Text Mining Literature Review on Indonesian Social Media

Angelina Pramana Thenata

Abstract


Era sekarang jumlah berita dari berbagai media sosial yang tersebar dalam waktu singkat dan kebutuhan masyarakat untuk mengkonsumsi berita dalam berbagai referensi dapat mempengaruhi kehidupan masyarakat. Hal ini menyebabkan data yang tersebar dapat dikumpulkan dan dimanfaatkan oleh pemerintah, pengusaha, analisis, ataupun peneliti untuk mengidentifikasi tren, mengembangkan bisnis, memprediksi perilaku pelanggan dan lain sebagainya. Pengumpulan data berita dari media sosial tersebut dapat menggunakan text mining yang melibatkan algoritma yakni Naive Bayes, K-NN, dan SVM. Namun, penggunaan algoritma pada studi kasus yang tidak sesuai dapat memberikan hasil yang tidak optimal. Oleh karena itu, penelitian ini akan menganalisis algoritma text mining yang diimplementasikan pada media sosial berbahasa Indonesia dengan memakai metode systematic literature review. Metode ini dimulai dengan melakukan tahap planning yang menetapkan pertanyaan penelitian, kata pencarian, sumber literatur digital, dan standard literatur. Dilanjutkan dengan tahap conducting yang memilih dan mencocokan standard literatur, serta ekstraksi data. Kemudian tahap reporting yang melakukan analisis hasil ekstraksi data sehingga bisa menemumkan informasi dan pengetahuan. Tolak ukur yang menjadi acuan untuk perbandingan yakni pengujian confusion matrix berupa accuracy, precision, dan recall. Adapun hasil dari penelitian ini ditemukan algoritma Naive Bayes memberikan hasil yang stabil tapi kurang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia. Sedangkan algortima K-NN dan SVM ditemukan memberikan hasil yang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia yang dibuktikan dengan accuracy (50%-98.13%), precision (58.22%-98.48%), dan recall (21.05%-98%).  


Keywords


Media Sosial; Text Mining; Literatur Review; Naive Bayes; K-NN; SVM

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References


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DOI: https://doi.org/10.26418/jp.v7i2.47975

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