Judul: Analisis Residu Antibiotika Tetrasiklin dalam Susu Bubuk yang Diimpor melalui Pelabuhan Laut Tanjung Priok
Abstrak: Using antibiotics in dairy farm are popular for therapy or applied as a stimulant growth. However, the presence of antibiotic residue in milk causes harmful effects on consumer's health. Tetracycline is one of the antibiotic used in an effort to improve the health of livestock, especially in disease treatment. The aim of this research was to analyze tetracycline residue in imported milk powder. Sixty imported milk powder samples were collected at Tanjung Priok Sea Port. All samples were tested using enzym linked immunosorbent assay (ELISA) as a screening test and the result showed 8 out of 60 samples (13.33%) were positive of tetracycline residue. Eight samples which were positive determined by ELISA then were confirmed by high performance liquid chromatography (HPLC) method. Five of the 8 samples contained tetracycline residue in the confirmation method. Five samples contained tetracycline residue in a high concentration, which was above the maximum residue limit according to the National Standardization Agency of Indonesia.
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Judul: Deteksi Residu Tetrasiklin Pada Keju Yang Diimpor Melalui Pelabuhan Tanjung Priok, Jakarta.
Abstrak: Tetrasiklin banyak digunakan di peternakan baik untuk pencegahan dan pengobatan penyakit maupun sebagai pemacu pertumbuhan. Administrasi yang tidak benar dapat mengakibatkan residu antibiotik pada susu dan produknya termasuk keju serta dapat berkontribusi terhadap perkembangan resistensi mikroba terhadap obat, penyebaran bakteri resisten, serta berdampak serius terhadap kesehatan konsumen. Penelitian ini bertujuan mendeteksi keberadaan residu tetrasiklin pada keju yang diimpor melalui Pelabuhan Tanjung Priok, Jakarta selama bulan Januari sampai Maret 2015. Kajian yang digunakan pada penelitian ini adalah kajian lintas seksional. Besaran sampel sebanyak 51 dihitung dengan menggunakan formula n = [1-(1-a)1/D] [N-(D-1)/2] dengan keterangan N = jumlah populasi, n = ukuran sampel, a = tingkat kepercayaan (95%) dan D = nilai dugaan populasi positif (13.33%). Deteksi residu tetrasiklin pada keju impor dilaksanakan dengan menggunakan dua metode uji yakni competitive enzyme-linked immunosorbent assay (ELISA) dan bioassay. Kedua pengujian tersebut dilakukan secara pararel. Data dari penelitian ini dianalisa secara deskriptif. Hasil analisis dengan metode ELISA kompetitif menunjukkan tujuh dari 51 (13.7%) sampel yang diuji mengandung residu tetrasiklin dengan konsentrasi 2.47-11.99 ppb. Hasil analisis dengan metode bioassay menunjukkan 11 dari 51 (21.6%) sampel yang diuji mengandung residu tetrasiklin. Kisaran konsentrasi yang didapat dari hasil pengujian ELISA menunjukkan bahwa konsentrasi residu dalam sampel keju impor berada jauh di bawah BMR yang ditetapkan oleh Standar Nasional Indonesia 01-6366-2000 (50 ppb) dan Codex Alimentarius Commission (100 ppb). Hasil penelitian menunjukkan bahwa surveilans terhadap berbagai residu antibiotik pada keju impor perlu dilakukan secara rutin demi menjamin perlindungan kesehatan konsumen.
Keyword: bioassay, ELISA, keju impor, residu antibiotik

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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