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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world. In order to lower production costs, Indonesian producers and retailers often mix high-priced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method’s development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, ResNet-50, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best CNN model using ResNet-50 with optimizer type RMSProp and learning rate 0.0001, providing an accuracy average of up to 85.00%. Later, the CNN model can be used to establish a real-time, non-destructive, rapid, and precise purity detection system.

Coffeeshop Boy's is one of the cafes that has survived the Large-Scale Social Restrictions (LSSR) and Enforcement of Restrictions on Community Activities (ERCA) policies during the pandemic. The power of investors and the quality of coffee are the reasons the Coffeeshop Boy's stay afloat. Coffee is a refreshing ingredient that comes from annual plantations. In Indonesia, farmers cultivate Robusta coffee and Arabica coffee, the difference between these two types of coffee can certainly be known from the taste. This research uses the descriptive qualitative method. Coffeeshop Boy's was originally a bottled coffee company that later turned into a home cafe. The LSSR and ERCA policies make this business must implement the right marketing strategy with the SWOT analysis method to find out the appropriate marketing strategy for this business in maintaining its business in the midst of a pandemic. The results of the SWOT analysis have shown that the total score of the IFAS matrix for strength items is 3.956 while for weakness items are 1.678. The results of the SWOT analysis have shown that the total score of the EFAS matrix for opportunity items is 3.241 while for threats items are 3.359. Based on the coordinates of the IFAS and EFAS matrices, Coffeeshop boy's is in quadrant 2 (ST Strategy) with a product diversification strategy. This has shown that Coffeeshop boy's can face the various threats that exist by taking advantage of the strengths they have. The ST strategy that needs to be implemented is to create new product innovations, provide special offers for new consumers, consumers who are having birthdays, or booking meetings, extend operational time, and create events to strengthen Coffeeshop Boy's branding.

Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network (ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency. The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3 outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442.