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Schedule Interview NowMy name is Nungga S. and I have over 0 years of experience in the tech industry. I specialize in the following technologies: Artificial Intelligence, Data Analysis, Data Extraction, Machine Learning, Machine Learning Model, etc.. I hold a degree in Bachelor's degree. Some of the notable projects I've worked on include: Seismic Fault Interpretation Deep Learning using (CNN), Early Awareness and Sensitivity to TB Symptoms, Redesign Website Company, Deep Learning Approaches for seismic GR Predictions, PINN Models for cases in between seismic data and well logs data. I am based in Jakarta, Indonesia. I've successfully completed 5 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
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BBPMGB Lemigas
Seismic fault interpretation is crucial in hydrocarbon exploration, traditionally relying on manual analysis of seismic profiles, which is time-consuming and prone to bias. This project employs Convol
Build Awareness to campaign for Indonesian as rank third in the world with many cases Tuberculosis (TB). We make dashboard with contain informations such as habitual to impact TB, Early Diagnose, Cost
Redesign website by based on approach client, we attached Figma design and implemented in the website https://victorindokimiatama.com/
Predicting gamma-ray (GR) logs from seismic data is vital for understanding subsurface properties, especially in areas with limited well data. This study explores machine learning techniques to enhanc
Physics-Informed Neural Networks (PINNs) combine machine learning with physical laws to improve seismic inversion. Instead of traditional step-by-step methods, PINNs use a neural network to estimate s
Bachelor's degree in Geophysics Engineering
2018-01-01-2023-01-01