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: Screening Herbal Drug Candidates for Finding COVID-19 Anti-Inflammatory Drugs Using Deep Semi-Supervised Learning
Abstrak: COVID-19 merupakan sebuah penyakit yang disebabkan oleh virus SARS-CoV-2. Peradangan akut (hiperinflamasi) merupakan salah satu gejala dari COVID-19. Gejala tersebut dapat memperparah kondisi pasien COVID-19. Penelitian ini membangun model Stacked Autoencoders-Deep Neural Network (SAE-DNN) yang nantinya digunakan untuk memprediksi kandidat senyawa herbal sebagai obat anti inflamasi COVID-19. Selain itu, dilakukan perbandingan performa model berdasarkan representasi data yang berbeda. Alur penelitian dimulai dari pengambilan data, praproses data, pemodelan, dan diakhiri dengan pengujian model pada data uji untuk mendapatkan kandidat senyawa herbal. Penelitian ini menunjukkan bahwa pemodelan menggunakan SAE-DNN dengan representasi senyawa menggunakan fingerprint dan protein menggunakan Dipeptide Composition (DC) menghasilkan performa terbaik dengan akurasi sebesar 0,98857, precision sebesar 0,96722, recall sebesar 0,96419, AUROC sebesar 0,99596, dan F1 Score sebesar 0,96567. Dengan pemodelan tersebut, didapatkan senyawa herbal yang berinteraksi dengan protein target sebanyak 33 senyawa., COVID-19 is a disease caused by the SARS-CoV-2 virus. Hyper inflammation is one of the COVID-19 symptoms that worsen a person’s condition. This research aims to build a Stacked Autoencoders-Deep Neural Network (SAE-DNN) model, which will be used for finding herbal drug candidates as a COVID-19 anti-inflammatory drug. The model performance is compared based on different data representations. The research flow starts from data collection, data preprocessing, modelling, and finally test the model on the herbal data to obtain candidate herbal compounds. This study shows that modelling using SAE-DNN with the representation of compounds using fingerprints and proteins using Dipeptide Composition (DC) produces the best performance with an accuracy of 0,96722, a recall of 0,96419, AUROC of 0,99596, and an F1 score of 0,96567. From this modelling, around 33 herbal compounds are found as candidate drug for anti-inflammation.
Keyword: COVID-19, deep learning, drug repurposing, hyperinflammation, semi-supervised learning

Judul: Sintesis BaTiO3 dari Campuran Ba(OH)2 dan TiO2 dengan Tambahan PbO
Abstrak: Piezoelectric material can generate electric field in response to applied mechanical stress. Conversely, they can stretch and contract in response to an electric field. This material can be used for many applications such as transducer, microphone, ultrasound, and pressure gauge. Barium titanate is a ferroelectric material with piezoelectric properties that can be synthesized easily. The aim of this study is to synthesis barium titanate from mixture of Ba(OH)2 and TiO2, also observe its optimum temperature and the effect of PbO addition. The method of synthesis was solid-state reaction and sol-gel method, then the products were characterized with x-ray diffraction (XRD). Based on XRD analysis, the optimum temperature for both methods was 800°C. Addition of PbO to BaTiO3 in the solid-state reaction formed a compound with formula of Ba1-xPbxTiO3. The addition has changed diffraction pattern peak at 2θ and strengthened its tetragonal structure. The best result was obtained at x of 0.5 which will form a compound of Ba0.5Pb0.5TiO3.
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