Analisis Tren Penelitian Residual Network (ResNet) melalui Systematic Literature Review dan Bibliometric Analysis
DOI:
https://doi.org/10.30595/sainteks.v22i2.27154Keywords:
residual network, tinjauan pustaka sistematis, analisis bibliometrik, pembelajaran mendalam, tren penelitianAbstract
Residual Network (ResNet) merupakan arsitektur penting dalam pengembangan Convolutional Neural Network (CNN) dan Deep Learning (DL), khususnya dalam tugas pengenalan pola visual. Studi ini bertujuan untuk mengkaji secara sistematis tren dan kontribusi ilmiah terkait ResNet melalui pendekatan Systematic Literature Review (SLR) dan Bibliometric Analysis terhadap publikasi dalam database Scopus pada periode 2020 hingga pertengahan 2025. Proses SLR mengikuti tahapan PRISMA: Identification, Screening, Eligibility, dan Inclusion, dengan hasil akhir 945 artikel dari 1.433 dokumen awal. Analisis dilakukan menggunakan perangkat R Studio untuk mengevaluasi tren publikasi, kolaborasi penulis, distribusi kata kunci, serta pemetaan tematik dan jaringan koeksistensi istilah. Hasil menunjukkan lonjakan publikasi pada 2024, dominasi jurnal seperti IEEE Access, serta kontribusi signifikan dari kelompok riset tertentu. Evolusi istilah kunci mengindikasikan pergeseran fokus dari isu teknis menuju penerapan praktis seperti optimization dan transfer learning. Visualisasi bibliometrik menegaskan bahwa ResNet tetap menjadi kerangka penting dalam ekosistem DL, meskipun mulai muncul integrasi dengan pendekatan baru. Studi ini memberikan kontribusi strategis berupa pemetaan komprehensif terhadap lanskap riset ResNet, yang relevan untuk pengembangan arsitektur jaringan saraf yang lebih adaptif dan aplikatif lintas disiplin.
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