Judul: Keefektifan Formulasi Cair dan Pasta Ralstonia pickettii untuk Mengendalikan Penyakit Hawar Pelepah pada Padi
Abstrak: Rice is the most important food crop in Indonesia. Sheath blight disease is one of inhibiting factors in rice productivity improvement. Ralstonia pickettii has been reported as potential biological control agent of sheath blight. R. pickettii formulation technologies should be developed to increase its durability and effectiveness in suppressing sheath blight disease in the field. The purpose of this research was to determine the effectiveness of R. pickettii formulation in liquid and paste form. We formulated R. pickettii in liquid formulation by addition of glycerol to a final concentration of 25%. Paste formulation was made with composition of 44% talc, 3% xanthan gum, 3% molasses, 40% vegetable oil and 10% bacterial suspension. Efficacy test of both formulations was performed at concentrations of 5% and 10% in liquid formulation and 25 g/l and 50 g/l in paste formulation. Liquid formulation at both concentrations inhibited sheath blight disease at 5, 8, and 9 week after planting, whereas paste formulation with concentration of 50 g/l suppressed sheath blight disease at 5, 6, and 8 week after planting. However, paste formulation at concentration of 25 g/l only suppressed sheath blight disease at 8 WAP. Application of liquid and paste formulations did not affect plant height, number of tillers, and number of panicles of rice plants. Furthermore, application of liquid formulation at concentrations of 5% and 10% increased the fresh weight as much as 22% and 35%, respectively, and dry weight of grain as much as 26% and 32%, respectively. Otherwise, application of paste formulation at a concentration of 50 g/l increased the fresh weight of grain
Keyword: Rhizoctonia solani., paste formulation, liquid formulation, biological control agents, Ralstonia pickettii

Judul: Pengembangan Formulasi Ralstonia pickettii sebagai Biopestisida
Abstrak: Agens pengendali hayati merupakan salah satu alternatif yang digunakan dalam mengendalikan patogen tanaman. Salah satu agens hayati yang baru dikembangkan yaitu R. pickettii. Sifat antagonis yang dimiliki oleh bakteri R. pickettii mampu mengendalikan penyakit hawar pelepah pada padi sehingga dikembangkan sebagai bahan aktif dalam formulasi biopestisida. Penelitian ini bertujuan mengembangkan formulasi R. pickettii sebagai biopestisida. Berdasarkan analisis in vitro menunjukkan bahwa R. pickettii mampu menghambat patogen: Rhizoctonia solani, Pyricularia oryzae, Helminthosporium oryzae, dan Xanthomonas oryzae pv. oryzae. Pembuatan formulasi pasta R. pickettii yang telah dimodifikasi menggunakan bahan pembawa tanah andosol, penambahan elisitor, dan pupuk daun. Formulasi diujikan pada beberapa parameter pengujian yaitu uji fitotoksik, uji viabilitas, dan uji keefektifan formulasi. Uji fitotoksisitas menunjukkan bahwa formulasi yang diujikan tidak bersifat toksik terhadap bibit padi. Berdasarkan uji viabilitas menunjukkan bahwa formulasi F2 (40% minyak sayur, 24% talek, 22% tanah, 3% molase, 1% CMC, 10% R. pickettii [109 cfu/ml]) menunjukkan konsistensi tinggi seperti yang ditunjukkan oleh populasi R. pickettii pada minggu keenam setelah penyimpanan yaitu ± 106 cfu/g. Uji keefektifan formulasi menunjukkan bahwa formulasi Fd, F1, dan F2 mampu menghambat pertumbuhan P. oryzae masing-masing sebesar 96.9%, 97.2%, dan 97.6%.
Keyword: andosol, bahan pembawa, formulasi pasta, penyakit padi.

Judul: Visualisasi Data Spasial di MonetDB Menggunakan OpenJUMP
Abstrak: There are currently the most used data model is relational. This model a database as a set of data that is stored in structural rows and columns. Besides the popullarity, the relational data model is lacking in the implementation of data is large and has a high complexity. Different of the relational model, MonetDB using a column store method to divide the data into sections binary tables . This method is only necessary to call the data, resulting method is faster. Anggi ( 2013 ) have tested the MonetDB as a spatial and non spatial data storage, but not yet visualize data from MonetDB. This result successfully visualize spatial data using a simple query “like” and “and” the average time 1 second , while the complex spatial queries using “joins”, “constains” and “Intersect” with average time more than 20 minutes. So it looks that MonetDB also can process spatial data simple and complex . But in the process of complex spatial data visualization that require run-time is relatively long .
Keyword: visualize, OpenJUMP, MonetDB, spatial data