Analisis Sentimen Ulasan Pengguna Aplikasi Kredivo Menggunakan Algoritma Support Vector Machine (SVM) dengan Metode TF–IDF


Ariska Sari(1*); Bambang Irawan(2); Ahmad Faqih(3); Arif Rinaldi Dikananda(4); Fathurrohman Fathurrohman(5);

(1) STMIK IKMI CIREBON
(2) STMIK IKMI CIREBON
(3) STMIK IKMI CIREBON
(4) STMIK IKMI CIREBON
(5) STMIK IKMI CIREBON
(*) Corresponding Author

  

Abstract


Perkembangan teknologi informasi telah mendorong peningkatan substansial dalam jumlah data teks yang dihasilkan melalui berbagai interaksi pengguna pada platform digital, khususnya di bidang layanan keuangan online. Data ulasan konsumen mengandung informasi berharga terkait tingkat kepuasan dan pandangan pelanggan terhadap suatu produk atau jasa. Kajian ini mengkhususkan diri pada penerapan analisis sentimen terhadap ulasan pengguna aplikasi Kredivo, dengan memanfaatkan algoritma Support Vector Machine (SVM) serta serangkaian langkah pra-pemrosesan teks yang komprehensif. Langkah-langkah tersebut meliputi case folding, pembersihan data, tokenisasi, penghapusan kata-kata berhenti, dan stemming dengan bantuan pustaka Sastrawi yang dirancang untuk Bahasa Indonesia. Fitur teks diekstraksi menggunakan pendekatan Term Frequency–Inverse Document Frequency (TF–IDF), kemudian diklasifikasikan melalui model SVM dengan kernel Radial Basis Function (RBF). Hasil percobaan menunjukkan bahwa model SVM menunjukkan kinerja klasifikasi yang superior, dengan tingkat akurasi yang tinggi dalam membedakan sentimen positif, negatif, dan netral. Temuan ini konsisten dengan studi sebelumnya yang menekankan bahwa penggabungan stemming, penghapusan kata-kata berhenti, dan SVM dapat meningkatkan akurasi analisis sentimen secara bermakna. Secara keseluruhan, penelitian ini memberikan sumbangan bagi pengembangan teknik analisis sentimen dalam Bahasa Indonesia, terutama di sektor teknologi keuangan, dengan membuktikan bahwa integrasi antara SVM dan TF–IDF, yang didukung oleh pra-pemrosesan yang sesuai, mampu menghasilkan model klasifikasi opini pelanggan yang efektif dan mampu menyesuaikan diri dengan nuansa linguistik Bahasa Indonesia

Keywords


analisis sentimen; Support Vector Machine (SVM); text preprocessing; TF–IDF; Bahasa Indonesia; fintech

  
  

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doi  https://doi.org/10.33096/linier.v2i4.3344 		  

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