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Digitalization is an effort to utilize digital-based technology to transform previously conventional work systems into more efficient and effective ones, including in the management of micro, small, and medium enterprises (MSMEs). This study aims to apply digitalization to corn-based coffe MSMEs in Talang Balai Baru 1 Village, Tanjung Raja District, Ogan Ilir Regency. The research method used is a descriptive qualitative approach with data collection techniques through interviews, observation, and documentation. The research findings indicate that the production and marketing activities of corn coffe MSMEs still use conventional methods. The implementation of digitaization has not been optimally implemented due to the low digital literacy of business owners and limited time in business managemen. Nevertheless, the initial implementation of digitalization through the use of social media and online sales platforms has had a positive impact in the form of expanding market reach and increasing ease of communication between producers and consumers. Thus, the digitalization of corn coffe MSME products has great potential to increase competitiveness and business sustainability if implemented gradually and continuously. .

The Community Partnership Program (PKM) at Rumah Kopi Banjarsengon (RKB) aims to improve product quality and coffee marketing according to consumer needs. Key activities include production observation, assistance with laboratory testing for nutritional value and shelf life, food safety education, digital marketing training, and the delivery of a 5 kg roasting machine. These efforts have successfully increased production capacity and product quality standards through informative labels. Digital marketing has seen significant progress, with increased social media engagement and broader market reach. Consumer satisfaction surveys have risen from 80 to 97 on a scale of 100. Overall, this PKM program not only supports sustainable coffee business development but also contributes to improving partner competency and strengthening product competitiveness in both local and export markets. RKB is expected to consistently implement digital marketing strategies and product innovation for long-term success. Going forward, RKB is expected to continue implementing digital marketing strategies and product innovation to ensure sustainable business success.

This study aims to analyze the influence of interest rates, exchange rates, and coffee production on the value of coffee exports in South Sulawesi. The background of this study is based on the condition of South Sulawesi coffee exports which have experienced significant fluctuations in recent years despite coffee production tending to increase. This study uses a quantitative approach with time series data for the period 2009-2023 sourced from the World Bank, International Monetary Fund and the Directorate General of Plantations, the Food Crops, Horticulture and Plantation Service of South Sulawesi Province. Data analysis was conducted using multiple linear regression through the EViews 12 application with the classical assumption test as a model prerequisite. The results show that partially interest rates have a significant effect on coffee exports, while exchange rates and coffee production do not have a significant effect. Simultaneously, the three independent variables do not have a significant effect on the value of coffee exports. This finding indicates that external factors, especially interest rates, are more dominant in determining the performance of South Sulawesi coffee exports than internal factors of production and exchange rates.

Coffee husk is an agro-industrial waste with significant potential to be utilized as a renewable energy source through the fast pyrolysis process. This study aims to analyze and optimize gas production from the fast pyrolysis of coffee husk biomass using a screw reactor through single-particle-based Computational Fluid Dynamics (CFD) simulations. The simulations were conducted by varying the operating temperature at 500°C, 600°C, and 700°C to examine pressure distribution, heat transfer, particle temperature, and the formation of pyrolysis products, namely bio-oil, biogas, and biochar. The modeling was performed using COMSOL Multiphysics 6.2 with a numerical approach to represent thermal phenomena and biomass decomposition reactions during the pyrolysis process. The simulation results indicate that increasing temperature significantly affects the rate of heat transfer and the temperature distribution of coffee husk particles. At 600°C, heat transfer and temperature distribution are more uniform compared to 500°C, although heating at the particle core is not yet fully optimal. The pressure distribution shows a stable flow of pyrolysis gas from the bottom to the top of the reactor. In terms of products, increasing temperature leads to a reduction in biochar and bio-oil formation due to the occurrence of secondary reactions, while biogas production increases. The highest gas production is achieved at 700°C, indicating the most optimal condition for maximizing gas yield from fast pyrolysis. Therefore, single-particle-based CFD simulation can be used as an effective tool to understand pyrolysis mechanisms and optimize process parameters in a screw reactor.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

Kopirates Maumere is a local micro, small, and medium enterprise (MSME) engaged in the processing and marketing of specialty coffee products from Sikka Regency. This study aims to analyze the management strategies implemented by the MSME based on interviews with the business owner. The research focuses on product strategy, marketing, operations, business development, and the challenges faced in running the MSME amid increasingly competitive market conditions. The research method employs a descriptive qualitative approach through in-depth interviews and direct observation. The results indicate that Kopirates Maumere applies a product differentiation strategy by highlighting the quality of local coffee, supported by increasingly developed digital marketing. The MSME also strives for operational efficiency despite facing challenges related to limited capital and production equipment. This study concludes that adaptive management strategies based on local potential are crucial for maintaining MSME sustainability. Furthermore, improved managerial capacity along with support from the government and local communities is needed to strengthen the MSME’s position in a broader market.