Pemilihan Calon Gubernur  dan  Wakil  Gubernur Bali 2018 akan  melalui  beberapa  tahapan  pemilu  mulai  dari penentuan  bakal  calon  Gubernur  dan  Wakil  Gubernur  Bali hingga tahapan penghitungan suara. Dalam pemilihan Gubernur dan  Wakil  Gubernur  Bali  masyarakat  dapat  terlibat  langsung dalam tahapan pemungutan suara yang akan dilaksanakan pada tanggal 27 Juni 2018 (KPU, 2018). Sehingga dapat memunculkan banyak  komentar  atau  pendapat,  tidak  hanya  komentar  positif dan   netral   tapi   juga   komentar   yang   negatif.   Penelitian   ini diharapkan   mampu   untuk   melakukan   riset   atas   komentar masyarakat  yang  mengandung  sentimen  baik  atau  positif,  sama sekali tidak mengandung senrimen atau netral dan mengandung sentimen   buruk   atau   negatif. Dalam   penelitian   ini   metode digunakan untuk preprocessingdata menggunakan tokenisasi N-gram.  N-gram  adalah  token  yang  terdiri  dari  tiga  kata  setiap satu token. Pada  tahap  stemming  menggunakan algoritma  Nzief Adriani.   Untuk   proses   klasifikasinya   menggunakan   metode Naïve   Bayes   Classifier (NBC).Pada   pengujian   data   calon Gubernur  akurasi tertinggi diperoleh  dari  klasifikasi  data KBS-Ace  pada  data  yang  diambil  dari  Twitter  dengan  nilai  akurasi 89%, presisi  91%  dan  recall  94%  dan  akurasi  terendah  pada saat proses kalsifikasi data KBS-Ace pada media sosial Facebook. 
Kata  Kunci—Analisa  Sentimen,  Calon  Gubernur  Bali  2018, Naive Bayes Classifier

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Pemanfaatan Big Data Media Sosial Dalam Menganalisa Kemenangan Pilkada

Along with the development of the movie industry and the internet, watching movies has become accessible to the public. Nowadays, it is easy to watch movies, and people need to know whether a film is good or not through a collection of movie reviews. The extensive collection of movie reviews spread across many sites makes it difficult for the public to find valid reviews of existing films. From these problems, the public can search for valid and valuable information about film reviews. Therefore, it is necessary to use sentiment analysis on movie reviews to make it easier for the public to find valid and valuable information. The method used in this research is Naive Bayes as classifier and Word2Vec as feature extraction. Word2Vec is chosen because this method can group words with the same meaning in a vector form. Naive Bayes is chosen as a classifier because the method works quickly and is easy to implement. The performance result from this research produces an accuracy value of 72.23%.

Sentiment Analysis on Movie Review from Rotten Tomatoes Using Word2Vec and Naive Bayes

Zomato is a restaurant search site that provides information about the restaurants in indonesia. Zomato provides accurate and very detailed information, ranging from photos, food, menus, to reviews from diners. Review helps restaurant owners can see how consumers resond to their products. With the large number of reviews, required a system that can process the data review by applying ofunderstanding, extracting and processing textual data automatically to get sentiment information contained in an opinion sentence.  In the sentiment analysis, one of the classification methods that can be used is the Naive BayesClassifier. This system uses the Naive Bayes Classifier method for its classification. The stage to be performed before the classification is preprocessing. Stages in preprocessing consist of case folding, tokenizing, stopword removal, and stemming using nazief adriani algorithm. Reviews that have passed the preprocessing stage will be classified into positive opinions or negative opinions and displayed into table form. The accuracy of the system in the analysis of sentiments on the review of fish streat in zomato application was 87,50%.

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Elisa Eka Shinta Pratiwi

Papers

Analisis Sentimen Terhadap Review Restoran Fish Streat pada Aplikasi Zomato Menggunakan Stemming Nazief Adriani dan Naive Bayes Classifier