A Study on the Effectiveness of Crossword Puzzle as Compared to Conventional Method in Teaching Vocabulary

This study is entitled “The Effectiveness of Crossword Puzzle as Compared TheConventional Method in Teaching Vocabulary.”The research was administered to answer the question “Is crossword puzzle ascompared to other methods effective in improving student vocabulary achievement?” The datawere collected through pretest and posttest, administered to 35 student and the data wasanalyzed by using T-test.The participants of this study were the grade five elementary pupils of kartika elementaryschool. in section 5A there were 34 pupils who were taught using conventional method andsection 5B there were 35 pupils who were taught using crossword puzzle method. The age ofthe participants were between 11-15 years old.From the computation of the pretest and posttest, the result mean of pretest controlgroup=51.17, the result mean of pretest experimental group=55.14. and the mean posttestcontrol group= 56.61,the mean posttest experiment group= 63.42. The result shows that t counted=2.106>t table = 1,684. There is significant different crossword puzzle method effective toimprove vocabulary skills.The researcher hopes that the finding of present crossword puzzle method could give somecontribution to the improvement vocabulary especially for the students of Kartika X-3,Parompong, Bandung

Pengembangan Indeks Bahaya Kebakaran di Hti Sbawi Sumatra Selatan

Sistem peringkat bahaya kebakaran sangat penting pada kondisi pengelolaan hutan tanaman industri untuk mendeteksi potensi kebakaran. Keetch-Byram Drought Index (KBDI) merupakan satuan indeks kebakaran yang dapat digunakan untuk menilai bahaya kebakaran hutan secara dini karena hanya memerlukan data curah hujan dan suhu udara maksimum harian saja. Makalah ini akan mengurai penggunaan KBDI untuk deteksi dini bahaya kebakaran hutan di SBA Wood Industries. Penelitian dilakukan di Ogan Komering Ilir, Sumatera Selatan dengan melakukan perbaikan terhadap nilai parameter KBDI agar sesuai dengan kondisi setempat. Metode yang digunakan adalah: (i) pengamatan terhadap curah hujan, suhu udara, dan kedalaman muka air tanah untuk periode 1 April 2009 sampai 11 Mei 2010 digunakan untuk penghitungan model KBDI pada lahan basah, (ii) proses optimisasi untuk memperoleh nilai parameter baru dalam perhitungan faktor kekeringan dan faktor muka air tanah. KBDI yang dikembangkan memiliki kinerja yang baik dalam mendeteksi bahaya kebakaranhutanyaitukejadian kebakaran hanya terjadi pada level ekstrim. Hasil penelitian menunjukkan bahwa kedalaman muka air tanah kritis untuk mempertahankan KBDI pada level yang aman yaitu pada kedalaman 0,659m, dan jika kedalaman melebihi nilai kritis tersebut maka potensi bahaya kebakaran di lokasi SBAWI akan meningkat

The Effect of Tourism Policy Implementation to Local Revenue

Tourism contribution has a promising prospect, but it is different from the West Bandung Regency which has 11 tourist attractions but 3 which are managed by the local government, namely ghua pawon, malela waterfall and ciburuy situ. This requires a study to be explored both in terms of Tourism policy implementation as well as implications for local revenue. This study uses qualitative methods of data collection carried out in natural settings, primary data sources, and more data collection techniques in participant observation, in-depth interviews. this study resulted that the original income of the speckle region reached its target because in terms of decision-making it was still dominated by political decisions and unclear ownership of land around the tourist destination, it required coordination and cooperation from the parties to resolve problems in the West Bandung Regency

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov’SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found