SISTEM INFORMASI REKOMENDASI PEMILIHAN PENDINGIN RUANGAN KOMERSIAL DENGAN MENGGUNAKAN METODE AGILE

Pemilihan chiller komersial sangat memengaruhi biaya operasional bangunan. Metode lama sering tidak efisien karena tidak memperhitungkan beban aktual. Penelitian ini mengembangkan Sistem Informasi Rekomendasi Pemilihan Pendingin Ruangan Komersial yang membandingkan chiller berdasarkan data profil beban aktual gedung dan kinerja part-load (IPLV/NPLV). Sistem ini dibuat dengan metodologi Agile (Scrum). Arsitekturnya menggunakan Vue.js (frontend), Python Django (backend), dan PostgreSQL (database), semua di deploy dalam Docker. Hasil pengujian menunjukkan sistem ini akurat dan responsif dalam menghitung konsumsi energi dan biaya. Membuktikan kemampuannya memberikan rekomendasi chiller paling efisien secara objektif. Implementasi Agile efektif dalam adaptasi dan pengiriman produk. Sistem ini diharapkan menjadi alat bantu penting untuk efisiensi energi di bangunan komersial.
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Selecting a commercial chiller is a critical decision significantly impacting a building's initial investment and long-term operational costs. Traditional methods often rely on static data or experience, proving suboptimal in identifying the most energy-efficient solutions. This research aims to develop a Commercial Chiller Selection Recommendation Information System that compares various chiller alternatives based on actual building load profile data and chiller performance under part-load conditions (IPLV/NPLV). The system is developed using the Agile (Scrum) methodology, enabling iterative and adaptive development. Its architecture adopts a client-server model, utilizing Vue.js for an interactive user interface (frontend), Python Django as the backend for managing business logic and energy efficiency calculations, and PostgreSQL as the database for structured data storage. All system components are deployed within a Docker environment to ensure consistency and portability. Functional and non-functional testing results demonstrate that the system accurately and responsively processes large load profile data, performing energy consumption calculations and operational cost estimations. A simulated case study using actual office building load profile data from Sidoarjo, East Java, successfully showcased the system's ability to provide objective, data-driven chiller recommendations, identifying the most cost-efficient option. The Agile implementation proved effective in adapting to changing requirements and delivering functional product increments regularly. This system is expected to be a strategic tool for HVAC professionals and building owners in making more informed decisions and supporting energy efficiency efforts.