Sistem Informasi Penjualan Berbasis Business Intelligence dengan Fitur Rekomendasi Promosi pada PT Bon Kreasi Indonesia

Abstract

This study designs and implements a Business Intelligence platform named BitesBI to address the operational challenges of PT Bon Kreasi Indonesia (Bonbonbites). The primary issues identified are product naming inconsistencies in the exported Point of Sales (POS) Olsera data—which generate dirty, unconsolidated historical data—and the absence of an analytical dashboard that forces management to determine promotion strategies based on intuition rather than empirical data. BitesBI was developed using the Rapid Application Development (RAD) method, implemented in Python 3.11 with the Flask framework. The system employs an Extract, Transform, Load (ETL) process equipped with a Mapping Alias feature to standardize Stock Keeping Unit (SKU) naming inconsistencies. Cleansed data is subsequently processed by a Rule-Based Engine to automatically generate next-period sales projection estimates (T+1) and objective promotion strategy recommendations per product. Black Box Testing validated all five functional scenarios as correct, while White Box Testing verified that the ETL pipeline and Rule-Based Engine algorithms are free from logical defects. The implementation of this system successfully transforms management's tactical decision-making process from intuition-based to fully data-driven.