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Economic activity is important in keeping the country's economy running well. One of these economic activities is the production process in which individual or group activities in managing raw materials into goods are carried out in order to increase the use value of the goods. Economic activities, especially production, can run well if the constraining variables can be overcome well. In this paper, the author analyzes what factors inhibit the development of the Kenanga Coffee business located in Lubuk Garam village, Siak Kecil Regency, Bengkalis Regency, Riau Province by direct observation in the field and interviews with related parties.

Coffee is an agricultural product that not only functions as a fresh drink, but also comes from an annual plant. Indonesia is known as one of the largest countries in coffee production in the world after Brazil, Vietnam and Colombia in 2017. There are two of the most commonly known varieties of coffee, namely arabica coffee (Coffea arabica) and robusta coffee (Coffea canephora). Due to the similarities between the two types of coffee beans, many people, especially those who are not experienced in the world of coffee, have difficulty telling the difference. Therefore, we need a tool that can help overcome this problem, such as smartengine which can classify arabica and robusta coffee beans. The development of this smartengine follows the Software Developer Life Cycle method using a spiral approach which involves several cycle stages. The first stage involves creating a deep learning model using the Deep Learning Life Cycle method which consists of several steps. In the second stage, deep learning models are provided as a service that can be used by other applications through application programming interfaces (APIs). For smartengine implementation, Google Colab with Keras API and TensorFlow backend is used. This smartengine has the ability to detect coffee beans and also allows the retrain process if needed. Testing is carried out using the blackbox method, where the feature functionality of the smartengine is tested. This research succeeded in developing a smartengine that can detect Arabica and Robusta coffee beans.

The volume of coffee exported from Indonesia between 2010 and 2020 will be reviewed in this study along with the influence of the variables coffee production, land area and exchange rate. In this study the influence of independent factors on dependent factors was assessed using multiple linear regression analysis techniques. The results of the multiple linear regression analysis of this study revealed that Indonesia's coffee exports were negatively affected but not significantly by variables related to coffee production. Indonesian coffee exports were negatively and insignificantly affected by changes in land area. Conversely, the exchange rate (exchange rate) has a positive but also insignificant impact on the volume of coffee exported from Indonesia.