Integrasi PIPRECIA dan MACROS Dalam Penentuan Lokasi Pertanian Berkelanjutan
DOI:
https://doi.org/10.47065/bulletincsr.v5i5.703Keywords:
PIPRECIA; MACROS; Integration; Decision Support; Agricultural LocationAbstract
This study develops a decision support system for selecting sustainable agricultural locations amid the challenges of climate change. Two integrated multi-criteria decision-making (MCDM) methods are employed: PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) to determine the importance weights of criteria based on expert rankings, and MACROS (Measurement Alternatives and Ranking according to the Compromise Solution), a structured method used to evaluate and rank alternatives through normalization, weighted scoring, and compromise-based ranking. Six key criteria are considered: water availability, land suitability, flood risk, market access, government support, and microclimate conditions. The integration of PIPRECIA and MACROS enables a systematic and transparent evaluation process. The results indicate that location "G" achieves the highest compromise score of 0.985, signifying its suitability as the most optimal site for sustainable agricultural development. The primary contribution of this research lies in offering a quantitative and structured approach that accommodates environmental uncertainties while enhancing decision-making transparency. By integrating expert judgment with computational assessment, this model supports data-driven decision-making in the planning of agricultural development. These findings are expected to provide strategic insights for policymakers in formulating adaptive agricultural policies, strengthening food security, and improving farmer welfare through accurate and sustainable location selection.
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References
E. S. Akpabio, K. F. Akeju, and K. O. Omotoso, ‘E-agriculture and food security in developing countries: beaming the searchlight on Nigeria’, Smart Agricultural Technology, vol. 10, no. March, p. 100689, 2025, doi: 10.1016/j.atech.2024.100689.
N. S. Tandogan Aktepe and ?. E. Kayral, ‘Unraveling the Major Determinants behind Price Changes in Four Selected Representative Agricultural Products †’, Agriculture (Switzerland), vol. 14, no. 5, p. 782, 2024, doi: 10.3390/agriculture14050782.
L. N. Yogi, T. Thalal, and S. Bhandari, ‘The role of agriculture in Nepal’s economic development: Challenges, opportunities, and pathways for modernization’, Heliyon, vol. 11, no. 2, p. e41860, 2025, doi: 10.1016/j.heliyon.2025.e41860.
R. R. Abdulrahman et al., ‘The Role of Agriculture Sector in Eradicating Poverty: Challenges, Policies, and Pathways for Economic Growth in Less Developed Countries’, International Journal of Social Science and Education Research Studies, vol. 5, no. 2, pp. 208–213, 2025, doi: 10.55677/ijssers/v05i02y2025-11.
A. Akbar, R. Darma, A. Irawan, Mahyuddin, F. Feryanto, and R. Akzar, ‘COVID-19 pandemic and food security: Strategic agricultural budget allocation in Indonesia’, Journal of Agriculture and Food Research, vol. 18, no. December, p. 101494, 2024, doi: 10.1016/j.jafr.2024.101494.
A. Saleem et al., ‘Securing a sustainable future: the climate change threat to agriculture, food security, and sustainable development goals’, Journal of Umm Al-Qura University for Applied Sciences, no. July, pp. 1–17, 2024, doi: 10.1007/s43994-024-00177-3.
H. Onyeaka et al., ‘The ripple effects of climate change on agricultural sustainability and food security in Africa’, Food and Energy Security, vol. 13, no. 5, pp. 1–15, 2024, doi: 10.1002/fes3.567.
S. Alemayehu, D. Olago, O. Alfred, T. T. Zeleke, and S. W. Dejene, ‘Spatiotemporal analysis of rainfall and temperature variability and trends for a mixed crop-livestock production system: its implications for developing adaptation strategies’, International Journal of Climate Change Strategies and Management, vol. 17, no. 1, pp. 268–290, 2024, doi: 10.1108/ijccsm-08-2024-0133.
W. Cheng, Y. Li, W. Zuo, G. Du, and M. Stanny, ‘Spatio-temporal detection of agricultural disaster vulnerability in the world and implications for developing climate-resilient agriculture’, Science of The Total Environment, vol. 928, no. June, p. 172412, 2024, doi: 10.1016/j.scitotenv.2024.172412.
M. H. Yao et al., ‘Agricultural Disaster Prevention System: Insights from Taiwan’s Adaptation Strategies’, Atmosphere, vol. 15, no. 5, p. 526, 2024, doi: 10.3390/atmos15050526.
F. Luo, C. Wang, S. Luo, Q. Tong, and L. Xu, ‘Optimizing natural resource markets: Accelerating green growth in the economic recovery’, Resources Policy, vol. 89, no. February, p. 104648, 2024, doi: 10.1016/j.resourpol.2024.104648.
