Aspect Category Classification with Machine Learning Approach using Indonesian Language Dataset

  • SYAIFULLOH AMIEN PANDEGA PERDANA UNIVERSITAS GADJAH MADA
  • Teguh Bharata Aji Universitas Gadjah Mada
  • Ridi Ferdiana Universitas Gadjah Mada

Abstract

Ulasan pelanggan merupakan opini terhadap kualitas barang atau jasa yang dirasakan konsumen. Ulasan pelanggan mengandung informasi yang berguna bagi konsumen maupun penyedia barang atau jasa. Ketersediaan ulasan pelanggan dalam jumlah besar di web membutuhkan suatu framework untuk mengekstraksi sentimen secara otomatis. Sebuah ulasan pelangan seringkali mengandung banyak aspek, sehingga Aspect Based Sentiment Analysys (ABSA) harus digunakan untuk mengetahui polaritas masing-masing aspek. Salah satu tugas penting dalam ABSA adalah Aspect Category Detection. Metode machine learning untuk Aspect Category Detection sudah banyak dilakukan pada domain berbahasa Inggris, namun pada domain Bahasa Indonesia masih sedikit. Penelitian ini membandingkan kinerja tiga algoritma machine learning yaitu Naïve Bayes (NB), Support Vector Machine (SVM) dan Random Forest (RF) pada ulasan pelanggan berbahasa Indonesia dengan menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) sebagai term weighting. Hasil penelitian menunjukkan bahwa RF berkinerja paling unggul dibandingkan NB dan SVM pada tiga domain yang berbeda yaitu restoran, hotel dan e-commerce dengan nilai F1-Score untuk masing-masing domain yaitu: 84.3%, 85.7%, dan 89,3%.

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Published
2021-08-26
How to Cite
SYAIFULLOH AMIEN PANDEGA PERDANA, Teguh Bharata Aji, & Ridi Ferdiana. (2021). Aspect Category Classification with Machine Learning Approach using Indonesian Language Dataset. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 10(3), 229-235. https://doi.org/10.22146/jnteti.v10i3.1819
Section
Articles