Implementation of the Analytic Hierarchy Process (AHP) to Determine Key Performance Indicator (KPI) Weights for Steam Turbine Power Plant Using Python
Abstract
The optimization of Key Performance Indicators (KPIs) in steam turbine power plants is crucial for enhancing operational efficiency in the palm oil processing industry. This study applies the Analytic Hierarchy Process (AHP) to determine the relative weights of KPIs, thereby supporting data-driven decision making for performance improvement. Four critical KPIs were evaluated through pairwise comparisons expertise. A Python based computational model was developed to automate AHP calculations, ensuring accuracy and efficiency in deriving priority weights. This study reveals power output (47.16%) is the most significant KPI, followed by availability factor (38.58%), steam consumption (9.69%), and capacity factor (4.58%). The consistency ratio (CR) for all expert judgments was below 0.10, validating the reliability of the AHP outcomes. This research demonstrates that integrating AHP with Python programming provides a robust framework for KPI prioritization. The findings offer practical insights for industry stakeholders to optimize steam turbine performance and reduce operational inefficiencies.
##Keywords:##
AHP, KPIs, Steam Turbine, Python Programming, Decision-Making.
Published
Mar 30, 2026
How to Cite
ANGGRIAWAN, Akbar; NAZARUDDIN, Nazaruddin; SUSILAWATI, Anita.
Implementation of the Analytic Hierarchy Process (AHP) to Determine Key Performance Indicator (KPI) Weights for Steam Turbine Power Plant Using Python.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 70, n. 1, p. 32-42, mar. 2026.
ISSN 2527-6085.
Available at: <https://isomase.org/Journals/index.php/jomase/article/view/582>. Date accessed: 25 apr. 2026.
doi: http://dx.doi.org/10.36842/jomase.v70i1.582.
References
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[4] Emawan, D., Pratama, A. T. & Nasution, H. (2021). Application of analytic hierarchy process (AHP) to develop the weighting of key performance indicators on gas engine power plants. Proceedings of the Conference on Management Engineering and Industry, 3(4), 18–23. https://doi.org/10.33555/cmei.v3i4.90.
[5] Yadav, S., Srivastava, A. K. & Singh, R. S. (2015). Selection and ranking of multifaceted criteria for the prioritization of most appropriate biomass energy sources for the production of renewable energy in Indian perspective using analytic hierarchy process. International Journal of Engineering Technology Science and Research, 2 (Special Issue), 89–98.
[6] Meri, M. & Dila, V. (2018). Penentuan prioritas KPI untuk performance measurement pemeliharaan PLTA menggunakan metode analytical hierarchy process (AHP). Jurteksi, 5(1). https://doi.org/10.33330/jurteksi.v5i1.39.
[7] Chen, X. (2022). Deaerator system pressure and liquid level PID regulation control strategy. Highlights in Science, Engineering and Technology, 27, 133–145. https://doi.org/10.54097/hset.v27i.3730.
[8] Soleh, M., Hidayat, Y. & Abidin, Z. (2019). Co-firing RDF in CFB boiler power plant. In Proceedings of the 2019 International Conference on Technology and Policy in Electric Power and Energy (TPEPE). https://doi.org/10.1109/IEEECONF48524.2019.9102591.
[9] Elie, T. (2016). Boiler fuels, emissions and efficiency.
[10] Oprea, S. V. & Bâra, A. (2017). Key technical performance indicators for power plants. In Recent improvements of power plants management and technology. https://doi.org/10.5772/67858.
[11] Gunawan, Y., Simorangkir, C. L. F. & Aman, M. (2017). Analisis kinerja PLTU Indramayu sepanjang tahun 2015. Ketenagalistrikan dan Energi Terbarukan, 16(2), 97–106.
[12] Tasri, A., & Susilawati, A. (2014). Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments, 7, 34–44. https://doi.org/10.1016/j.seta.2014.02.008.
[13] Barzilai, J., & Lootsma, F. A. (1997). Power relations and group aggregation in the multiplicative AHP and SMART. Journal of Multi-Criteria Decision Analysis, 6(3), 155–165. https://doi.org/10.1002/(SICI)1099-1360(199705)6:3<155::AID-MCDA131>3.0.CO;2-4.
[14] Krej?í, J., & Stoklasa, J. (2018). Aggregation in the analytic hierarchy process: Why weighted geometric mean should be used instead of weighted arithmetic mean. Expert Systems with Applications, 114, 97–106. https://doi.org/10.1016/j.eswa.2018.06.060.
[15] Issn, O., et al. (2025). Enhancing data analysis efficiency: A comparative study of Excel’s VBA & Power Query vs. Python for large-scale data. International Journal of Advanced Research, 13(7), 143–147.
[16] Chaudhari, P., & Nahar, S. (2022). Comparing the performance of a business process: Using Excel & Python. 2324–2326.
[17] NumPy Community. (2013). NumPy user guide (Version 1.11). https://numpy.org/doc/1.18/numpy-user.pdf.
[18] Hunter, J., Dale, D., Firing, E., & Droettboom, M. (2017). Matplotlib (Version 2.0.2). https://matplotlib.org/2.0.2/Matplotlib.pdf.
