Judul: Ekstraksi Informasi Interaksi Senyawa- Protein pada Kumpulan Dokumen Abstrak.
Abstrak: Farmakologi adalah studi yang berkaitan dengan obat-obatan terutama untuk mempelajari interaksi antara senyawa dan protein. Perkembangan studi farmakologi telah menghasilkan basis data interaksi senyawa-protein. Masalah saat ini adalah tidak semua interaksi senyawa-protein sepenuhnya tersedia di basis data. Informasi interaksi senyawa-protein sebagian besar tersebar di dokumen penelitian atau literatur. Pertumbuhan dokumen literatur mempengaruhi kesulitan bagi pengguna terutama untuk mendapatkan informasi interaksi senyawa-protein dengan cepat. Untuk menjawab tantangan ini, penelitian ini berupaya untuk mendapatkan informasi interaksi senyawa-protein menggunakan penambangan teks dari berbagai literatur penelitian yang berkaitan dengan farmakologi. Penelitian ini menggunakan machine learning dan pendekatan deep learning untuk mengklasifikasikan teks yang memiliki informasi interaksi senyawa-protein. Pembangunan sistem yang dapat mengekstrak informasi interaksi senyawa-protein ini juga berupaya untuk menemukan akurasi terbaik dari beberapa metode seperti (1) Naive Bayes, (2) Multinomial Naive Bayes, (3) Logistic Regression, (4) Convolutional Neural Network, (5) Recurrent Neural Network, dan (6) Hierarchical Attention Network. Metode-metode tersebut bertujuan untuk melakukan klasifikasi teks pada kelompok kalimat yang mengandung entitas senyawa dan protein. Evaluasi algoritme prediksi ini telah mencapai hasil akurasi yang baik. CNN mencapai hasil terbaik dengan akurasi sebesar 85.2%. RNN mencapai akurasi 83.6%, HAN memberikan akurasi 81.8%, Regresi Logistik memberikan nilai akurasi sebesar 80.1%, Naive Bayes mencapai akurasi sebesar 73.1%, dan yang terkecil dihasilkan dari Multinomial Naive Bayes dengan nilai akurasi sebesar 70,3%.
Keyword: deep learning, ekstraksi informasi, interaksi senyawa protein, machine learning, teks klasifikasi

Judul: Information Extraction of Compound-Protein Interaction in A Collection of Abstract Documents Contains Compound Sentences
Abstrak: Drug Target Interactions (DTI) adalah proses penting dalam penemuan obat di mana bertujuan untuk mengidentifikasi senyawa yang berguna bagi pengobatan atau penyembuhan penyakit. Informasi terkait penelitian Drug Target Interactions sebagian besar terdapat dalam public domain database dan literatur atau paper peneliti. Informasi DTI di public domain database masih terbatas disebabkan tidak banyak peneliti menginputkan data hasil penelitiannya ke dalam public domain database. Untuk mendapatkan informasi DTI tersebut diperlukan cara lain, yaitu dengan melakukan ekstraksi informasi dari literatur atau paper untuk mengambil informasi-informasi terkait penelitian drug target interactions. Informasi pada dokumen abstrak paper peneliti banyak yang mengandung kalimat majemuk. Regular expression dapat digunakan untuk mengidentifikasi kalimat majemuk dalam dokumen abstrak. Penelitian ini menggunakan regular expression untuk identifikasi kalimat majemuk dan menggunakan pendekatan machine learning untuk mengklasifikasikan teks yang memiliki informasi interaksi senyawa-protein. Penelitian ini menggunakan kumpulan dokumen abstrak dari penelitian Muztahid (2019), di mana terdapat 3,000 dokumen abstrak yang diurai menjadi 29,363 kalimat. Kalimat yang telah diurai selanjutkan dilakukan tahapan Named Entity Recognition(NER). Dengan menerapkan NER, dari total 29,363 kalimat, terdeteksi 7,653 kalimat yg mengandung senyawa dan atau protein. Pada tahapan selanjutnya, dengan regular expression menggunakan pattern matching dihasilkan 17,451 kalimat tunggal yang akan diproses ke tahapan text preproccessing. Kalimat yang diperoleh dari tahapan text preproccessing dibagi menjadi data latih dan data uji dengan menggunakan 10-fold cross validation. Proses identifikasi interaksi antara senyawa dan protein dari abstrak yang mengandung kalimat majemuk dilakukan memggunakan Bernoulli Naive Bayes. Hasil evaluasi menunjukkan akurasi rata-rata sebesar 76.44%., Drug Target Interactions (DTI) is an important process in drug discovery where it aims to identify compounds useful for the treatment or cure of diseases. Information related to Drug Target Interactions research is mostly contained in public domain databases and literature or research papers. DTI information in public domain databases is still limited because not many researchers input data from their research into the public domain database. To get the DTI information is needed another way, by extracting information