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"Effective presidential rhetoric is an important issue. This study investigated the argumentation of President Susilo Bambang Yudoyono's State of Nation Address 2006. The argumentation to investigate encompasses clain, narration, explanation, and evaluation which consists of validity, reliability, and significancy. Content analysis is employed by investigating every word, phrase, and discourse in the text. It is discovered that the address does not belong to the rhetoric of crisis, and it puts more importance on economy than most other issues, and it was prepared to avoid controversy. It was dominated by narration than other elements of argumentation, and it was a lack of evaluation which consists of validity, reliability, and significancy."

"his successful textbook gives students a thorough grounding in the basics of sentence structure and acquaints them with the essentials of syntactic argument. Now updated and expanded, the third edition features an additional chapter on grammatical indeterminacy, a case study on subordinating conjunctions and prepositions, and new exercise."

"This book attempts to consolidate contemporary thinking and research on the role of scientific argumentation in education. Perspectives on scientific argumentation brings together prominent scholars in the field to share the sum of their knowledge about the place of scientific argumentation in teaching and learning. Chapters explore scientific argumentation as a means of addressing and solving problems in conceptual change, reasoning, knowledge-building and the promotion of scientific literacy. Others interrogate topics such as the importance of language, discursive practice, social interactions and culture in the classroom. The material in this book, which features intervention studies, discourse analyses, classroom-based experiments, anthropological observations, and design-based research, will inform theoretical frameworks and changing pedagogical practices as well as encourage new avenues of research."

"ABSTRAKArgumentation mining merupakan bidang penelitian yang berfokus pada kalimat dengan tipe argumentasi. Kalimat argumentasi sering digunakan pada komunikasi sehari-hari serta memiliki peran penting pada setiap proses pengambilan keputusan atau kesimpulan. Tujuan penelitian ini adalah untuk melakukan observasi mengenai pemanfaatan deep learning dengan mekanisme atensi pada anotasi dan analisa kalimat argumentasi.Anotasi argumentasi merupakan pengelompokan komponen argumen dari sebuah wacana ke dalam beberapa kelas. Kelas didefinisikan menjadi 4, yaitu major claim, claim, premise dan non-argumentative. Analisa argumentasi mengarah kepada karakteristik dan validitas argumentasi yang tersusun pada topik tertentu. Salah satu bentuk analisa adalah penilaian apakah argumentasi yang dibentuk sudah terkategori sufficient atau belum. Dataset yang digunakan untuk anotasi dan analisa argumentasi adalah 402 esai persuasif. Dataset ini juga ditranslasikan ke dalam Bahasa Indonesia untuk memberikan gambaran bagaimana model bekerja pada bahasa lain.Beberapa model deep learning, diantaranya CNN Convolutional Neural Network , LSTM Long Short-Term Memory , dan GRU Gated Recurrent Unit digunakan untuk anotasi dan analisa argumentasi sedangkan HAN Hierarchical Attention Network hanya digunakan untuk analisa argumentasi. Mekanisme atensi ditambahkan pada model sebagai pemberi weighted access untuk performa yang lebih baik. Classifier yang digunakan adalah fully connected layer dan XGBoost.Dari eksperimen yang dilakukan, integrasi deep learning dengan mekanisme atensi untuk anotasi dan analisa kalimat memberikan hasil yang lebih baik dari penelitian sebelumnya.

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ABSTRACTArgumentation mining is a research field which focuses on sentences in type of argumentation. Argumentative sentences are often used in daily communication and have important role in each decision or conclusion making process. The research objective is to do observation in deep learning utilization combined with attention mechanism for argument annotation and analysis.Argument annotation is argument component classification from discourse to several classes. Classes include major claim, claim, premise and non-argumentative. Argument analysis points to argumentation characteristics and validity which are arranged in one topic. One of the analysis is how to assess whether an established argument is categorized as sufficient or insufficient. Datased used for argument annotation and analysis is 402 persuasive essays. This data is translated to Bahasa as well to give overview about how does it work with other language.Several deep learning models such as CNN Convolutional Neural Network , LSTM Long Short-Term Memory , and GRU Gated Recurrent Unit are utilized for argument annotation and analysis while HAN Hierarchical Attention Network is utilized only for argument analysis. Attention mechanism is combined with the model as weighted access setter for a better performance. The classifiers are fully connected layer and XGBoost.From the whole experiments, deep learning and attention mechanism integration for argument annotation and analysis arrives in a better result compared with previous research."