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"Indonesian language Texts"
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Critical Thinking Tasks Manifested in Indonesian Language Textbooks for Senior Secondary Students
This study aims to investigate to what extent critical thinking is manifested in the Indonesian language textbooks used by senior high school students in Indonesia. Content analysis has been adopted to determine whether the tasks in the textbooks encourage and promote students’ critical thinking skills. Ilyas’s critical thinking framework, which was the result of evaluating, examining, and synthesizing 21 critical thinking programs, strategies, tests, and taxonomies, was used for the analytic categories. The findings showed that the textbooks did not contain many tasks promoting critical thinking. Besides this, tasks potentially encouraging students’ critical thinking were not varied. The findings suggest that as critical thinking has been included as one of the education objectives in this country, textbook writers need to create more tasks promoting critical thinking; moreover, Indonesian language teachers need to be taught how to modify tasks that can promote critical thinking skills among students.
Journal Article
The analysis of Ecoliteracy elements in language textbooks
This study reports the analysis of ecoliteracy aspects in four Indonesian language textbooks. The textbooks are based on different curriculum and from different publishers. In analyzing the ecoliteracy aspects, we used the ecological literacy framework proposed by Roth (
1992
)-Ecoliteracy Content Analysis Frame (ECAF). This framework consists of four elements, namely: (1) knowledge, (2) effective tendency, (3) behavior, and (4) cognitive skills. The results show that the aspect of ecoliteracy, which has the least number, is cognitive skills, while the one which has the highest number of frequencies is the knowledge aspect. Affective tendency and cognitive skill elements are missing in two books. Furthermore, the ecoliteracy aspect of each book is different in frequency. It shows that the textbooks need to be revised to meet the purpose of ecoliteracy. Teachers may use it as a consideration in selecting the textbooks for the teaching-learning process.
Journal Article
Easy Indonesian : learn to speak Indonesian quickly!
Indonesia is the hot new Asian destination, a country where lively conversation, good humor and warm human interaction are integral parts of daily life. Easy Indonesian brings the language and culture of Indonesia alive, giving you all the basics you need to start speaking Indonesian immediately. The opening chapters introduce the essentials of pronunciation and sentence construction, with each subsequent chapter moving you forward in gradually building up your knowledge for specific situations and activities, such as making new acquaintances, going shopping, traveling around, and much more. Every chapter contains helpful information on the customs and culture of this warm and friendly country, so that you'll soon be able to talk about everything from the weather to your job and home and family with ease. An extensive glossary at the back and a section on verb and noun forms are included to help you build your vocabulary and understand the different forms that Indonesian words take. The accompanying audio CD gives you a solid foundation in correct pronunciation and helps you get a feel for the spoken language, so you'll be able to understand what people are saying-and participate in conversations!.
eBook
Enhancing text classification performance by preprocessing misspelled words in Indonesian language
[...]our research aims to implement a method for processing misspelled words in Indonesian language using the Levenshtein distance algorithm, which has been proven to search for similar words [23] in the text preprocessing phase. - [...]two classification models using Naïve Bayes algorithm are built, one of them utilizes the method for preprocessing misspelled words and the other does not. - [...]we test the both ability of Levenshtein distance in processing misspelled words in the dataset and the ability of the models to classify texts from the test set. [...]we collected a portion of the feedback data from previous work which can be found in [11] for training and testing the classification model.
Journal Article