Pub Date : 2023-10-26DOI: 10.61320/jolcc.v1i2.126-140
Nur Indah Sulistyowati Lutfiyah, Jumanto Jumanto
Medical terms are language units used to describe medical conditions such as diseases, symptoms, anatomy, and procedures. Medical terms are composed of a combination of affixes and root words. This research focused on contrastive analysis, a study comparing two languages, the source language and their equivalent in the target language. This study aims (1) to find out similarities between English medical terms with affixes in English and their Indonesian equivalents in Dorland's Illustrated Medical Dictionary (2) to find out differences between English medical terms with affixes in English and their Indonesian equivalents in Dorland's Illustrated Medical Dictionary (3) to predict learning problems based on the differences between English medical terms with affixes in English and their Indonesian equivalents in Dorland's Illustrated Medical Dictionary. The data source of this research is "Dorland's Illustrated Medical Dictionary 31st edition" by W.A. Newman Dorland and translated into Indonesian under the title " Kamus Kedokteran Dorland Edisi 3" by Retna Neary Elseria et al. This research is qualitative, and the type of research is library research. The study's results revealed seven affix English medical terms that have similarities with Indonesian equivalents and 93 terms that differ from Indonesian equivalents. The similarities and differences are divided into prefixes and suffixes. There are 8 types of prefixes and 4 types of suffixes. Predictions of problems that may occur in these conditions are in different forms, uses, and meanings. This research is expected to help better understand affixes in English medical terms and their Indonesian equivalents.
{"title":"Contrastive Analysis Between English Medical Terms with Affixes and Their Indonesian Equivalent in Dorland’s Illustrated Medical Dictionary","authors":"Nur Indah Sulistyowati Lutfiyah, Jumanto Jumanto","doi":"10.61320/jolcc.v1i2.126-140","DOIUrl":"https://doi.org/10.61320/jolcc.v1i2.126-140","url":null,"abstract":"Medical terms are language units used to describe medical conditions such as diseases, symptoms, anatomy, and procedures. Medical terms are composed of a combination of affixes and root words. This research focused on contrastive analysis, a study comparing two languages, the source language and their equivalent in the target language. This study aims (1) to find out similarities between English medical terms with affixes in English and their Indonesian equivalents in Dorland's Illustrated Medical Dictionary (2) to find out differences between English medical terms with affixes in English and their Indonesian equivalents in Dorland's Illustrated Medical Dictionary (3) to predict learning problems based on the differences between English medical terms with affixes in English and their Indonesian equivalents in Dorland's Illustrated Medical Dictionary. The data source of this research is \"Dorland's Illustrated Medical Dictionary 31st edition\" by W.A. Newman Dorland and translated into Indonesian under the title \" Kamus Kedokteran Dorland Edisi 3\" by Retna Neary Elseria et al. This research is qualitative, and the type of research is library research. The study's results revealed seven affix English medical terms that have similarities with Indonesian equivalents and 93 terms that differ from Indonesian equivalents. The similarities and differences are divided into prefixes and suffixes. There are 8 types of prefixes and 4 types of suffixes. Predictions of problems that may occur in these conditions are in different forms, uses, and meanings. This research is expected to help better understand affixes in English medical terms and their Indonesian equivalents.","PeriodicalId":487954,"journal":{"name":"Journal of Linguistics Culture and Communication","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The lyrics emphasize the importance of determination and having a positive mindset to overcome obstacles. This research paper analyzes the moral values present in the song "Never Say Never” by Justin Bieber. The study aims to explore the moral values conveyed in the song lyrics. This research used a qualitative approach to analyze this song lyric. The data was analyzed by identifying, classifying, and interpreting the moral values. The findings of research found that three main moral values in the song: self-belief (57,14%), positivity (21,42%), and optimism (21,42%). The dominant moral value identified was self-belief, followed by positivity and optimism. These moral values can make students inspiring, provide them positive energy, and enable them to carry out worthwhile activities into education and personal development
歌词强调了克服困难的决心和积极心态的重要性。本研究分析了贾斯汀·比伯的歌曲《Never Say Never》中所体现的道德价值观。本研究旨在探讨歌曲歌词所传达的道德价值观。本研究采用定性的方法来分析这首歌的歌词。通过识别、分类和解释道德价值观来分析数据。研究发现,歌曲中的三个主要道德价值观是:自信(57,14%),积极(21,42%)和乐观(21,42%)。主要的道德价值是自信,其次是积极和乐观。这些道德价值观可以鼓舞学生,为他们提供正能量,使他们能够在教育和个人发展中开展有价值的活动
{"title":"Having Good Moral Values Through \"Never Say Never\" Song by Justin Bieber","authors":"None Yuli Rohmiyati, Sabrina Fatihah, Putri Solihatunnisa","doi":"10.61320/jolcc.v1i2.116-125","DOIUrl":"https://doi.org/10.61320/jolcc.v1i2.116-125","url":null,"abstract":"The lyrics emphasize the importance of determination and having a positive mindset to overcome obstacles. This research paper analyzes the moral values present in the song \"Never Say Never” by Justin Bieber. The study aims to explore the moral values conveyed in the song lyrics. This research used a qualitative approach to analyze this song lyric. The data was analyzed by identifying, classifying, and interpreting the moral values. The findings of research found that three main moral values in the song: self-belief (57,14%), positivity (21,42%), and optimism (21,42%). The dominant moral value identified was self-belief, followed by positivity and optimism. These moral values can make students inspiring, provide them positive energy, and enable them to carry out worthwhile activities into education and personal development","PeriodicalId":487954,"journal":{"name":"Journal of Linguistics Culture and Communication","volume":"12 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.61320/jolcc.v1i2.91-99
Vincent Smith, Michael Garrett, Austin Harwood, James Shamblin
Many YouTube videos provide written audio transcripts which provide information on the language used on YouTube. One important measure relating to language usage is word frequency. Using student-developed software and libraries in R, Python, and Microsoft Excel, the transcripts of one million YouTube videos from the YouTube-8M data set were scraped and analyzed. The word frequency of the YouTube data set was shown to correlate with commonly used word frequency measures from established studies, such as the subtitle word frequency and the HAL word frequency.
