Habibullah Akbar



Nowadays, the development of digital technology allows the higher institutions such as universities to observe public opinion/sentiment through social media. Unfortunately, the analysis of the data is still done manually. In this study, we develop an automated sentiment analysis framework to predict the polarity of public opinion on several higher education institutions in Indonesia. The data used comes from the comments that are available on the official page of the institution's Facebook. We make comparisons of several combined models between classifiers (Logistic Regression, Support Vector Machine, Random Forest Classifier and Neural Network) with feature extraction methods (Count vectorizer and TF-IDF). The experimental results show that the best sentiment analysis results are given by the Neural Network model which uses the TF-IDF weighting technique with an accuracy of more than 86%. These results indicate that the proposed sentiment analysis framework is potential to be used to find out public opinion on higher education institutions in Indonesia.


Keywords: Data mining, sentiment analysis, machine learning.



Di masa sekarang ini, perkembangan teknologi digital memungkinkan institusi pendidikan tinggi seperti universitas untuk mengetahui opini/sentimen masyarakat lewat media sosial. Sayangnya, analisis terhadap data tersebut masih dilakukan secara manual. Pada penelitian ini, kami membangun sebuah kerangka analisis sentimen otomatis untuk memprediksi polaritas dari opini masyarakat terhadap beberapa institusi pendidikan tinggi di Indonesia. Data yang digunakan berasal dari komentar-komentar yang berada pada halaman resmi Facebook institusi yang digunakan untuk penelitian. Kami melakukan perbandingan dari beberapa model gabungan antara klasifikator (Logistic Regression, Support Vector Machine, Random Forest Classifier dan Neural Network) dengan metode ekstraksi fitur (Count vectorizer dan TF-IDF). Hasil eksperimen menunjukkan bahwasanya hasil analisis sentimen terbaik diberikan oleh model Neural Network yang menggunakan teknik pembobotan TF-IDF dengan akurasi lebih dari 86%. Hasil ini menunjukkan bahwa kerangka analisis sentimen yang diusulkan berpotensi untuk digunakan untuk mengetahui opini masyarakat terhadap institusi pendidikan tinggi yang ada di Indonesia.   


Kata kunci: Data mining, analisis sentimen, machine learning. 

Teks Lengkap:



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