Sentiment Analysis of GoPay Application is Improved Using  Natural Language Processing Method to Optimize Services

Ibnu Sina Salman Syam; Martanto; Arif Dikananda; Dede Rohman

doi:10.59934/jaiea.v4i3.961

Authors

Ibnu Sina Salman Syam STMIK IKMI Cirebon
Martanto STMIK IKMI Cirebon
Arif Dikananda STMIK IKMI Cirebon
Dede Rohman STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.961

Keywords:

Natural laguage Processing, Sentiment Analysis,User Reviews

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Gopay menggunakan pendekatan Natural Language Processing (NLP) berbasis algoritma Naïve Bayes Multinomial. Ulasan pengguna diambil dari kumpulan data publik yang tersedia di platform Kaggle, yang berisi teks ulasan dan skor penilaian dalam bahasa Indonesia. Tahapan penelitian ini meliputi pengumpulan data, praproses data, transformasi data, pemodelan sentimen, dan evaluasi model. Pada tahap pemilihan data, hanya dua kolom yang relevan, yaitu teks ulasan dan skor penilaian, yang dipertahankan untuk memastikan analisis yang terfokus. Tahap praproses meliputi pembersihan teks, penghilangan tanda baca, tokenisasi, penghilangan kata henti, dan stemming untuk menghasilkan data yang bersih dan terstruktur. Data yang diolah kemudian diubah menjadi representasi numerik menggunakan teknik TF-IDF (Term Frequency-Inverse Document Frequency). Proses klasifikasi dilakukan menggunakan algoritma Naïve Bayes Multinomial, di mana ulasan dikategorikan menjadi tiga sentimen: positif, netral, dan negatif, berdasarkan skor penilaian pengguna. Evaluasi model dilakukan dengan menggunakan metrik akurasi, presisi, recall, dan skor F1. Hasil penelitian menunjukkan bahwa algoritma Multinomial Naïve Bayes mampu mengklasifikasikan sentimen dengan akurasi 90%, dengan kinerja terbaik pada sentimen positif. Namun, model menunjukkan kelemahan pada klasifikasi sentimen netral, dengan skor F1 yang sangat rendah, yang disebabkan oleh ketidakseimbangan jumlah data antar kategori. Penelitian ini berkontribusi dengan menghasilkan wawasan tentang persepsi pengguna terhadap aplikasi Gopay. Hasil analisis menunjukkan bahwa sebagian besar ulasan bersifat positif, mencerminkan kepuasan pengguna terhadap kemudahan dan keamanan layanan. Sementara itu, ulasan negatif menunjukkan masalah teknis dan layanan pelanggan yang memerlukan perhatian lebih lanjut. Penelitian ini merekomendasikan untuk meningkatkan kualitas layanan berdasarkan keluhan dalam ulasan negatif dan mempertahankan keunggulan fitur yang diapresiasi pengguna. Dengan demikian, hasil penelitian ini dapat digunakan untuk mendukung pengambilan keputusan strategis dalam pengembangan layanan GoPay.

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