Quantitative Analysis of Coffee Shop Operational Waste Using Python - Adyaraka Banyu Langit; Rai Ikhsan Wiratama (Publikasi Indonesia, 2026)
This research analyzed 13 months of daily operational waste data from a coffee shop in Jakarta, Indonesia, covering the period from January 2024 to January 2025. The data were collected from bar and kitchen operations across five waste categories: bar non-organic waste, bar organic waste, bar coffee grounds (ampas kopi), kitchen non-organic waste, and kitchen organic waste. This research employed a quantitative descriptive approach using secondary data from daily waste records to identify and summarize waste generation patterns without manipulation or intervention. After the data were loaded and organized using Microsoft Excel, descriptive statistics were calculated to identify patterns in daily waste generation. The results showed that the mean total daily waste was 14.46 kg, with a median value of 11.90 kg. A substantial reduction in waste generation was observed between the first three months (January to March 2024), which averaged 24.0 kg/day, and the remaining period, which averaged 12.0 kg/day, indicating improved operational efficiency over time. Organic waste accounted for 41.2% of total waste, while non-organic waste accounted for 58.8%. The two kitchen waste categories demonstrated a strong correlation (r = 0.659), indicating that they tended to increase and decrease simultaneously. No significant differences in waste generation were identified across days of the week (p = 0.903). This research demonstrates that basic data analysis using Python can transform simple daily operational records into valuable insights for business planning and waste management improvement.