Judul: Dinamika populasi ikan lemuru, Sardinella Lemuru Bleeker 1853 (Pisces: Clupeidae) di perairan Selat Bali dan alternatif pengelolaannya
Abstrak: Perairan Selat Bali merupakan suatu bentuk perairan yang relatif sempit, berbentuk corong dengan luas kira-kira 2.500 km2 , produktivitasnya tinggi karena ter jadi proses penaikan air mulai kira-kira bulan April sampai Oktober. Ikan lemuru merupakan jenis ikan yang dominan yang tertangkap di perairan tersebut, produksinya berkisar antara 1.583-21.363 ton (32,85-97,9%) atau rata-rata 10.708 ton (78,46%) per tahun, sedangkan setiap bulannya berkisar antara 501-1.441 ton (65,86-92,89%) atau rata-rata 894 ton (76,07%) dalam periode 1984 sampai dengan 1989 (Data hanya dari KUD Mina Karya, Pengambengan, Bali).
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Judul: Distribution and Abundance Analysis of the "Lemuru" (Sardinellla lemuru Bleeker, 1853) in Bali Strait Waters: Its Relation with Fishing Optimization
Abstrak: lkan lemuru ( Sardine/la /emuru) merupakan sumber daya perikanan taut yang sangat penting dan spesifik di perairan Selat Bali, dengan rata-rata produksi tahunan mencapai 80% dari produksi total perikanan laut. Produksi perikanan lemuru di perairan Selat Bali ternyata sangat berfluktuasi baik secara musiman ataupun tahunan. Untuk menangkap ikan lemuru di perairan Selat Bali digunakan alat tangkap pukat cincin dan dilakukan oleh nelayan yang berasal dari dua propinsi, yakni Jawa Timur (Muncar) dan Bali (Pengambengan). Perairan Selat Bali mempunyai bentuk unik yang menyerupai corong yaitu menyempit di utara dengan lebar sekitar 1 mil dan melebar di selatan sekitar 28 mil. Perairan sebelah utara berhubungan dengan Laut Bali, sedang di sebelah selatan berhubungan dengan Samudera Hindia. Dengan topografi seperti ini, karakteristik perairan Selat Bali sangat dipengaruhi oleh massa air yang berasal dari Samudera Hindia dan diperkirakan sangat bervariasi baik secara spasi,al ataupun temporal (musiman). Terjadinya variasi parameter oseanografi, baik secara spasial maupun temporal akan berpengaruh langsung atau tidak langsung terhadap penyebaran dan kelimpahan ikan lemuru. lkan lemuru juga memiliki sifat seperti kebanyakan jenis ikan clupeids lainnya yaitu senang hidup bergerombol, membentuk gerombolan besar yang dinamakan schooling. lnformasi tentang karakteristik (panjang, tinggi, densitas, dan kedalaman) gerombolan ikan lemuru ini sangat penting bagi keberhasilan penangkapan dengan pukat cincin yang dioperasikan dengan cara berburu tanpa menggunakan alat bantu pengumpul ikan. De_mikian juga dengan informasi tentang ukuran ikan lemuru yang akan ditangkap sangat diperlukan untuk penentuan pola penangkapan yang optimum sehingga tidak mengganggu kelestarian sumber dayanya. Penelitian ini dilakukan dengan tujuan: (1) mengkaji penyebaran dan kelimpahan ikan lemuru berdasarkan musim dan hubungannya dengan parameter oseanografi, (2) mendapatkan informasi tentang karakteristik gerombolan ikan lemuru berdasarkan waktu (musim dan tengah harian), (3) mengetahui pola pengelompokan ukuran ikan berdasarkan daerah penangkapan dan waktu (bulan), dan (4) menyusun pola penangkapan ikan lemuru yang optimum dengan memperhatikan kaidah-kaidah pengelolaan sumber daya perikanan., small pelagic resources in Bali Strait waters. Some factors have played important roles in the variation of lemuru production, among others, are the oceanographic parameters. The objectives of this research are: (1) to study the distribution and the abundance of lemuru specifically their relationship to oceanographic parameters, (2) to analyze the size structure of lemuru caught by the purse seine, (3) to evaluate the characteristics of lemuru schools in different seasons, (4) to develop an optimum fishing pattern for lemuru purse seine fisheries. This research was carried out in two years, from 1996 to 1998 in the Bali Strait waters, applying acoustic methods and the analysis of its abundance index (catch per unit effort, CPUE). Oceanographic observations were also carried out along cruise tracks using research vessel. The analytical hierarchy process was applied for determining the optimum fisheries pattern. In Bali Strait, lemuru was abundant at the end of southeast monsoon (August) and transition season-2 (September-November) which was indicated by a high CPUE. The acoustic data analysis showed that during transition season-2 the abundance pelagic fish was higher than during transition season-1. Upwelling was occurred during southeast monsoon in Bali Strait, characterized by low temperature, high salinity, and phytoplankton was abundant. · Using factorial correspondence analysis, it can be identified four
Keyword: Echolocation Sonar, Ikan Lemuru, sardinella Lemuru

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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