TREN PENELITIAN KLASIFIKASI SENTIMEN PELANGGAN BERBASIS MACHINE LEARNING

Authors

  • Rohimatul Maisyaroh Teknik Informatika, Fakultas Teknik, Universitas Islam Madura, Pamekasan
  • Nur Azizah Teknik Informatika, Fakultas Teknik, Universitas Islam Madura, Pamekasan
  • Nurul Hidayat Teknik Informatika, Fakultas Teknik, Universitas Islam Madura, Pamekasan
  • Hozairi Hozairi Teknik Informatika, Fakultas Teknik, Universitas Islam Madura, Pamekasan

DOI:

https://doi.org/10.23960/jrl.v4i3.69

Abstract

Pesatnya perkembangan industri online reviews mendorong kebutuhan akan teknologi cerdas, seperti machine learning, untuk menganalisis sentimen pelanggan secara efektif. Penelitian ini bertujuan untuk mengidentifikasi tren publikasi, pola kolaborasi, fokus penelitian, dan tren sitasi terkait klasifikasi sentimen pelanggan berbasis machine learning dalam konteks online reviews pada database Scopus selama periode 2020–2025. Metode yang digunakan adalah analisis bibliometrik dengan pendekatan visualisasi dan deskriptif, meliputi lima tahap utama: penentuan kata kunci, pencarian awal, penyempurnaan hasil pencarian, kompilasi statistik, dan analisis data. Dari total 2.101 publikasi awal, diperoleh 441 dokumen relevan setelah proses penyaringan. Hasil menunjukkan peningkatan publikasi signifikan pada 2023, dengan dominasi bidang ilmu Computer Science (45,7%). India dan China tercatat sebagai negara dengan kontribusi publikasi tertinggi, sedangkan IEEE Access menjadi sumber publikasi terkemuka. Analisis co-word mengidentifikasi topik dominan seperti machine learning, electronic commerce, dan deep learning. Temuan ini memberikan gambaran menyeluruh mengenai peta riset global, serta menjadi landasan penting untuk pengembangan teknologi dan strategi bisnis berbasis machine learning dalam analisis online reviews ke depannya.

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Published

2025-10-12

How to Cite

Maisyaroh, R., Azizah, N., Hidayat, N., & Hozairi, H. (2025). TREN PENELITIAN KLASIFIKASI SENTIMEN PELANGGAN BERBASIS MACHINE LEARNING. Jurnal Rekayasa Lampung, 4(3). https://doi.org/10.23960/jrl.v4i3.69

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Vol. 4 No. 3 (2025)

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