Judul: Graphics Card Utilization as a Computing Tool in Information Retrieval Vector Space Model.
Abstrak: In recent years, the ability of the graphics card or GPU increase dramatically. This condition allows the GPU to do other than computing or graphics processing is known as the General Purpose GPU (GPGPU). With hundreds to thousands of processors inside GPU, hardware is expected to improve the performance of most applications. Unluckly, the price to be able to implement GPU GPGPU could not be reached by the majority of the community. This research tried to emulate GPGPU on GPU with Shader Model 2.0a support which is a standard GPU. Vector Space Model is used as a case study because it is easy to implement. Information retrieval computation done in parallel by the CPU and GPU. Process in the GPU is divided into three, namely: multiplication, addition, and sorting. Odd-even transition sort algorithms used to sort the most relevant documents. The results showed that the emulation GPGPU on GPU with Shader Model 2.0a support able to retrieve the same results with the results retrieved by the CPU. However, this emulation can not produce the expected performance, even the GPU requires more memory than the CPU. Keyword: GPGPU, GPU Computing, Vector Space Model, Odd-Even Transition Sort
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Judul: Dynamic data mining for highly intercorrelated feature selection with Graphic Processing Unit Computing (GPU)
Abstrak: Feature selection with correlated features in the real world should be performed on dynamic data. This is due to insertion, deletion and updating transactions occur continuously on the database. This transaction makes the dimension of data becomes very large in number of records and number of features. The question is how we do feature selection on very large data and is constantly changing. This problem should be solved and one common method is to perform the sampling on database. In this research, random sampling method is used to extract the overall synopsis of the data in the database. The reservoir algorithm used to initialize the sample. Type of sample is backing sample composed of identities, priorities and time stamp. When there is a change in the database, the old data in the sample with the lowest priority will be replaced with a new one. Sample as a representation of the database has maintained the same class distribution using the kullback leibler divergence. All the techniques and algorithms are incorporated and implemented in the SQL language using MySQL 5.5. As a result, the sampling process of taking a small amount of new data as a representation of the database with the same class distribution database. Sampling process reduces the dimension of the required number of records. We need other methods to speed up the feature selection on very large number of features. In this research, parallel GPU computing is used to speed up feature selection that have large dimensions. Feature selection algorithm is divided into two sub problems. This problem is discreetization and computational geometry x- monotone. Parallel algorithm is applied to each sub problem as long as no recursiveness and dependencies. The algorithm is written into the program with the CUDA C language. The program is divided into two, which are discreetization kernel function and x-monotone kernel function. Tests performed on a data set with three distributions, which are balanced, negatively skewed and positively skewed. Average acceleration doing with parallel for one pair feature is 1.5 times to 1.87 times for discreetization and x-monotone rather than sequential. Average acceleration parallel for 45, 190, 435, 780, 1225, 1770, 2415, 3160, 4005, 4950 number of features pairs is 8.2 times faster than sequential. Accuracy was measured for each kernel function in CUDA programs. Discreetization kernel has accuracy of 81.76 % and an x-monotone kernel has accuracy of 85 %. Feature selection for 4950 pairs with a balanced distribution requires only 0.85 seconds. The same calculation on negative skewed and positive skewed is 1.66 and 1.84 seconds. These facts show the calculation on the data with skewed distribution requires a longer duration than a balanced distribution.
Keyword: random sampling, kullback liebler divergence, general purpose computing on graphics processing unit.

Judul: Insecticidal Activity of Essential Oils Leaves of Cinnamomum spp. (Lauraceae) against Crocidolomia pavonana and Their Phytotoxicity on Broccoli Seedlings
Abstrak: Essential oils from leaves of 13 species of Cinnamomum were tested for their insecticidal activity on the cabbage head caterpillar Crocidolomia pavonana with leaf immerse method. The results showed that the treatment with 1% (w/v) essential oils leaves of C. celebicum, C. multiflorum, C. sintoc, and C. verum caused 21.45, 93.65, 60.30, and 76.10% mortality, respectively. Based on the results of phytotoxicity and insecticidal activity tests, C. multiflorum oil was further tested at 6 concentration levels. In all treatments, larval mortality has occurred at 24 hours after treatment (HAT) and increased at 48 HAT. LC50 of C. multiflorum oil at 24 and 48 HAT was 0.504 and 0.396%, respectively. The treatment with C. multiflorum oil at ≤0.31% did not inhibit the development of C. pavonana larvae. Determination of the chemical constituents of Cinnamomum oils were carried out by gas chromatography-mass spectrometry (GCMS). Totally, 345 compounds were identified. Methyleugenol constituted the main component (49.4%) of leaf essential oil of C. multiflorum and was assumed to be responsible for the insecticidal activity of the oil against C. pavonana larvae. LC50 of pure methyleugenol obtained at 0.365% (48 HAT) and 0.363% (72 HAT). The results showed that C. multiflorum was more effective with LC50 value equivalent to methyleugenol content of 0.32% (24 HAT) and 0.24% (48 HAT).
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