CAMPUR KODE DALAM AKUN TWITTER YOUTUBER NESSIE JUDGE

Penelitian ini bertujuan untuk menganalisis dan mendeskripsikan jenis, bentuk, serta faktor penyebab terjadinya campur kode yang digunakan oleh Nessie Judge dalam akun media sosial Twitter. Data yang digunakan merupakan tulisan berupa cuitan beserta komentar dan balasan komentar dalam akun Twitter Nessie Judge. Metode penelitian yang digunakan adalah metode deskriptif kualitatif. Teknik pengumpulan data menggunakan teknik observasi, teknik dokumentasi, teknik simak bebas libat cakap, dan teknik catat. Hasil penelitian menunjukkan bahwa: (1) jenis campur kode yang ditemukan merupakan jenis campur kode ke luar antara bahasa Inggris dan bahasa Indonesia sebanyak 79 pertuturan tulisan (33,33%); (2) bentuk campur kode yang ditemukan berupa penyisipan unsur kata, frasa, baster, idiom, dan klausa. Bentuk campur kode yang paling dominan ditemukan yaitu penyisipan unsur kata sebanyak 51 pertuturan tulisan (21,52%); dan (3) faktor penyebab terjadinya campur kode yang paling dominan ditemukan yaitu faktor penggunaan istilah yang lebih populer sebanyak 21 pertuturan tulisan (8,86%). Kata kunci: sosiolinguistik, campur kode, akun Twitter ********** This study aims to analyze and describe the types, forms, and factors that cause code mixing used by Nessie Judge on Twitter accounts. The data used is written in the form of tweets along with comments and replies to comments on Nessie Judge's Twitter account. The research method used is a descriptive qualitative method. Data collection techniques used observation techniques, documentation techniques, free listening and conversation techniques, and note-taking techniques. The results showed that: (1) the types of code mixing found were outward code mixing types between English and Indonesian as many as 79 written speeches (33.33%); (2) the form of code mixing found in the form of insertion of elements of words, phrases, baster, idioms, and clauses. The most dominant form of code mixing was found, namely the insertion of word elements as many as 51 written speeches (21.52%); and (3) the most dominant factors causing code mixing were found, namely the use of the more popular terms as many as 21 written speeches (8.86%). Keywords: sociolinguistics, code mixing, Twitter account