이미지: 원문 영상 캡처
Summary
- The Microsoft Elevate Training Center gave Indonesian non-IT majors and users of low-spec devices access to AI and cloud learning opportunities.
- Yusuf uses machine learning to analyze flood-risk areas, while Rano used GitHub Copilot to complete a camera-and-voice app for the visually impaired.
- Their stories show that cloud infrastructure and community access—rather than location in major tech hubs or access to the latest equipment—can help close the digital divide.
From a Forestry Major to Flood-Prediction AI Research
Muhammad Yusuf Fadhel Marwiji, from Makassar, majored in forestry, where he learned to work with spatial data through fieldwork and GIS (Geographic Information Systems). For him, spatial data was never just about maps or visualization—it was a way to turn the condition of a given area into measurable information. Geospatial work that requires reviewing multiple environmental variables at once, such as elevation, slope, and rainfall, had the drawback of being time-consuming when done manually.
Encountering machine learning opened up new possibilities for Yusuf. In his master's research, he explored using machine learning to analyze flood-risk areas. By processing multiple environmental variables simultaneously and detecting patterns that would be difficult to spot manually, he was able to identify flood-prone areas more efficiently. He explained that the value of machine learning lies not just in faster analysis, but in making preliminary conclusions more reliable before field verification. "Before, I had to analyze data point by point, but now I can process much faster even with many data points or variables," Yusuf said.
The Microsoft Elevate Training Center became the catalyst for turning this interest into structured skill. Through the program, Yusuf gained a broad understanding of the Microsoft ecosystem, from Microsoft Excel for tabular data analysis to the potential of Microsoft Azure as a platform supporting automation and cloud-based workflows. He predicted that geospatial AI (GeoAI) will become increasingly important in environmental and industrial fields, and expressed hope that young people outside major tech hubs could also receive skills training aligned with industry demand.
When an Old Laptop Meets the Cloud
Emerhealeano Rusmin Ngefo, known as Rano, from Tangerang, struggled to picture a concrete path to college during high school due to his family's financial difficulties. In the aftermath of the pandemic, his family had to sell off various assets to make ends meet. Thanks to strong encouragement from his school's guidance counselor, he was able to enroll in the Department of Information and Communication at Sultan Ageng Tirtayasa University through SNBP, a national merit-based admission track. A conversation with his IT teacher, in which he learned that machine learning could contribute to healthcare, sparked his interest in technology.
Alongside his studies, Rano completed a backend developer internship, worked as a Dicoding Community Builder, and founded DiGiat Group, an initiative focused on developing digital products and services. But he had to do all of this with a single aging laptop. Tasks that could have been finished quickly took far longer because the laptop would slow down, overheat, or restart without warning. "I remind myself why I'm studying—it's for my future and my family. The laptop shouldn't be the reason I stop," Rano said.
After being introduced to Microsoft Fabric and Azure through Microsoft Elevate, Rano came to see the cloud not as an abstract concept but as a practical way to overcome device limitations. His laptop had no GPU, so training machine learning models locally would have taken far longer than expected. He realized that a cloud-based approach would let him handle data processing, transformation, model experimentation, and data workflow development on more flexible computing infrastructure—a shift in perspective that showed him device specs need not be an absolute barrier to learning and project work.
Discovering Data Engineering Through an ETL Project
The experience that left the deepest impression on Rano during the program was his final ETL project. He handled the entire process of extracting, cleaning, and transforming data into a form suitable for business analysis or model training. Through this, he came to understand that AI development doesn't depend on the model alone. Data needs to be well-prepared, relevant, and structured in order to yield meaningful insights, support informed decision-making, and allow AI systems to learn effectively. This experience fundamentally changed how he viewed the data engineering profession.
After completing Elevate, Rano began actively incorporating GitHub Copilot into his everyday projects. As an AI assistant integrated directly into the coding process, GitHub Copilot offers context-aware code suggestions, meaning he doesn't have to write every line of code from scratch. Tasks that once took considerable time from initial planning to implementation could now move much faster. This let Rano focus more on the concepts and logic behind his solutions, shifting his workflow toward using AI to turn ideas into working code. One result of this was an app combining a camera and voice output to help visually impaired users recognize objects around them, which he was able to complete in a relatively short period with the help of GitHub Copilot.
Accessibility as Equal Opportunity
Yusuf and Rano started from very different places. Yusuf found his intersection with geospatial AI through hands-on forestry fieldwork, while Rano broadened his learning through the cloud despite financial constraints and an aging device. Both cases show that a digital career can be built with the right learning opportunities and tools, even without being based in a major tech hub or having access to the latest equipment.
Rano defined the meaning of accessibility this way: "Accessibility is the key to equality and opportunity. With accessibility, students from any background can have an equal chance to learn, innovate, and shape their future." Their stories show that Indonesia's digital talent doesn't only emerge from ideal environments—it can also grow in a lab in Makassar or in front of a modest laptop in Tangerang. The source did not disclose further details on program usage, such as costs or eligible countries.