{"title":"YouTube Transcripts Word Frequency Measure","authors":"Vincent Smith, Michael Garrett, Austin Harwood, James Shamblin","doi":"10.61320/jolcc.v1i2.91-99","DOIUrl":"https://doi.org/10.61320/jolcc.v1i2.91-99","url":null,"abstract":"Many YouTube videos provide written audio transcripts which provide information on the language used on YouTube. One important measure relating to language usage is word frequency. Using student-developed software and libraries in R, Python, and Microsoft Excel, the transcripts of one million YouTube videos from the YouTube-8M data set were scraped and analyzed. The word frequency of the YouTube data set was shown to correlate with commonly used word frequency measures from established studies, such as the subtitle word frequency and the HAL word frequency.","PeriodicalId":487954,"journal":{"name":"Journal of Linguistics Culture and Communication","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135938481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-10DOI: 10.61320/jolcc.v1i2.100-115
Hasyifa Diffani, Adhan Kholis
A movie consists of a dialogue between its characters. Both the speaker and the listener use many different kinds of utterances. In communication, we use utterances in action, also known as speech actions. This research aimed to determine the types of speech acts performed by the characters and determine the function of the speech acts performed by the characters in Turning Red's movie. A movie can deliver a message to the audience as one form of communication. It contains a variety of genres, making it an interesting topic to be a research. In this case, understanding speech act theory becomes essential for solving the issue. Speech acts are things that can be accomplished through speech. There are three types of speech acts: locutionary, illocutionary, and perlocutionary. The movie has been chosen since it is a children's genre movie representative of Asian parenting style and leaves the deepest messages to children. The data were taken directly from the movie dialogue, focusing on the speech acts uttered by the characters and the context of the occurrence of the utterance. In collecting the data, the researchers transcribed the data comprehensively, which were analyzed using content analysis on the movie Turning Red. The researchers analyzed some illocutionary acts in the movie conversation. The data was divided into five types: directive, representative, declarative, commissive, and expressive. Teachers can create relevant and engaging learning experiences that build pragmatic competence and improve students' communicative skills by adding actual speech acts from movies into language lessons. It helps to understand how movies can be used as valuable instruments in English language teaching, especially in the development of language acquisition.
{"title":"An Analysis of Speech Act in the Movie \"Turning Red\"","authors":"Hasyifa Diffani, Adhan Kholis","doi":"10.61320/jolcc.v1i2.100-115","DOIUrl":"https://doi.org/10.61320/jolcc.v1i2.100-115","url":null,"abstract":"A movie consists of a dialogue between its characters. Both the speaker and the listener use many different kinds of utterances. In communication, we use utterances in action, also known as speech actions. This research aimed to determine the types of speech acts performed by the characters and determine the function of the speech acts performed by the characters in Turning Red's movie. A movie can deliver a message to the audience as one form of communication. It contains a variety of genres, making it an interesting topic to be a research. In this case, understanding speech act theory becomes essential for solving the issue. Speech acts are things that can be accomplished through speech. There are three types of speech acts: locutionary, illocutionary, and perlocutionary. The movie has been chosen since it is a children's genre movie representative of Asian parenting style and leaves the deepest messages to children. The data were taken directly from the movie dialogue, focusing on the speech acts uttered by the characters and the context of the occurrence of the utterance. In collecting the data, the researchers transcribed the data comprehensively, which were analyzed using content analysis on the movie Turning Red. The researchers analyzed some illocutionary acts in the movie conversation. The data was divided into five types: directive, representative, declarative, commissive, and expressive. Teachers can create relevant and engaging learning experiences that build pragmatic competence and improve students' communicative skills by adding actual speech acts from movies into language lessons. It helps to understand how movies can be used as valuable instruments in English language teaching, especially in the development of language acquisition.","PeriodicalId":487954,"journal":{"name":"Journal of Linguistics Culture and Communication","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}