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  • Mahasiswa Ilmu Komputer DIKE UGM Kembangkan Model Hybrid Deep Learning untuk Analisis Sentimen Berbasis Aspek

Mahasiswa Ilmu Komputer DIKE UGM Kembangkan Model Hybrid Deep Learning untuk Analisis Sentimen Berbasis Aspek

  • Berita Prodi S1 Ilmu Komputer, Kegiatan
  • 2 Juli 2025, 08.33
  • Oleh: wimbono
  • 0

Yogyakarta, 1 Juli 2025 – Peter Johan Arkadhira Setiabudi, mahasiswa Program Studi Ilmu Komputer, Departemen Ilmu Komputer dan Elektronika (DIKE) FMIPA UGM, berhasil mengembangkan model hybrid deep learning untuk meningkatkan akurasi dalam analisis sentimen berbasis aspek (Aspect-Based Sentiment Analysis/ABSA). Inovasi ini akan dipresentasikan dalam International Conference on Advanced Machine Learning and Data Science (AMLDS) 2025 yang akan diselenggarakan pada 19-21 Juli 2025 di Tokyo, Jepang.

Penelitian ini mengusulkan kombinasi BERT (Bidirectional Encoder Representations from Transformers) dan BiLSTM (Bidirectional Long Short-Term Memory) sebagai model hybrid untuk mengatasi keterbatasan pendekatan ABSA konvensional. Model dikembangkan dan diuji menggunakan dataset terbaru FABSA (Feedback Aspect-Based Sentiment Analysis) yang memuat lebih dari 10.000 ulasan dari berbagai sektor. Dengan menggabungkan keunggulan BERT dalam memahami konteks kata dan kemampuan BiLSTM dalam mempertahankan urutan kata, model ini mampu meningkatkan performa dalam memahami opini pengguna secara lebih mendalam. Penelitian ini dibimbing oleh Faizah, S.Kom., M.Kom., dosen DIKE UGM.

Hasil evaluasi menunjukkan bahwa model BERT-BiLSTM yang dikembangkan mencapai skor rata-rata F1 sebesar 0,7583 untuk tugas deteksi kategori aspek (Aspect Category Detection) dan akurasi sebesar 0,9334 untuk klasifikasi sentimen kategori aspek (Aspect Category Sentiment Classification). Nilai ini menunjukkan peningkatan performa dibandingkan model BERT standar, yang hanya mencatatkan F1-score sebesar 0,6425 dan akurasi 0,9112. Hasil ini menunjukkan bahwa pendekatan hybrid dapat meningkatkan kualitas analisis sentimen.

Analisis sentimen berbasis konteks menjadi salah satu pendekatan yang semakin relevan dalam memahami opini publik secara lebih mendalam untuk informasi digital saat ini. Teknologi ini memiliki berbagai potensi pemanfaatan, mulai dari pemantauan media sosial, analisis ulasan pelanggan, hingga pengembangan sistem rekomendasi yang mempertimbangkan emosi pengguna. Dengan kemampuannya dalam mengolah data teks yang kompleks, pendekatan ini dapat mendukung pengambilan keputusan yang lebih tepat oleh industri, pembuat kebijakan, maupun lembaga riset yang ingin lebih responsif terhadap berbagai pendapat masyarakat.

Penelitian ini juga memberikan kontribusi terhadap pencapaian Tujuan Pembangunan Berkelanjutan (SDGs), khususnya SDG 9: Industri, Inovasi, dan Infrastruktur. Melalui pengembangan solusi teknologi berbasis kecerdasan buatan,dan memperkuat ekosistem digital Indonesia.  Selain itu, penelitian ini juga membuka peluang eksplorasi lanjutan terhadap strategi penyeimbangan kelas data dan fine-tuning model untuk domain tertentu, sehingga dapat terus meningkatkan performa model dalam menganalisis opini masyarakat yang semakin kompleks. Diharapkan penelitian ini dapat memberikan salah satu kontribusi dalam pengembangan pemrosesan bahasa alami (Natural Language Processing/NLP) di masa depan.

 

Author: Peter Johan Arkadhira Setiabudi, Faizah

Editor: Marina 

#SDGs9

Tags: SDGs SDGs 9: Industri Inovasi dan Infrastruktur

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Universitas Gadjah Mada

Departemen Ilmu Komputer dan Elektronika

Fakultas Matematika dan Ilmu Pengetahuan Alam

Universitas Gadjah Mada

Gedung C, Lantai 4

Sekip Utara Bulaksumur Yogyakarta 55281

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