Akbbar Sukman Pradhipta

Personal & career background

I was born in Sumbawa Besar, West Nusa Tenggara, and now live in Muara Bungo, Jambi. Outside work, I enjoy cooking, reading biographies and watching documentaries. To me, books are like time machines: they allow us to learn from other people’s lives and experiences.

I graduated from Sumbawa University of Technology in 2017 with a degree in Metallurgical Engineering. After graduating, I worked as a teaching assistant in mineral processing, which strengthened my understanding of mineral processing principles and plant operations.

Joining Datamine’s Production Accounting team was my first professional role as a metallurgist. Since then, I have developed practical expertise in configuring reliable metal accounting systems that reflect the requirements of individual mining operations and align with the AMIRA P754 Code of Practice.

Challenges, projects & learnings 

Can you describe a project you delivered at Datamine that best showcases your expertise?

One project that best demonstrates my expertise was the implementation of a statistical mass-balance solution for external streams within a major Indonesian mining operation’s production process.

The objective was to translate the plant flowsheet and the client’s metal accounting requirements into a structured model within Production Accounting. The solution reconciled information across the relevant process streams, improving the accuracy and reliability of daily and month-end production data.

The project brought together my knowledge of metallurgy, mineral processing, data validation and metal accounting.

What was your role in the project?

As the Functional Metallurgist, I was responsible for designing, configuring and validating the statistical mass-balance model.

I translated the metallurgical process into a structured model, defined the relevant process streams, developed the calculation logic and configured the reconciliation process. I also reviewed the reconciled results, investigated discrepancies and worked closely with the client to ensure that the model accurately represented the plant’s operations.

To validate the solution, I compared measurements from online instruments with the mass-balanced results. This helped confirm that the model was producing reliable outputs and identify areas requiring further investigation or adjustment.

What made this project challenging?

The most challenging aspect was identifying reliable shift-level assay data for the statistical mass balance. An unexpected issue arose when we discovered that the samples initially selected were not representative of the material processed during the corresponding shift. Using this information could therefore have produced misleading balanced results.

This required us to review the source, timing and representativeness of the assay data before proceeding with the reconciliation process.

The key lesson for other mining teams is that the quality of a mass balance depends on the quality of its inputs. Assay data must be accurate, appropriately timed and representative of the material being processed. Validating this information before performing the mass balance is essential for producing results that users can trust.

How did the project improve the client’s or operation’s way of working?

The project introduced a more structured and consistent workflow for validating shift-level production data.

Rather than relying solely on raw instrument readings and assay values, the team could use the statistical mass balance within Production Accounting to identify inconsistencies and generate more reliable daily production figures.

The new workflow involved reviewing the source data, running the mass balance, investigating discrepancies and approving the reconciled results for reporting. This improved the traceability and reliability of production information, enabling faster and more confident operational decision-making.

How does this project experience influence the way you support Datamine customers today?

This experience taught me the importance of understanding the client’s plant processes and verifying the quality of the source data before configuring a solution.

Today, I apply this approach by asking focused questions, reviewing flowsheets and data sources, and testing configurations using representative operational data. This allows me to identify potential risks early and ensure that each solution accurately reflects the client’s actual processes.

It also reinforces the principle that a technically correct configuration is only effective when it is built on reliable data and a clear understanding of the operation. This approach helps me deliver practical, dependable solutions that customers can confidently use for production accounting and decision-making.

Beyond the work

What do you enjoy doing outside work?

I enjoy cooking, reading biographies and watching documentaries. I am particularly drawn to books because they offer an opportunity to learn from the experiences, decisions and perspectives of people from different periods and backgrounds.