Judul: Bioaktivitas insektisida Nabati Bintaro (Cerbera Odollam Gaertn.) sebagai pengendali hama Pteroma plagiophleps Hampson dan Spodoptera litura F.
Abstrak: Salah satu permasalahan yang sering dihadapi dalam pembangunan tanaman adalah serangan hama. Serangan hama yang terjadi secara terus menerus dan berat dapat menghambat pertumbuhan tanaman bahkan mengakibatkan kematian tanaman sehingga menyebabkan kegagalan pembangunan hutan tanaman. Pada umunya masyarakat menggunakan pestisida kimia/sintetik dalam mengendalikan hama.
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Judul: Critical Review of the Potential of Preparations of Twenty Species from Fourteen Plant Families for Crop Pest Control
Abstrak: Serangan hama tanaman dapat menimbulkan kerugian yang besar sehingga harus dikelola dengan tindakan pengendalian yang tepat. Insektisida sintetik yang sering digunakan petani dapat menyebabkan berbagai dampak samping yang tidak diinginkan terhadap organisme bukan sasaran dan lingkungan. Insektisida nabati merupakan salah satu alternatif potensial untuk mengendalikan hama tanaman. Kajian pustaka ini dilakukan untuk meninjau secara kritis publikasi tentang sediaan insektisida nabati 20 spesies dari 14 famili tumbuhan. Potensi insektisida nabati tersebut dievaluasi berdasarkan ketersediaan, kefektifan, keamanan terhadap organisme bukan sasaran, dan potensi pemasyarakatannya. Publikasi daring yang relevan ditelusuri melalui database seperti Google, Google Scholar, ResearchGate, PubMed, National Center for Biotechnology Information, dan IPB Repository, dengan tambahan beberapa materi cetak. Hasil analisis literatur menunjukkan bahwa di antara 20 spesies tumbuhan yang dievaluasi, hanya 5 spesies yang ekstrak pelarut organik atau minyak atsirinya memiliki bioaktivitas yang sangat kuat terhadap setidaknya satu spesies hama sasaran. Kelima spesies tumbuhan tersebut ialah Allium sativum, Azadirachta indica, Curcuma longa, Dysphania ambrosioides, dan Nicotiana tabacum. Sementara itu, sediaan A. sativum, A. indica, dan Citrus limon yang disiapkan dengan pelarut air juga memiliki aktivitas insektisida yang sangat kuat. Data yang tersedia menunjukkan bahwa insektisida nabati aman bagi organisme bukan sasaran dan lingkungan. Peningkatan penggunaan insektisida nabati dapat dilakukan dengan mengintegrasikan pemasyarakatan dan penggunaannya dalam program pengelolaan hama yang relevan., Crop losses due to pest organisms can be substantial and should be managed by applying proper crop protection measures. Synthetic insecticides commonly used by farmers could cause some undesirable side effects to non-target organisms and the environment. Botanical insecticides represent one of the potential alternatives to synthetic insecticides for controlling crop pests. This literature study was carried out to critically review published papers on botanical insecticide preparations of 20 species from 14 plant families. Those botanical insecticides were assessed based on their availability, effectiveness, safety to non-target organisms, and dissemination potential. Relevant publications were searched through online databases such as Google, Google Scholar, ResearchGate, PubMed, National Center for Biotechnology Information, and IPB Repository, in addition to some printed materials. Results of the literature analysis showed that among the 20 plant species studied, only 5 species yielded organic solvent extracts or essential oils that had very strong insecticidal activity against at least one target pest species. Those five plant species were Allium sativum, Azadirachta indica, Curcuma longa, Dysphania ambrosioides, and Nicotiana tabacum. Meanwhile, A. sativum, A. indica, and Citrus limon aqueous extracts also had very strong insecticidal activity. Available data indicated that botanical insecticides are safe to nontarget organisms and the environment. Enhancement of use of botanical insecticides can be done by integrating their dissemination and use in relevant pest management programs.
Keyword: botanical insecticides, crop pests, feasibility, safety, dissemination

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.