Judul: Pengaruh sistem pertanaman dalam strip terhadap aliran permukaan erosi dan kehilangan hara melalui erosi
Abstrak: Penggunaan strip rumput setaria merupakan salah satu teknik konservasi tanah dan air yang dapat mengurangi aliran permukaan, erosi serata jumlah hara dan bahan organik yang hilang lewat erosi sehingga dapat mempertahankan produktivitas lahan. Penelitian ini dilaksanakan di SUB-DAS Garang Hulu di desa Lerep, kecamatan Ungaran, Kabupaten Semarang yang bertujuan untuk mengetahui dan mempelajari pengaruh sistem pertanaman strip dengan melibatkan strip kacang tanah dan strip rumput setaria terhadap perubahan jumlah aliran permukaan, erosi, kehilangan hara dan bahan organik yang terbawa erosi, dan produktivitas lahan. Penelitian disusun berdasarkan Rancangan Acak kelompok dengan ulangan sebanyak 3 kali yang dikelompokkan berdasarkan kemiringan lereng. Tujuh perlakuan yang dicobakan pada masing-masing kemiringan lereng meliputi 1) Penanaman strip kacang tanah lebar 4,5 m dengan strip rumput setaria lebar 0,6 m (LS1); 2) Penanaman strip kacang tanah lebar 4,5 m dengan strip rumput setaria lebar 1,2 m (L1S2); 3) Penanaman strip kacang tanah lebar 4,5 m dengan strip ..dst
Keyword: Soil conservation, Water conservation, Land productivity

Judul: Pengaruh strip rumput vetiver dan sistem pengelolaan sisa tanaman terhadap aliran permukaan dan erosi
Abstrak: Penanaman strip rumput dan sistem pengelolaan sisa tanaman merupakan teknik konservasi tanah dan air yang relatif murah dan mudah dilakukan petani serta mampu mengatasi masalah erosi dan penurunan produktivitas tanah..dst
Keyword: Vetiver, Erosion control, Pengelolaan tanah

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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