Klasifikasi Prioritas Pengaduan Pelayanan Publik Menggunakan Natural Language Processing dan Machine Learning
DOI:
https://doi.org/10.57093/jisti.v9i2.430Keywords:
Pengaduan publik, NLP, Machine Learning, Klasifikasi prioritas, TF-IDFAbstract
Penelitian ini mengklasifikasikan prioritas pengaduan pelayanan publik menggunakan Natural Language Processing dan Machine Learning pada 470 pengaduan Kantor Desa Gattareng Toa. Label Tinggi, Sedang, dan Rendah ditetapkan melalui penilaian dua pakar dan konsensus. Teks diproses melalui pembersihan, normalisasi, tokenisasi, dan TF-IDF, kemudian diuji menggunakan Naive Bayes, Logistic Regression, SVM, dan Random Forest. Evaluasi lima fold terkelompok berdasarkan pola inti menunjukkan Logistic Regression menghasilkan accuracy 88,94% dan Macro-F1 87,13%, dengan 30 pengaduan Tinggi salah diklasifikasikan sebagai Sedang. Penyesuaian ambang meningkatkan recall Tinggi dari 68,42% menjadi 80,00%, menurunkan kasus terlewat menjadi 19, tetapi meningkatkan peringatan keliru dari 11 menjadi 24. Temuan menunjukkan pentingnya evaluasi ketahanan pola dan kompromi risiko; keluaran model dimaksudkan sebagai alat bantu verifikasi petugas, bukan keputusan otomatis
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