Analisis Sentimen Terhadap Pembelajaran Daring Di Indonesia Menggunakan Support Vector Machine (SVM)
DOI:
https://doi.org/10.22441/fifo.2022.v14i1.007Keywords:
Analisis Sentimen, Klasifikasi, Support Vector MachineAbstract
Pada masa pandemi ini tercipta kebijakan baru dalam dunia pendidikan. Kebijakan tersebut menganjurkan pelajar untuk melaksanakan pembelajaran dalam jaringan (daring) dengan jangka waktu yang panjang. Kebijakan baru menimbulkan banyaknya opini publik yang disampaikan melalui media sosial. Oleh karena itu, penelitian ini akan melakukan analisis sentimen terhadap opini publik mengenai pembelajaran daring di Indonesia untuk memberikan informasi atau evaluasi terhadap opini publik pada media sosial twitter. Analisis sentimen dapat dilakukan dengan mengklasifikasi opini publik menjadi opini positif dan opini negatif dengan metode Support Vector Machine (SVM). Dalam mengklasifikasikan data dapat dilakukan pelabelan data dan pembersihan data terlebih dahulu sebelum melalui proses text preprocessing, kemudian data diberikan bobot setiap kata dengan Term Frequncy–Invers Document Frequency (TF-IDF) yang akan dijadikan sebagai fitur setelah itu pembagian data menggunakan 10-fold cross validation dan diklasifikasikan dengan metode Support Vector Machine (SVM). Hasil rata-rata evaluasi dengan confusion matrix yaitu accuracy sebesar 0,72.
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