Judul: Penelitian Angkutan Sedimen Suspensi Kaitannya Dengan Kondisi Fisik Daerah Aliran Sungai Citarum Hulu
Abstrak: Fungsi dan peranan das Citarum di Jawa Barat sangat- lah vital dalam menguasai hajad hidup orang banyak. Ke- putusan Presiden nomor 21 tahun 1984 Repelita IV menyata- kan bahwa das Citarum, khususnya Citarum Hulu termasuk di- kembangkan pengelolaannya secara terpadu dan ditetapkan sebagai salah satu das super prioritas di Indonesia untuk ditangani secara intensif. Secara awam kerusakan suatu das dapat dilihat dari keruhnya air sungai, disamping adanya indikasi berkurang- nya debit air sungai pada musim kemarau dan banjir pada musim penghujan (Ilyas, 1982). Menurut DPMA (1985), per- bandingan debit aliran maksimum dan minimum Sungai Citarum Hulu makin meningkat dari tahun 1973 sampai tahun 1981, yaitu: 51, 18, 41, 90, 59, 66, 79. Gejala lain adalah tingginya intensitas erosi dan sedimentasi. Oleh karena itu untuk memantau kerusakan das, maka diperlukan suatu analisis dan evaluasi sedimentasi yang kontinyu. Lima sub das Citarum Hulu yang dijadikan objek pene- litian adalah Cikapundung Gandok, Citarik Rancakemit, Ci- jalupang Peundeuy, Cirasea Cengkrong dan Citarum Majalaya, berturut-turut mempunyai bentuk daerah pengaliran radial, ...
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Judul: Analisis angkutan sedimen suspensi hubungannya dengan kondisi fisik daerah pengaliran Cimanuk Hulu
Abstrak: Daerah pengaliran Cimanuk Hulu, yang memegang peranan pentin dari segi hidrologis maupun sosial ekonomis,dinyatakan (1972) sedang menunjukkan gejala kemunduran hidrologis, indikasi awam kerusakan DAS dapat dilihat dari keruhnya air sungai, selain ditandai pula oleh besarnya fluktuasi debit air sungai serta meningkatnya laju erosi dan sedimentasi.
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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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