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
Keyword: 

Judul: Deep Learning Optimation for Drug-Target Interaction with Graphic Processing Unit
Abstrak: Data drug-target interaction (DTI) yang semakin meningkat secara eksponensial menimbulkan permasalahan baru, yakni lamanya waktu komputasi. Besarnya data DTI tersebut menyebabkan lamanya waktu pelatihan model prediksi DTI untuk menentukan senyawa-senyawa potensial berkhasiat obat. Lamanya pengolahan data ini dapat berdampak pada waktu penelitian drug discovery yang lebih lama juga, karena tahap in silico biasa dilakukan di awal rangkaian penelitian, sehingga keterlambatan tahap ini membuat tahapan lainnya mengalami penundaan. Penelitian ini bertujuan untuk melihat seberapa baik performa GPU saat digunakan untuk permasalahan deep learning dalam rangka melakukan prediksi drug-target interaction. Data penelitian menggunakan lima dataset gold standard yakni enzyme, ion receptor, G-protein coupled receptor, dan nuclear receptor, serta dataset COVID-19. Metode yang digunakan adalah utilisasi algoritme deep learning secara paralel menggunakan GPU. Model yang dibangkitkan terdiri dari dua jenis, yakni yang menggunakan Central Processing Unit (CPU), dan menggunakan GPU. Kedua jenis model ini dibuat agar performa GPU dalam mengolah data dapat dibandingkan secara langsung dengan pengolahan data tanpa GPU. Setelah model dibuat, dilakukan hyperparameter tuning untuk menentukan hyperparameter yang optimal bagi masing-masing model. Performa model dihitung menggunakan parameter akurasi, recall, precision, F-measure, dan speed-up. Hasil dari penelitian ini menunjukkan bahwa penggunaan GPU dalam model deep learning dapat mempercepat proses pelatihan hingga 100 kali lipat. Selain itu, proses hyperparameter tuning juga sangat terbantu dengan adanya GPU karena dapat mempersingkat proses dari hingga 55 kali lebih cepat. Performa model deep learning yang dihasilkan dengan model CPU pun tidak jauh berbeda bila dibandingkan dengan model GPU dilihat dari nilai akurasi, recall, precision, dan F-measure yang tidak jauh berbeda. Saat dicobakan dengan dataset COVID-19, model menunjukkan performa yang baik dengan akurasi 76%, F-measure 74%, dan nilai speed-up 179. Data tersebut menunjukkan bahwa potensi GPU sangat besar apabila diintegrasikan dengan model deep learning. Penerapan lebih lanjut dapat digunakan dalam komputer server dan high performance computer untuk mengolah data bioinformatika yang lebih besar dan lebih kompleks., The exponentially increasing drug-target interaction (DTI) data raised a new problem, namely the length of computation time. The amount of DTI data that needs to be processed caused longer training time needed to do DTI prediction on potential drug compounds. The length of this computation time can have an impact on a longer drug discovery research time as well, because the in silico stage is usually carried out at the beginning of the research series, so the delay in this stage will cause other stages to be delayed. This study aims to see how well the GPU performs when used for deep learning problems in order to predict drug-target interactions. The research data uses five gold standard dataset which is enzyme, ion channel, G-protein coupled receptor, and nuclear receptor, as well as COVID-19 dataset. The method used is the utilization of deep learning algorithms in parallel using the GPU. The generated model consists of two types, namely those that use the Central Processing Unit (CPU), and those that use GPU. These two types of models are designed so that GPU performance in processing data can be compared directly with data processing without a GPU. After the model is created, hyperparameter tuning is performed to
Keyword: bioinformatika, deep learning, drug-target interaction, GPU, HPC

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).
Keyword: