Implementasi Natural Language Processing Pada Pengukuran Sentimen Subsidi Pemerintah Terhadap Harga Barang Pokok

Sean Lawrence Meiruntu, Sunneng Sandino Berutu, Jatmika Jatmika

Abstract


Pemerintah memberikan subsidi atas harga barang pokok, pelaksanaan subsidi itu sudah berlangsung beberapa tahun Tetapi opini masyarakat terhadap subsidi pemerintah ini belum pernah di ukur. Oleh karena itu, penelitian ini dirancang untuk mengukur opini masyarakat menggunakan pendekatan Natural Language Processing (NLP). Sumber data berasal dari platform Twitter dengan rentang waktu 2016 hingga 2024. Data dikumpulkan melalui proses crawling, kemudian diseleksi dengan kata kunci tertentu. Setelah melalui tahap pra-pemrosesan, data dianalisis menggunakan algoritma VADER. Untuk mendeteksi nuansa emosi yang lebih beragam, diterapkan pula pendekatan logika fuzzy sebagai pelabelan tambahan berdasarkan metode dari Berutu et al. Dalam proses klasifikasi, data yang telah dilabeli VADER digunakan dalam dua bentuk: data asli (tidak seimbang) dan data seimbang yang diperoleh melalui teknik Synthetic Minority Over-sampling Technique (SMOTE). Model klasifikasi menggunakan algoritma Random Forest. Hasil evaluasi menunjukkan bahwa data seimbang memberikan hasil prediksi yang lebih baik, terutama dalam mengenali kategori sentimen dengan jumlah data lebih sedikit. Penelitian ini menunjukkan bahwa kombinasi NLP, pelabelan berbasis VADER, serta algoritma pembelajaran mesin dapat digunakan untuk memahami opini publik terhadap program bantuan pemerintah secara lebih objektif dan terukur.

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DOI: https://doi.org/10.29100/jipi.v11i2.8174

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