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All Journal IJID (International Journal on Informatics for Development) English Education and Applied Linguistics Journal (EEAL Journal)
Lusiana Lestari
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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GRAMMATICAL ERROR ANALYSIS IN EFL SPEAKING PERFORMANCE Widi Lestari; Setia Muljanto; Lusiana Lestari
English Education and Applied Linguistics Journal (EEAL Journal) Vol 3, No 2 (2020): EEAL Journal
Publisher : Institut Pendidikan Indonesia Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/eealjournal.v3i2.1840

Abstract

This study was carried out to analyze the grammatical errror analysis which are made by the students in their speaking performance. It was based on the assumption that grammatical rule has a function in speaking to make the communication clearly and to convey the information in appropriate way. The study was conducted under qualitative methodology by using case study as the appropriate research design. The data source of this research is 1st grade student from the speaking class of English Education major that consisted of 24 students. For collecting the data, the researcher used observation fieldnote and also video record. The writer identified and analyzed data based on the types of error using Dulay’s theory. Based on the finding, the result of this research showed that students made a total errors 318 times divided into four types of errors: omission, addition, misformation, and misordering. From the quantity of each error types, misformation was the highest errors produced by the students. It took 175 times of errors. And omission had 115 times of errors, moreover, 24 errors fell into error of addition, and the last one misordering was the lowest errors produced by the students the total was 5 errors. Actually, errors are necessary in learning a language, especially learning English language as the foreign language. Thus, error analysis also helps the students identify what the errors are made, because the students cannot apply their language acquisition directly without committing error firstly. They cannot achieve the target language perfectly when the errors appear. Error analysis is very advantageous for both learner and teacher. For learner, by paying more attention, the learners are expected to increase their knowledge on the English grammar. Whereas for teachers, hopefully the research can be useful information in teaching process and in the end, the teacher can be able to teach the material appropriately
Veil and Hijab: Twitter Sentiment Analysis Perspective Lusiana Lestari; M Didik R Wahyudi; Usfita Kiftiyani
IJID (International Journal on Informatics for Development) Vol. 9 No. 1 (2020): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09108

Abstract

Controversies about veil and hijab are often occur in society. Especially in today’s digital era, public opinion expressed through social media can greatly influence the others opinions, regardless of whether it is positive or negative. Therefore, this research was aiming to conduct an approach through analysis sentiment of public opinion about the veil and hijab to know how much accurate the sentiment analysis predict the positive, negative, or other sentiments with using Twitter data as the research object. The algorithm used in this study is Support Vector Machine (SVM) because of its fairly good classification model though it trained using small set of data. The SVM on this research was combined with Radial Base Function (RBF) kernel because of its numerical difficulties that are fewer than linear and polynomial kernel and also because this research doesn’t have a large feature.  The amount of data used is 3556 tweets data. Tweets data, which is numbered 1056, is classified manually for the learning process. The remaining 2500 data will be classified automatically with the classifier model that has been created. A total of 1056 tweets data that have been classified manually is separated into training and testing data with a ratio of 8: 2. The result of the sentiment analysis process using Support Vector Machine algorithm RBF kernel with C=1 and γ=1  has an accuracy score of 73.6% with precision to negative opinions are 62%, positive opinions are 83%, neutral opinions reach 53% and irrelevant opinions that talk about hijab and veil reach 98%. It shows that sentiment analysis can be used for predicting the negative, positive or other sentiments of a sentence based on a certain topic, in this case veil and hijab.
Co-Authors M Didik R Wahyudi Setia Muljanto Usfita Kiftiyani Widi Lestari