Judul: Telaah Pengadaan "Stok Tegakan" Bahan Baku Substitusi Pabrik Kertas di Kabupaten Banyuwangi
Abstrak: Industri kertas merupakan industri yang menggunakan bahan baku yang dagat diperbaharui (renewable), berupa kayu dan bahan berserat (berlignoselulosa) lainnya. D i Kabupaten Ranyuwangi terdapat industri kertas yang menggunakan bahan baku jenis s e r a t pendek (hardwood) dan kayu jenis serat panjang (softwood) s e r t a pulp impor.
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Judul: Analisis biaya sumberdaya domestik (BSD) dan dampak kebijaksanaan pemerintah pada proyek pengembangan industri kertas terpadu kasus pada PT Indah Kiat Pulp & Paper Corporation, Perawang, Kecamatan Siak Sri Indrapura, Kabupaten Bengkalis, Propinsi Riau
Abstrak: Kertas tulis dan cetak merupakan produk yang paling banyak dibutuhkan sehari-hari diantara berbagai macam jenis kertas. Kertas merupakan salah satu harapan andalan ekspor nasional dalam jangka waktu beberapa tahun mendatang. Sejalan dengan hal tersebut, maka langkah pengembangan industri kertas di Indonesia diarahkan pada skala yang besar dan terpadu dengan pembangunan Hutan Tanaman Industri (HTI) sebagaimana PT Indah Kiat Pulp & Paper Coorporation di Propinsi Riau. Langkah ini didukung oleh seperangkat kebijaksanaan pemerintah di bidang pengusahaan hutan, restriksi ekspor kayu bulat serta proteksi yang berupa tarif terhadap pulp dan kertas impor.
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Judul: Clustering metagenome fragments using growing self organizing map
Abstrak: Metagenome is a research about analyzing microbes in the large community and allowed the culture-independent. The microorganism samples taken directly from environment is not easy to assembly because contains mixture microorganism. If sample complexity is very high and come from high diversity environment, difficulties of assembling DNA sequence are increasing because the interspecies chimeras can be happen. Clustering commonly using supervised learning, but the supervised learning depends on avaibillity of data training. Because of that, in this research we used unsupervised learning to clustering the metagenome fragments. Beside that, clustering usually using the longer fragments, which is ≥ 8 kbp and have a small community (less than 100 microorganism). The purpose of this research is to analyze the effectiveness and efficiency of Growing Self Organizing Map to the clustered large community of metagenome fragments. We used trinucleotide, tetranucleotide, and combination of oligonucleotide frequency that consider the don’t care situation called spaced k-mer frequency as a features. As a feature extraction, we using k-mer and spaced k-mer. Based on parameter combination using oligonucleotide frequency, the best combine between Learning Rate and Neighborhood Size is a spaced k-mer frequency. We tested to get a better parameter combinatoin into [10 10] map size and 10 epochs training lenght. Error to mapped metagenome fragments using spaced k-mer frequency is 0.665 for quantization error, 0.06 for topographic error and 13.07% for error percentage. Using the map size between [100 – 500], map unit 300 – 5000 unit, and training lenght 10 epochs, gives the best training in the map size [100 150] with 300 map unit. The training time is 51 minutes and percentage error is 6.43%.
Keyword: Clustering, Growing Self Organizing Map, metagenome fragments