from literature or paper to retrieve information related to drug target interactions research. The information on the abstract paper documents of many researchers contain compound sentences. Regular expression can be used to identify compound sentences in abstract documents. This study used regular expression for compound sentence identification and uses machine learning approach to classify text that has compound-protein interaction information. This study used a collection of abstract documents from Muztahid (2019), there are 3,000 abstract documents has been parsed into 29,363 sentences. The sentence that has been parsed is further carried out the Named Entity Recognition (NER) stage. By applying NER, out of a total of 29,363 sentences, 7,653 sentences were detected containing compounds and or proteins. In the next stage, with regular expression using pattern matching produced 17,451 single sentences that will be processed to the text preprocessing stage. Sentences obtained from the text preprocessing stage are divided into training data and test data using 10-fold cross validation. The process of identifying interactions between compounds and proteins from abstracts containing compound sentences was carried out using Bernoulli Naive Bayes. The evaluation showed an average accuracy of 76.44%.
Keyword: drug target interactions, public domain database, regular expression, information extraction, text mining, compound sentences

Judul: Thermal Characteristics and Dynamics of Tropical Cyclone over Western North Pacific Ocean: A Case Study of Typhoon Songda (Chedeng) 2011
Abstrak: The frequent occasion of Tropical Cyclone (TC) over Western North Pacific (WNP) Ocean lies from 10˚N to 26˚N and 121˚E to 170˚E. Sources of TC development come from warm ocean temperature where latent heat is released during condensation processes and subsequently concentrated in the boundary layer. Relative humidity at 850-700 hPa is responsible to produce the most intense storm. Meanwhile, weak vertical wind shear between 850-200 hPa is one of the dynamical factors that lead to the development of TC. Pressure level of 850-200 hPa is chosen to minimize the thermodynamic effect in the lower troposphere. Typhoon Songda (Chedeng in the Philippines) or TSC lasted from May 18th to 30th, 2011 and strengthened into powerful Super Typhoon stage between May 24th and 27th. Long curving track and the exhibition of all sequences of TC life cycle, TSC is then considered as an ideal case of TC. The objective of the study with case of TSC is to investigate the role of thermal characteristics and dynamics on TC intensity. The research is carried out by using Advanced Research of Weather Research and Forecasting model (WRF ARW model) version 3.3. Two domains are employed in the model. The first domain situate from 02˚N to 40˚N and 118˚E to 149˚E and cover spatial resolution () of 33.1 km. The second domain situate from 09˚N to 27˚N and 119˚E to 132˚E with of 11 km. Timestep of the model () is 120 seconds. The model is supported by the following physical parameterization, i.e., Yonsei University Scheme (YSU) in the boundary layer, Kain-Fritsch scheme (KF) in the cumulus, and WRF Single-Moment 3-class scheme in the microphysics. Initial meteorological condition prior to the formation of the cyclone is generated using WRF ARW standard-release model. Sensitivity of the model output is obtained from a selected single-parameter change scenarios, namely, 10 % to 20 % decrease and increase of wind shear initial data at 850-200 hPa, and by 10 % to 20 % decrease of relative humidity initial data at 850-700 hPa. The result showed that the negative correlations between wind shear and intensity are found in wind shear scenarios. Wind shears of 10 ms-1 which lead to strengthening of the wind intensity in the early period of Typhoon development are found in wind shear scenarios. The effect of wind shear changes on wind intensity is statistically significant in the 20 % wind shear scenarios. During mature stage, weak wind intensity occurred when wind shear increase as given in the WRF standard-release and the 20 % decreasing scenario. Different situation is found in the 20 % increasing scenario where wind shear tend to weak and lead to strong wind intensity at this stage. Meanwhile, the reduction of moisture supply at 850-700 hPa resulting from 20 % decreasing relative humidity play a major role in the weakening of wind intensity and reduces mechanical energy of the cycle process as much as 300 J kg-1.
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