Penilaian Kinerja Pemasok Biomassa untuk Pembangkit Listrik Tenaga Batubara Menggunakan Pendekatan Pengambilan Keputusan Multi-Kriteria
Main Article Content
Transisi energi menuju sistem rendah karbon mendorong pemanfaatan biomassa sebagai bahan bakar alternatif melalui skema co-firing pada pembangkit listrik tenaga batubara. Keberhasilan implementasi strategi ini sangat bergantung pada kinerja pemasok biomassa yang mampu menjamin kontinuitas pasokan, kualitas bahan baku, efisiensi biaya, serta aspek keberlanjutan lingkungan. Penelitian ini bertujuan mengembangkan kerangka kerja evaluasi kinerja pemasok biomassa yang komprehensif dan sistematis sebagai dasar pengambilan keputusan dalam pengelolaan rantai pasok biomassa secara multidimensional berdasarkan kriteria biaya, kualitas, keandalan pasokan, dan lingkungan, serta menghasilkan penilaian pemasok sebagai dasar pendukung pengambilan keputusan. Metodologi yang digunakan mengombinasikan pendekatan Multi-Criteria Decision Making (MCDM), yaitu Fuzzy Analytic Hierarchy Process (Fuzzy AHP) untuk menentukan bobot kriteria dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk melakukan evaluasi dan pemeringkatan 124 pemasok biomassa PLN Grup. Hasil penelitian menunjukkan bahwa biaya merupakan kriteria dengan bobot tertinggi (0,3287), diikuti keandalan pasokan (0,2854), kualitas (0,2767), dan lingkungan (0,1092), sedangkan harga jual biomassa menjadi subkriteria dengan bobot global tertinggi (0,2607). Hasil TOPSIS menunjukkan adanya perbedaan tingkat kinerja relatif antar pemasok, dengan nilai relative closeness (Ci) berkisar antara 0,168 hingga 0,855 (rata-rata 0,788). Kerangka kerja yang dikembangkan dari integrasi Fuzzy AHP–TOPSIS dapat digunakan sebagai pendekatan sistematis untuk supplier performance evaluation dengan mempertimbangkan berbagai dimensi kinerja secara simultan. Hasil penelitian dapat dimanfaatkan sebagai decision-support tool untuk supplier performance monitoring dan identifikasi prioritas perbaikan kinerja pemasok.
Keywords:
Biomassa, co-firing, evaluasi pemasok, Fuzzy AHP, TOPSIS
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Gocer, F., & Sener, N. (2022). Spherical fuzzy extension of AHP?ARAS methods integrated with modified k?means clustering for logistics hub location problem. Expert Systems, 39(2). https://doi.org/10.1111/exsy.12886
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Kabeyi, M. J. B., & Olanrewaju, O. A. (2022). Sustainable energy transition for renewable and low carbon grid electricity generation and supply. Frontiers in Energy Research, 9. https://doi.org/10.3389/fenrg.2021.743114
Kementerian ESDM. (2022, August 29). Akselerasi transisi energi, co-firing biomassa di PLTU jadi teknologi pilihan. Siaran Pers Nomor: 324.Pers/04/SJI/2022.
Krishnadevarajan, P. K., Ravichandran, V., Balasubramanian, S., & Kannan, N. (2015). Supplier management: A framework for selection, evaluation and performance. International Journal of Management, 6(9), 16–28.
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Nguyen, D. H., & Chen, H. (2018). Supplier selection and operation planning in biomass supply chains with supply uncertainty. Computers & Chemical Engineering, 118, 103–117. https://doi.org/10.1016/j.compchemeng.2018.07.012
Noorul Haq, A., & Kannan, G. (2006). Fuzzy analytical hierarchy process for evaluating and selecting a vendor in a supply chain model. The International Journal of Advanced Manufacturing Technology, 29(7–8), 826–835. https://doi.org/10.1007/s00170-005-2562-8
Nunes, L. J. R., Causer, T. P., & Ciolkosz, D. (2020). Biomass for energy: A review on supply chain management models. Renewable and Sustainable Energy Reviews, 120. https://doi.org/10.1016/j.rser.2019.109658
Nunes, L. J. R., & Silva, S. (2023). Optimization of the residual biomass supply chain: Process characterization and cost analysis. Logistics, 7(3), 48. https://doi.org/10.3390/logistics7030048
O’Brien, J. (2022). Supplier relationship management: Unlocking the value in your supply base (3rd ed.). Kogan Page Publishers.
Okay, N. C., Sencer, A., & Taskin, N. (2024). Quantitative indicators for environmental and social sustainability performance assessment of the supply chain. Environment, Development and Sustainability, 28(3), 6049–6069. https://doi.org/10.1007/s10668-024-05210-3
Pehlivan, N. Y., Paksoy, T., & Çalik, A. (2018). Comparison of methods in FAHP with application in supplier selection. In A. Emrouznejad & W. Ho (Eds.), Fuzzy Analytic Hierarchy Process (pp. 45–75). CRC Press.
Permana, I., & Salisah, F. N. S. (2022). Pengaruh normalisasi data terhadap performa hasil klasifikasi algoritma backpropagation. Indonesian Journal of Informatic Research and Software Engineering, 2(1), 67–72. https://doi.org/10.57152/ijirse.v2i1.311
Prihartono, B., Darmawan, R. A., Kamilah, M., & Rizkiana, A. (2026). Perancangan mekanisme penilaian dan evaluasi kinerja supplier pada pusat penjualan dan distribusi X dengan pendekatan fuzzy multi-criteria decision making (FMCDM) dan ISM-fuzzy MICMAC. J@ti Undip: Jurnal Teknik Industri, 1(1), 47–57. https://doi.org/10.14710/jati.1.1.47-57
PT PLN (Persero). (2025, February 1). Co-firing biomassa di PLTU PLN hasilkan 1,67 juta MWh listrik hijau sepanjang 2024. Siaran Pers 01 Feb 2025.
PT PLN Energi Primer Indonesia. (2025, May 7). Biomassa.
Pujawan, I. N., & Er, M. (2017). Supply chain management (3rd ed.). ANDI.
Rao, C., Wang, C., Hu, Z., Meng, Y., & Liu, M. (2018). Sustainable procurement decision of electric coal under fuzzy information environment. Scientia Iranica. https://doi.org/10.24200/sci.2018.5054.1065
Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8
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