Pengembangan Sistem Rekomendasi Merchandise K-Pop dengan Content-Based Filtering dan Scraping Data: Indonesia - Edmond Sorensen; Wilfridus Bambang Triadi Handaya; Bekty Tandaningtyas Sundoro (Jurnal Informatika Atma Jogja, 2026)
The rapid growth of Hallyu and K-Pop has increased demand for merchandise, but Hearteuhearteu Store still determines purchases manually without systematic processing of product performance data, resulting in the risk of inaccurate procurement. This study developed a K-Pop merchandise recommendation system based on Shopee Seller Center data to help sellers determine the type and quantity of goods objectively and structurally, with popularity trends as support. The system utilizes product performance data to generate relevant merchandise recommendations and supports dynamic updates to recommendations based on the latest data. System evaluation results shows that K = 5 provides the best performance compared to other parameters, with Coverage 0.983333 and Average Distance 0.345484. Additionally, black box testing on 31 respondents achieved a 100% success rate, and usability scored 4.9687, so the system is considered accurate, effective, and easy to use in supporting store purchasing decisions.   Pesatnya perkembangan Hallyu dan K-Pop meningkatkan permintaan merchandise, namun Toko Hearteuhearteu masih menentukan pembelian secara manual tanpa pengolahan data performa produk yang sistematis, sehingga berisiko terjadi ketidaktepatan pengadaan. Penelitian ini mengembangkan sistem rekomendasi merchandise K-Pop berbasis data Shopee Seller Center untuk membantu penjual menentukan jenis dan jumlah barang secara objektif dan terstruktur, dengan tren popularitas sebagai pendukung. Evaluasi menunjukkan K = 5 memberikan performa terbaik dengan Coverage 0,983333 dan Average Distance 0,345484. Pengujian black box pada 31 responden mencapai tingkat keberhasilan 100%, serta usability memperoleh skor 4,9687, sehingga sistem dinilai akurat, efektif, dan mudah digunakan dalam mendukung keputusan pembelian toko.