksik sedang. Formula jamu antidiabetes dengan konsentrasi sambiloto 5 mg/kgBB dan 10 mg/kgBB, tidak menunjukkan efikasi sebagai penurun gula darah pada tikus diabetes induksi streptozotosin. Penelitian mengekplorasi dosis formula yang efektif masih perlu dilakukan dan perlunya pendekatan metabolomik.
Keyword: BSLT, formula jamu antidiabetes, KLT, toksisitas

Judul: In vivo anti-diabetic assay of jamu formula and its chromatographic fingerprint analysis
Abstrak: Pengujian in vivo khasiat suatu formula jamu penting dilakukan sebelum dikonsumsi oleh manusia. Tujuan penelitian ini adalah membuktikan khasiat antidiabetes dari formula jamu biofarmaka secara in vivo dan menentukan profil sidik jari formula tersebut dengan analisis kromatografi. Uji in vivo menggunakan mencit yang diinduksi diabetes dengan streptozotosin. Efektivitas formula jamu dalam menurunkan kadar gula darah mencit dibandingkan dengan kontrol positif metformin. Berikutnya, metode kromatografi lapis tipis (KLT) dan kromatografi cair kinerja tinggi (KCKT) digunakan untuk menghasilkan profil sidik jari formula jamu. Hasil penelitian menunjukkan bahwa formula jamu biofarmaka dapat menurunkan kadar gula darah mencit diabetes selama perlakuan. Hasil penurunan ini tidak berbeda nyata dengan kontrol positif. Kromatogram KLT formula jamu yang dielusi dengan toluena:aseton:asam asetat menunjukkan 5 pita yang terpisah dengan baik. Adapun analisis KCKT dengan elusi gradien fase terbalik dan fase gerak air:asetonitril dapat menghasilkan pola kromatogram yang dapat berfungsi sebagai sidik jari formula jamu. Kata kunci: analisis sidik jari, jamu antidiabetes, metformin, streptozotosin, In vivo assay of a jamu formula's efficacy is necessary before it is consumed by human. The aims of this study were proving the efficacy of biopharmaca antidiabetic jamu formula by in vivo assay and determine the formula fingerprint profiles with chromatographic analysis. In vivo assay was done using mice which were diabetic-induced by streptozotocin. The effectiveness of jamu formulas in reducing the mice blood sugar level was compared with metformin as a positive control. The fingerprint profiles of jamu formula were further generated by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC) methods. The results showed that the formula of biopharmaca jamu could reduce the blood sugar levels during treatment. The result of this reduction was not significantly different to that of the positive control. TLC chromatogram jamu formula eluted using toluene:acetone:acetic acid showed 5 well-separated spots. The HPLC analysis with phase-reversed gradient elution and mobile phase of water:acetonitrile gave the chromatogram patterns which can serve as fingerprints of jamu formulas. Keywords: anti-diabetic jamu, fingerprint analysis, metformin, streptozotocin
Keyword: analisis sidik jari, jamu antidiabetes, metformin, streptozotosin

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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