Judul: Studi Pendahuluan Tentang Usaha Pengendalian Erosi DAS Cipeles
Abstrak: Disadari bahwa kerusakan daerah penyangga di daerah hulu sungai-sungai d i Indonesia akan merusak perilaku tata air ( hidrologi ) daerah aliran sungai (DAS) tersebut. Sel a i n i t u , penanganan petani terhadap lahan garapannya tanpa disadari telah mengakibatkan hilangnya lapisan subur tanah sedikit demi sedikit melalui proses erosi .
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Judul: Analisa dan Simulasi Sistem Perencanaan Program Pengendalian Erosi di daerah Aliran Air Sungai Cimanuk Hulu
Abstrak: Munggaran Supriatna (F.20 0934). Analisa dan Simulasi Sis- tem Perencanaan Program Pengendalian Erosi di Daerah Aliran Sungai Cimanuk Hulu. (Di bawah bimbingan Ir. H. Sukandi Su- kartaatmadja, MS). milik IPB Di dalam ekosistem DAS ada dua proses alami yang sangat penting, yaitu limpasan permukaan dan erosi. Erosi yang ter- jadi dapat menyebabkan menurunnya daya dukung tanah. Sedang- kan dilain pihak erosi dapat pula menyebabkan terjadinya pen- dangkalan sungai, bendungan dan danau di bagian hilirnya. Sampai saat ini belum banyak diketahui pola tata guna lahan mana yang dapat mengurangi terjadinya erosi, sedangkan untuk mengetahui besarnya erosi di lahan pertanian, harus dilakukan pengukuran langsung di lapangan dengan percobaan- percobaan, pekerjaan ini sangat memakan waktu, biaya dan te- naga. Sedimen tahunan rata-rata (ton/ha) di DAS Cimanuk Hulu sejak tahun 1981 - 1985 menunjukkan peningkatan yang cukup besar, yaitu 100.12, 128.19, 528.58, 977.14 dan 5103.00 (Su- harto, 1986). Ini menunjukkan bahwa keadaan hidro-orologis DAS Cimanuk Hulu semakin kritis. Berdasarkan hal-hal terse- but diatas, maka penelitian ini dilaksanakan dengan titik perhatian utama pada erosi untuk berbagai pola penggunaan lahan dengan simulasi. ...
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Judul: Klasifikasi genre musik menggunakan Learning Vector Quantization (LVQ)
Abstrak: Radio stations and music television have a milion of music tapes. A lot of musical genres create a problem when people wants to determine the right genre of a new kind of music. To classify the musical genre is not an easy task, because the musical genre is really difficult to standardization. Automatic musical genre classification can assist the human role in that process and help people to searching for the song acording to the genre that people want. This research using Mel Frequency Coefficient Cepstrum (MFCC) to obtain feature extraction. Learning Vector Quantization (LVQ), one kind of artificial neural network used for classification method. The number of genres that are used were four kind of musical genre, that is rock, classic, keroncong, and jazz with four different duration that is 5 second, 10 second, 20 second and 25 second. This research using k- fold cross validation to distribute dataset for training and testing set with the number of folds as much as 2 until 10 fold. This research succesfully implemented MFCC feature exraction and classification using LVQ. Based on this research, the accuracy of the classification using Learning Vector Quantization reaches 93,75% for the four type musical genre. The highest accuracy value was obtain from the experiments with a duration of 10 second and the number of fold 4. Training time for each duration is 30 minute for 5 second music duration, 45 minute for 10 second music duration 120 minute for 20 second music duration and 150 minute for 25 second music duration.
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