Identifying Knowledge Gaps in Geological Information Understanding within the POI–ATE Process at PT Freeport Indonesia

Andreas Yudha Sugiyanto; Utomo Sarjono Putro

doi:10.59261/inkubis.v8i4.558

Authors

Andreas Yudha Sugiyanto Institut Teknologi Bandung, Indonesia
Utomo Sarjono Putro Institut Teknologi Bandung, Indonesia

DOI:

https://doi.org/10.59261/inkubis.v8i4.558

Keywords:

Stakeholder Understanding, Geological Information, POI–ATE Process, Perception Patterns, Knowledge Alignment, SMART

Abstract

Background: Geological information supports technical and operational decision-making in underground mining; however, its interpretation may vary across stakeholder groups. At PT Freeport Indonesia (PTFI), the Plan of Intent (POI)–Approval to Excavate (ATE) process requires personnel from Geology, Geotech, and Engineering to review geological information before excavation in the Deep Mill Level Zone (DMLZ).

Objective: This study examines patterns in stakeholder perceptions to identify areas of knowledge alignment and gaps within the POI–ATE process.

Methods: An exploratory quantitative survey was conducted with 38 respondents from Geology (information sender), Geotech, and Engineering (information receivers). Responses were analyzed using descriptive statistics, independent-samples t-tests, Hedges' g effect sizes, and item-level gap analysis, with the findings interpreted through a Supported–Tentative–Preliminary framework. Subsequently, semi-structured interviews with six decision-makers were conducted to develop and prioritize improvement alternatives using the Simple Multi-Attribute Rating Technique (SMART).

Results: The findings indicated moderate-to-high perceptions across five dimensions, with no statistically significant differences between information senders and receivers and predominantly very small to small effect sizes. Findings concerning understanding of report content, the need for discussion, delays, rework, and stakeholder-oriented reporting were well supported, whereas findings concerning clarification at the approval stage, report complexity, and systematic evaluation remained preliminary.

Conclusion: SMART ranked the Clear and Decision-Focused ATE Report (A1) first among the four business-solution alternatives, making it the highest-priority alternative for pilot evaluation; this ranking remained stable across seven weighting scenarios.

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References

Abubakar, A. M., Elrehail, H., Alatailat, M. A., & Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104–114. https://doi.org/10.1016/j.jik.2017.07.003

Alkharusi, H. (2022). A descriptive analysis and interpretation of data from Likert scales in educational and psychological research. Indian Journal of Psychology and Education, 12(2), 13–16.

Arslan, E. (2022). Validity and reliability in qualitative research. Pamukkale University Journal of Social Sciences Institute, 51(1), 395–407.

Brydges, C. R. (2019). Effect size guidelines, sample size calculations, and statistical power in gerontology. Innovation in Aging, 3(4), igz036. https://doi.org/10.1093/geroni/igz036

Carlson, R. (2025). Understanding geologic uncertainty in mining studies. SEG Compilations, 16, 187–195. https://doi.org/10.5382/COM.16.15

Dhungana, A., Doyle, E. E. H., Prasanna, R., & McDonald, G. (2025). From scientific models to decisions: Exploring uncertainty communication gaps between scientists and decision-makers. Environment Systems and Decisions, 45, 44. https://doi.org/10.1007/s10669-025-10039-w

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11

Freeport-McMoRan Inc. (2022). Grasberg district. Freeport-McMoRan Inc. https://www.fcx.com/sites/fcx/files/images/operations/GrasbergDistrict.pdf

Iacobucci, D., Popovich, D. L., Moon, S., & Román, S. (2023). How to calculate, use, and report variance explained effect size indices and not die trying. Journal of Consumer Psychology, 33(1), 45–61.

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. https://doi.org/10.3389/fpsyg.2013.00863

Lee, K. M., & Kim, K. (2026). An analysis of educational satisfaction using sentiment analysis: correlation analysis between Likert-scale evaluation and descriptive feedback. Human Resource Development International, 29(1), 98–122.

Marsh, K., IJzerman, M., Thokala, P., Baltussen, R., Boysen, M., Kaló, Z., Lönngren, T., Mussen, F., Peacock, S., Watkins, J., & Devlin, N. B. (2016). Multiple criteria decision analysis for health care decision making—emerging good practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force. Value in Health, 19(2), 125–137. https://doi.org/10.1016/j.jval.2015.12.016

Pérez-Díaz, L., Alcalde, J., & Bond, C. E. (2020). Introduction: Handling uncertainty in the geosciences: Identification, mitigation and communication. Solid Earth, 11(3), 889–897. https://doi.org/10.5194/se-11-889-2020

Prayudi, Susilo, M. E., & Probosari, N. (2025). Developing a crisis communication model for the mining industry. RSF Conference Proceeding Series: Business, Management and Social Science, 5(2), 379–388. https://doi.org/10.31098/bmss.v5i2.990

Sieber, M. J., Brink, F. J., Leys, C., King, P. L., & Henley, R. W. (2020). Prograde and retrograde metasomatic reactions in mineralised magnesium-silicate skarn in the Cu–Au Ertsberg East Skarn System, Ertsberg, Papua Province, Indonesia. Ore Geology Reviews, 125, 103697. https://doi.org/10.1016/j.oregeorev.2020.103697

Sonntag Hoerlle, G., Remus, M. V. D., Müller, T., Piazolo, S., Lana, C., & Sorger, D. (2022). Metasomatic reactions triggered by localized and episodic fluid flux record multistage intrusion history: An example from the syntectonic Caçapava do Sul Granitic Complex, Southern Brazil. Southern Brazil.

Stebbins, R. A. (2001). Exploratory research in the social sciences (Vol. 48). Sage Publications.

Stebbins, R. A. (2025). Exploratory research in the social sciences. SAGE publications.

Sürücü, L., & Maslakçı, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), 2694–2726. https://doi.org/10.15295/bmij.v8i3.1540

Taherdoost, H., & Mohebi, A. (2024). Using SMART method for multi-criteria decision making: Applications, advantages, and limitations. Archives of Advanced Engineering Science, 2(4), 190–197. https://doi.org/10.47852/bonviewAAES42022765

van Schaik, L. F., Coves, A. F., Grimm, S. E., Ramaekers, B., van Til, J. A., van Harten, W. H., Joore, M. A., & Retèl, V. P. (2026). Perspectives in the value assessment of comprehensive genomic profiling: A multi-criteria decision analysis in NSCLC among stakeholders. Journal of Cancer Policy, 100817.

Vveinhardt, J., Sorochak, O., Gvozd, M., & Deikus, M. (2026). The impact of knowledge management on organisational effectiveness: A conceptual model. In Digital technologies in education II: Selected cases (pp. 109–128). Springer.

Yagin, F. H., Pinar, A., & de Sousa Fernandes, M. S. (2024). Statistical effect sizes in sports science. Journal of Exercise Science & Physical Activity Reviews, 2(1), 164–171.

Zieliński, G. (2026). Getting to know pain effect sizes–guidelines for effect size and sample size in global pain research. Archives of Physical Medicine and Rehabilitation.

Zrineh, A. (2026). Purposive sampling in research: Types, justifications, applications, and limitations. Journal of Medicine, Surgery, and Public Health, 100249.