Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter

Ghulam Asrofi Buntoro

Abstract


Abstract. Jakarta Governor Election 2017 discussed in society or internet, especially Twitter. Everyone is free opine on Jakarta governor candidate 2017 so many opinions, not only positive or neutral opinion but also negative. Social media, especially Twitter now become promotions or campaigns are effective and efficient. This research is expected be useful to conduct on public opinion containing sentiment positive, neutral or negative. The method used in this study, for data preprocessing using tokenisasi, cleansing and filtering, to define class sentiment with methods Lexicon Based. For classification using Naive Bayes classifier (NBC) and Support Vector Machine (SVM). The data is 300 tweet in Indonesian by keyword AHY, Ahok, Anies. The results of research is analysis sentiment Jakarta governor candidate 2017. The highest accuracy when using the method of classification Naïve Bayes Classifier (NBC), with average 95% accuracy, 95% precision, 95% recall, TP rate 96,8% and TN rate 84,6%.

Keywords: analisis sentimen, jakarta governor candidate 2017, lexicon based, naïve bayes classifier, support vector machine

Abstrak. Pemilihan Gubernur DKI Jakarta 2017 ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Twitter. Semua orang bebas berpendapat atau beropini tentang calon Gubernur DKI Jakarta 2017 sehingga memunculkan banyak opini, tidak hanya opini yang positif atau netral tapi juga yang negatif. Media sosial khususnya Twitter sekarang ini menjadi salah satu tempat promosi atau kampanye yang efektif dan efisien. Penelitian ini diharapkan dapat bermanfaat membantu untuk melakukan riset atas opini masyarakat yang mengandung sentimen positif, netral atau negatif. Metode yang digunakan dalam penelitian ini, untuk preprocessing data menggunakan tokenisasi, cleansing dan filtering, untuk menentukan class sentimen dengan metode Lexicon Based. Untuk proses klasifikasinya menggunakan metode Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Data yang digunakan adalah tweet dalam bahasa Indonesia dengan kata kunci AHY, Ahok, Anies, dengan jumlah dataset sebanyak 300 tweet. Hasil dari penelitian ini adalah analisis sentimen terhadap calon gubernur DKI Jakarta 2017. Akurasi tertinggi didapat saat menggunakan metode klasifikasi Naïve Bayes Classifier (NBC), dengan nilai rata-rata akurasi mencapai 95%, nilai presisi 95%, nilai recall 95% nilai TP rate 96,8% dan nilai TN rate 84,6%.

Kata Kunci: analisis sentimen, calon gubernur dki jakarta 2016, lexicon based, naïve bayes classifier, support vector machine


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