Artificial Intelligence (AI) has currently become a cornerstone of modern life development. Through it, we gain various conveniences in executing technical operational activities. With an adequate supply of datasets, AI can learn user preferences while performing autonomous tasks.

We are now witnessing diverse implementations of AI across various sectors, ranging from system security (Cybersecurity) to the Agriculture Sector.

The Agriculture Sector is one that holds high potential and expansive value. With Indonesia’s vast landscape harboring rich biodiversity, the utilization of Natural Resources is highly attractive. This ranges from management for food, industry, and renewable energy, to conversion into valuable commodities like compost fertilizer.

One such abundant natural resource is the Citrus Leaf (also known as the Kaffir Lime Leaf), which is a highly sought-after commodity in the market, especially by culinary and beauty enthusiasts. However, for the majority of people, identifying Citrus leaves can be an obstacle, particularly when different species share a striking visual resemblance.

Building upon research based on Technology and AI in the Agriculture sector, a lecturer from the Informatics Engineering Department at Universitas Dian Nusantara (UNDIRA) has successfully implemented Deep Learning to accurately map Citrus leaf types and determine the quality of the desired variety, with the research focusing specifically on Citrus aurantifolia.

By utilizing the ResNet 50 architecture, the system employs visual learning to determine Citrus leaf types with high accuracy. For context, ResNet 50 is a collection of layered Convolutional Neural Networks (CNN) that rely on pattern-based and color gradient-based learning.

This research applies image capture via a camera, which is then sampled by the system. Within the system, by leveraging the ResNet 50 architecture trained on a large-scale dataset covering hundreds of thousands to millions of visual data points, Deep Learning is achieved, enabling the system to precisely identify target objects.

Furthermore, to optimize input, the gamma correction method is applied to standardize image quality. This approach allows the system to perform deep learning to accurately determine classifications, patterns, or objects. The success of this implementation underscores the strategic role of Informatics in birthing innovations that not only solve complex problems but also facilitate workflow standardization in various sectors, including Agriculture, Biological Studies, and research scopes.

This demonstrates the competence and prospects of Informatics Engineering, which plays a role not only in the IT world but also supports other sectors in developing technology-based solutions. Given the importance of developing IT competencies for the younger generation, Universitas Dian Nusantara (UNDIRA) is the answer for those interested in the world of technology development.

One of the Informatics Engineering study concentrations, Software Engineering, centers on equipping students for optimal software development. The integration of key courses such as Data Visualization and Programming supports a deep understanding of automation, serving as the primary foundation for designing intelligent systems that are efficient, standardized, and reliable.