This research aims to determine marketing strategies through SWOT analysis with a focus on segmentation, market position, targeting and marketing mix, which aims to increase consumer buying interest at Restaurant Garuda. The research results show that Restaurant Garuda implements various marketing mix strategies that influence the SWOT analysis, placing it in quadrant I (SO quadrant). This indicates a successful implementation of the odds and profits strategy, although with a slight difference of 2.4:2.95. Garuda Restaurant emphasizes the aspects of price, product, location and physical form of the building as the main factors that stimulate consumers' buying interest to try and remain loyal to their products. To maintain and increase consumer buying interest, it is recommended that Restaurant Garuda adopt product development strategies, improve brand image and maintain price stability. By implementing these strategies, it is hoped that Restaurant Garuda can maintain and increase their market share and customer satisfaction.

The eyes are crucial tools for human observation and perception, facilitating various tasks in daily life. Individuals, including those with visual impairments or blindness, engage in currency transactions, posing challenges in recognizing notes and preventing mishaps with counterfeit money. Despite government efforts, features like embossing on banknotes have limited effectiveness due to the circulated currency's disheveled condition. Addressing the visually impaired community's needs is imperative. An innovative solution, the "all-in-one smart white cane," integrated with machine learning supports daily activities, enhancing independence for visually impaired individuals. The YOLOv8 algorithm is employed for the precise detection of monetary denominations, subsequently recorded through a camera and seamlessly integrated into a smart cane, resulting in a consolidated device. This device, designed with standout features, excels in detecting Indonesian Rupiah banknote denominations. Detection performance testing, incorporating methods like object rotation, utilized a dataset divided into training (70%), validation (20%), and test (10%) segments. Modifications to contrast and variability rotation are essential in the context of real-time nomination recognition. These adjustments are implemented to ensure accurate and swift identification in dynamic, real-world scenarios. Testing results reveal a 99% average accuracy in recognizing currency note denominations, presenting an effective solution for the visually impaired community.