Pengembangan Digital Acquisition Product Sebagai Layanan Pelanggan di Industri Perbankan - Intan Ardiani Putri; Jap Tji Beng; Rahmiyana Nurkholiza; Vienchenzia Oeyta Dwitama Dinatha (Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, 2026)
This study evaluates the success of Digital Acquisition Product development through the implementation of the Quick Response Code Indonesian Standard payment system at Bank X using a computational data analytics approach. Most previous studies have focused on consumer adoption through perception-based surveys, leaving a research gap in evaluating the operational capacity of internal banking systems. This study processes real transactional Big Data from 2023 to April 2026 using the Python programming language within the Google Colab environment. The computational stages include data wrangling using the Pandas library, data structure transformation (melting), missing value handling, and the development of custom algorithms to standardize time-log formats. The novelty of this study lies in the application of computational scripts to validate system performance longitudinally. The computational analysis of the 2026 projected data indicates high system scalability, as evidenced by the dominance of transaction loads through digital channels, accounting for 96.1 percent of total transactions compared to physical infrastructure. The study concludes that the Digital Acquisition Product architecture has proven robust in handling increasing data traffic and recommends that banking institutions begin integrating predictive algorithms to ensure the sustainability of the digital server ecosystem.Keywords: Data Analytics; System Evaluation; Descriptive Computing; Python; Digital Acquisition Product.AbstrakPenelitian ini mengevaluasi keberhasilan pengembangan Digital Acquisition Product melalui implementasi sistem pembayaran Quick Response Code Indonesian Standard pada Bank X menggunakan pendekatan data analytics komputasional. Sebagian besar penelitian terdahulu berfokus pada adopsi konsumen melalui survei persepsi, sehingga menyisakan kesenjangan analisis pada evaluasi kapasitas operasional sistem internal perbankan. Penelitian ini memproses data transaksional riil (Big Data) periode 2023 hingga April 2026 menggunakan bahasa pemrograman Python di lingkungan Google Colab. Tahapan komputasi mencakup data wrangling menggunakan pustaka Pandas, transformasi struktur data (melting), penanganan missing values, serta pembuatan algoritma kustom untuk standardisasi format log waktu. Kebaruan studi ini terletak pada penerapan script komputasi untuk memvalidasi performa sistem secara longitudinal. Hasil analisis komputasi pada proyeksi data 2026 menunjukkan skalabilitas sistem yang tinggi, ditandai dengan dominasi beban transaksi pada kanal digital sebesar 96,1 persen dibandingkan infrastruktur fisik. Penelitian ini menyimpulkan bahwa pengembangan arsitektur Digital Acquisition Product terbukti tangguh dalam menangani lonjakan traffic data, dan perbankan disarankan mulai mengintegrasikan algoritma prediktif untuk menjaga keberlanjutan ekosistem server digital.