Lexical bundles are one of the important characteristics of academic discourse which tell readers to know whether the writer is professional or novice. Inevitably, studies on lexical bundles in scientific essays are important to do. This study identifies the most frequent, structural characteristics, and the functional categorization of lexical bundles in the Master Theses in Teaching English as a Foreign Language (TEFL), specifically in the Findings and Discussion section. There were 651.083 words from 74 different theses compiled to create the corpus by using Antconc 3.5.8. The results found 117 different lexical bundles and the sequences ‘the result of the’ and ‘on the other hand’ dominate the section. Noun phrase + of structure which covers one third of overall forms in the corpus were the most lexical bundles’ structural types in the findings and discussion section followed by other noun phrase structures (22% out of overall bundles). Functionally, research-oriented bundles (45% of overall bundles) were the most frequent ones followed by text-oriented (40%) and the least frequent bundles were participant-oriented. Reported findings are further discussed with related theories.

This research aims to investigate the types and factors of code mixing which found in the official twitter account of BKN (Indonesia National Civil Service Agency) during the registration of civil servant’s candidate in November 2019. Qualitative approach was conducted to analyze the types and social factors of code mixing. Observation method with writing technique used to collect the data. Data reduction, data display and drawing conclusion are the process of analysis technique. To classify and explain the social factors of code mixing, Musyken’s typology of code mixing (2000) and Kim’s theory of reasons and motive of mixing code (2006) were used. From the finding, there was found that three types of code mixing such as insertion, alternation and congruent lexicalization. Insertion type of code mixing was dominated in BKN’s twitter status, followed by congruent lexicalization and the last is alternation. Most of mixing code in twitter status related with particular topic about computer technical support, online registration system, and requirements of civil servant candidates. However, there are other code-mixing factors; participant roles and relationships and internal factors (quotation).