Judul: Biologi reproduksi Ikan Lemuru (Amblygaster sirm Walbaum,1792) di perairan Selat Sunda
Abstrak: Ikan lemuru (Amblygaster sirm) merupakan bagian dari ikan pelagis kecil yang tertangkap menggunakan purse seine di perairan Selat Sunda, namun belum diketahui dengan memadai aspek biologinya. Tujuan dari penelitian ini yaitu mengkaji beberapa aspek biologi reproduksi yang mencakup rasio kelamin, ukuran pertama kali matang gonad (Lm), musim pemijahan, tipe pemijahan dan potensi reproduksi. Penelitian ini berdasarkan data primer yang dilaksanakan pada bulan April-Agustus 2015 dengan waktu pengambilan contoh dilakukan setiap 30 hari sebanyak lima kali. Jumlah ikan lemuru yang diambil selama penelitian sebanyak 255 ikan betina dan 536 ikan jantan. Hasil penelitian ini menunjukkan bahwa rasio ikan lemuru tidak seimbang (rasio jantan dan betina sebesar 2:1). Hasil analisis ukuran pertama kali matang gonad ikan betina dan jantan masing-masing adalah 186 mm dan 188 mm. Musim pemijahan ikan lemuru terjadi pada bulan Mei sampai Agustus dengan puncak pemijahan pada bulan Juli, dan tipe pemijahan terindikasi bersifat partial spawner. Potensi reproduksi ikan lemuru dapat dikategorikan cukup tinggi dengan kisaran fekunditas 3 600-42 390 butir.
Keyword: reproductive biology, spotted sardinella, Sunda Strait

Judul: Aspek Biologi Reproduksi Ikan Lemuru (Sardinella longiceps C.V.) di Perairan Teluk Sibolga, Sumatera- Utara
Abstrak: Ikan lemuru (Sardinella longiceps C.V.) merupakan ikan ekonomis penting Saat ini informasi biologis ikan lemuru di perairan Sibolga belum ada. Informasi tersebut dibutuhkan untuk tujuan pengelolaan kegiatan perikanan di wilayah tersebut. Penelitian bertujuan untuk mengetahui aspek biologi reproduksi ikan lemuru (Sardinella longiceps C.V.).
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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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