This study aims to analyze the influence of maintenance technique analysis on the increase of Gayo Arabica coffee production by farmers in Belang Gele, Bebesen District, Central Aceh Regency. Central Aceh Regency, especially the Belang Gele area, is known as one of the best producers of Arabica coffee in Indonesia, but farmers in this area face various challenges in increasing production, such as climate change, lack of maintenance technology, and pest and disease problems. Good maintenance techniques, including pruning, application of appropriate fertilizers, weed control, pest control, and plant shading, are expected to increase the production of high-quality coffee. This study uses a descriptive approach with data collection through field observations, interviews with coffee farmers, and analysis of the maintenance techniques applied. The results of the study show that the application of agroforestry-based maintenance techniques, the use of organic fertilizers, and efficient water management can increase the production yield of Gayo Arabica coffee. In addition, uncertain weather factors are also a major challenge that affects the quality and quantity of production output. Therefore, the results of this study are expected to provide practical solutions for farmers in increasing coffee production in a sustainable and competitive manner in the global market.

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.