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All Journal Jurnal Tekno Insentif Jurnal Sisfotek Global Academic Journal of Computer Science Research (AJCSR) Agroswagati : Jurnal Agronomi Jurnal Sisfotek Global Jurnal Tren Bisnis Global
Iqbal, Muchamad
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering

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Analisis Text Mining terhadap Penggunaan Paylater Menggunakan Naïve Bayes Classifier Ryando, M. Bucci; Iqbal, Muchamad; Syahidah, Kurnia
Jurnal Tekno Insentif Vol 19 No 2 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i2.2065

Abstract

Abstrak Popularitas Paylater sebagai metode pembayaran e-commerce pasca-pandemi di Indonesia meningkat pesat, namun disertai risiko utang konsumtif, khususnya di kalangan Generasi Z. Penelitian ini bertujuan menganalisis sentimen dan opini publik guna memahami faktor yang memengaruhi keputusan adopsi Paylater. Dengan menggunakan metode analisis sentimen berbasis Naïve Bayes Classifier terhadap data Twitter (kini X), penelitian ini mengklasifikasikan tanggapan masyarakat terhadap penggunaan Paylater. Model yang dibangun divalidasi dengan nilai F1-score 0.432 dan Precision 0.508. Hasil analisis menunjukkan mayoritas sentimen (50,75%) bersifat netral atau ambigu, mencerminkan adanya keraguan publik terhadap penggunaan layanan ini. Selain itu, ditemukan dominasi sentimen negatif yang menyoroti isu peningkatan utang, kesulitan mengelola keuangan, serta ketergantungan terhadap fasilitas kredit konsumtif. Penelitian ini berkontribusi dalam pemanfaatan text mining untuk memetakan persepsi Generasi Z terhadap adopsi Paylater, sehingga hasilnya dapat menjadi dasar bagi perusahaan fintech dalam merumuskan strategi pemasaran yang lebih bijak dan bertanggung jawab. Kata kunci: paylater, analisis sentimen, text mining, generasi z, naïve bayes classifier. Abstract The popularity of Paylater as an e-commerce payment method in post-pandemic Indonesia has grown rapidly but is accompanied by the risk of consumptive debt, particularly among Generation Z. This study aims to analyze public sentiment and opinions to understand the factors influencing Paylater adoption decisions. Using a Naïve Bayes Classifier-based sentiment analysis method on Twitter (now X) data, this research classifies public responses toward Paylater usage. The developed model was validated with an F1-score of 0.432 and Precision of 0.508. The results indicate that the majority of sentiments (50.75%) are neutral or ambiguous, reflecting public uncertainty toward the service. In addition, dominant negative sentiments were identified, highlighting issues such as increasing debt, financial management difficulties, and dependency on credit facilities. This study contributes to the use of text mining in mapping Generation Z’s perceptions of Paylater adoption, providing insights that can help fintech companies develop more responsible and ethical marketing strategies. Keywords: paylater, sentiment analysis, text mining, z generation, naïve bayes classifier.
Co-Authors A.A. Ketut Agung Cahyawan W Agusli, Rahmat Amri, Mila Awanda, Irfan Cahyadiputra, Achmat Syabanu Dedi Dedi Dwi Puji Astuti Ferawati Ferawati Fitriani, Risma Hanafri, Muhammad Iqbal Hendrawansyah, Hendrawansyah Hidayanti, Putri Insani, Azzelia Nur Irmansyah, Deri Julianti, Muhammad Ramaddan Julyanto, Mohamad Fallah M. Bucci Ryando Mariana, Arni Retno Nur, Subandi Otto Fajarianto Prasetyo, Aditya Budi Puput Puspito Rini Rachmadi, Basuki Rika, Rika Sanjaya, Hengki Santi, Chika Andriyani Gumay Saputra, Fendi Setiawan, Prayoga Soetjipto, Ken Sabardiman Sofyan, Asep Abdul SRI RAHAYU Sudiarjo, Aso Sutarman Sutarman Syahidah, Kurnia Tadjudin, Endang Tamaulina Br Sembiring Triono Triono Tullah, Rahmat Yudistira, Arie