Pengelolaan pengisian ulang uang tunai pada mesin Anjungan Tunai Mandiri (ATM) memerlukan perencanaan rute yang efisien untuk menghindari keterlambatan, menekan biaya operasional, dan meminimalkan risiko denda. Penelitian ini mengembangkan sistem perencanaan rute optimal menggunakan algoritma A-Star (A*) yang dikenal efektif dalam pencarian jalur terpendek. Studi kasus dilakukan di PT Usaha Garda Arta Branch Bekasi dengan memanfaatkan TomTom API sebagai penyedia data peta digital dan kondisi lalu lintas terkini. Sistem dirancang untuk menentukan urutan kunjungan ATM berdasarkan jarak dan waktu tempuh optimal, lalu dibandingkan dengan metode manual yang selama ini digunakan perusahaan. Pengembangan dilakukan menggunakan pendekatan Agile melalui tahap analisis kebutuhan, perancangan, implementasi, dan pengujian dengan data riil lokasi ATM. Hasil penelitian menunjukkan bahwa sistem berbasis A-Star mampu mengoptimalkan rute distribusi secara signifikan, menghasilkan penghematan waktu dan biaya operasional. Selain itu, sistem ini juga dapat mengurangi risiko keterlambatan yang berpotensi menyebabkan denda. Integrasi A-Star dan TomTom API terbukti memberikan solusi logistik yang lebih efisien dan adaptif terhadap kondisi nyata di lapangan, serta mendukung peningkatan kinerja operasional dalam layanan keuangan berbasis teknologi.

Automated Teller Machines (ATMs) are critical banking infrastructures that require a constant supply of cash to maintain service availability. One of the key challenges in ATM management is planning efficient cash replenishment routes, as inefficient routing can lead to increased travel time, higher operational costs, and the risk of delays that may incur financial penalties. This study proposes a route planning system using the A Star (A*) algorithm, known for its effectiveness in finding optimal paths. A case study was conducted at PT Usaha Garda Arta, Branch Bekasi, utilizing the TomTom API for real-time digital mapping and traffic data. The A-Star algorithm was applied to calculate the most efficient sequence of ATM visits based on travel distance and time, and the outcomes were compared to the company’s current manual planning methods. System development followed the Agile methodology, including requirement analysis, system design, implementation, and testing using real ATM location data. Results indicate that the proposed system generates more efficient routes than traditional methods, achieving time savings and reducing operational costs. Moreover, the system lowers the risk of late arrivals that can result in financial penalties. The integration of the A-Star algorithm with TomTom API demonstrates a practical and adaptive solution for optimizing ATM cash replenishment logistics, supporting advancements in efficient financial technology systems.