Judul: Herbal Compound Screening with GPU Computation on ZINC Database through Sequence Alignment Approach.
Abstrak: Covid-19 adalah sebuah pandemi yang membuat banyak peneliti berlomba mencari solusi untuk kemaslahatan sesama, terutama dalam segi kesehatan, pengobatan, dan penanggulangan penyakit tersebut. Penapisan awal dengan proses in-silico berperan penting dalam mengurangi jumlah bahan kimia yang perlu diuji untuk menemukan suatu obat. Penelitian ini bertujuan untuk mencari kandidat senyawa obat terbaik yang ada dalam basis data ZINC sehingga bisa diteliti lebih lanjut secara in-vitro. Sebanyak 997.402.117 senyawa kimia dicari kemiripannya dengan beberapa senyawa obat yang sudah teruji untuk melawan virus corona. Komputasi sekuensial akan memakan banyak waktu beberapa bulan untuk menyelesaikannya. Pendekatan general programming graphic processing unit digunakan melalui implementasi algoritme pembandingan similaritas secara paralel sehingga dapat mempercepat proses pencarian. Hasil dari penelitian ini menunjukkan bahwa implementasi parallel algoritme ini dapat mempercepat proses komputasi hingga 55 kali, serta beberapa kandidat senyawa herbal yang dapat ditemukan pada bahan-bahan alam, memiliki nilai similaritas yang tinggi dengan obat coronavirus., Covid-19 is a global pandemic that drives many researcher strive to look for its solution, especially in the field of health, medicine, and total countermeasures. Early screening with in-silico processes is crucial to minimize the search space of the potential drugs to cure a disease. This research aims to find potential drugs of covid-19 disease in ZINC database to be further investigated through in-vitro method. About 997.402.117 chemical compounds are searched about its similarity to some of confirmed drugs to combat coronavirus. Sequential computation would take months to accomplish this task. General programming graphic processing unit approach is used to implement similarity comparison algorithm in parallel, in order to speed up the process. The result of this study shows the parallel algorithm implementation can speed-up the computation process up to 55 times faster, and also that some of the chemical compounds have high similarity score and can be found in nature.
Keyword: covid-19, pembandingan similaritas, pemrograman GPU, pemrograman paralel, covid-19, GPU programming, parallel programming, similarity comparison

Judul: Herbal Compound Screening for COVID-19 using Multi-Label Learning Method with Deep Neural Network
Abstrak: Wabah COVID-19 yang disebabkan oleh SARS-CoV-2 telah berlangsung selama satu tahun dan belum terlihat akan usai dalam waktu dekat. Tumbuhan herbal memiliki potensi sebagai pengobatan alternatif pada COVID-19. Dari interaksi yang dapat terjadi antara protein COVID-19 dengan beberapa senyawa, permasalahan ini dapat diformulasikan sebagai multi-label problem. Deep neural network merupakan salah satu algoritme yang dapat mengolah multi-label problem. Penelitian ini menggunakan data interaksi senyawa dengan protein signifikan SARS-CoV-2 yang akan dimasukkan ke dalam model deep neural network. Dari model tersebut, dilakukan prediksi kandidat senyawa herbal yang berinteraksi dengan protein SARS-CoV-2. Terdapat beberapa kandidat senyawa herbal hasil prediksi, ialah caffeine pada kopi, L-Theanine pada teh, coclaurine pada custard apple, carpaine pada papaya, cucurbitine pada labu, dan theobromine pada kokoa atau coklat., The COVID-19 pandemic caused by SARS-CoV-2 has been going on for a year and it doesn't look like it's over any time soon. Herbal plants have potential as an alternative treatment for COVID-19. From the interactions that can occur between the COVID-19 protein with several compounds, this problem can be formulated as a multi-label problem. Deep neural network is an algorithm that can process multi-label problems. This study uses data on interactions compound with significant SARS-CoV-2 proteins that will be included in the deep neural network model. From the model, prediction of interaction between herbal compound candidate with SARS-CoV-2 protein was made. There is some candidate from the prediction, the result is caffeine in coffee, L-Theanine in tea, coclaurine in custard apple, carpaine in papaya, cucurbitine in pumpkin, and theobromine in cocoa or chocolate.
Keyword: covid-19, deep neural network, multi-label learning, senyawa herbal

Judul: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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