Y. Xing and X. Wang, ‘Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions’, Plants, vol. 13, no. 22, p. 3184, 2024, doi: 10.3390/plants13223184.
G. Demir, P. Chatterjee, S. Kadry, A. Abdelhadi, and D. Pamu?ar, ‘Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis’, Decision Making: Applications in Management and Engineering, vol. 7, no. 2, pp. 313–336, 2024, doi: 10.31181/dmame7220241137.
B. Kizielewicz, J. W?tróbski, and W. Sa?abun, ‘Multi-criteria decision support system for the evaluation of UAV intelligent agricultural sensors’, Artificial Intelligence Review, vol. 58, no. 194, pp. 1–25, 2025, doi: 10.1007/s10462-025-11201-1.
I. Sabry, A.-H. I. Mourad, M. Alkhedher, M. Alkhedher, and A. El-Araby, ‘A comparative study of multiple-criteria decision-making methods for selecting the best process parameters for friction stir welded Al 6061 alloy’, Welding International, vol. 37, no. 11, pp. 626–642, 2023, doi: 10.1080/09507116.2023.2270896.
S. Popovi? et al., ‘Optimization of Artificial Intelligence Algorithm Selection: PIPRECIA-S Model and Multi-Criteria Analysis’, Electronics (Switzerland), vol. 14, no. 3, pp. 1–17, 2025, doi: 10.3390/electronics14030562.
Setiawansyah, S. H. Hadad, A. A. Aldino, P. Palupiningsih, G. F. Laxmi, and D. A. Megawaty, ‘Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections’, Bulletin of Electrical Engineering and Informatics, vol. 13, no. 6, pp. 4273–4284, 2024, doi: 10.11591/eei.v13i6.7713.
S. Setiawansyah, S. Sintaro, V. H. Saputra, and A. A. Aldino, ‘Combination of Grey Relational Analysis (GRA) and Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) in Determining the Best Staff’, Bulletin of Informatics and Data Science, vol. 2, no. 2, p. 57, 2024, doi: 10.61944/bids.v2i2.67.
A. Alnoor, Y. R. Muhsen, N. A. Husin, X. Chew, M. B. Zolkepli, and N. Manshor, ‘Z-cloud Rough Fuzzy-Based PIPRECIA and CoCoSo Integration to Assess Agriculture Decision Support Tools’, International Journal of Fuzzy Systems, vol. 27, no. June, pp. 190–203, 2025, doi: 10.1007/s40815-024-01771-7.
M. Waqas Arshad and Y. Rahmanto, ‘Combination of TOPSIS Method and PIPRECIA Weighting for Best Hotel Selection’, International Journal of Informatics and Data Science, vol. 1, no. 2, pp. 46–54, 2024, [Online]. Available: https://journals.adaresearch.or.id/ijids/index
A. Aggarwal, I. Sharma, V. Kukreja, T. Verma, and R. Aggarwal, ‘Assessing and ranking the skills required for IT personnel: a hybrid decision-making model using fuzzy AHP-TOPSIS’, Global Knowledge, Memory and Communication, no. March, 2025, doi: 10.1108/GKMC-05-2024-0253.
M. E. Brown, A. J. P. Carcedo, M. Eggen, K. L. Grace, J. Neff, and I. A. Ciampitti, ‘Integrated modeling framework for sustainable agricultural intensification’, Frontiers in Sustainable Food Systems, vol. 6, no. 1, p. 1039962, 2023, doi: 10.3389/fsufs.2022.1039962.
S. Sridevy, M. N. Devi, and M. Sankar, ‘Decision support systems in agricultural industry perspective’, International Journal of Statistics and Applied Mathematics, vol. 8, no. 2S, pp. 29–31, 2023, doi: 10.22271/maths.2023.v8.i2sa.984.
M. J. Barons, L. E. Walsh, E. E. Salakpi, and L. Nichols, ‘A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks’, Agriculture (Switzerland), vol. 14, no. 3, p. 458, 2024, doi: 10.3390/agriculture14030458.
M. Gardezi, D. T. Adereti, R. Stock, and A. Ogunyiola, ‘In pursuit of responsible innovation for precision agriculture technologies’, Journal of Responsible Innovation, vol. 9, no. 2, pp. 224–247, 2022, doi: 10.1080/23299460.2022.2071668.
D. Pasha, M. Safi, and S. Setiawansyah, ‘Penerapan Multi-Atributive Ideal-Real Comparative Analysis dan PIPRECIA Dalam Evaluasi Kinerja Pemasok Bahan Baku’, KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 4, pp. 2005–2017, 2024, doi: 10.30865/klik.v4i4.1652.
S. Chakraborty, P. Chatterjee, and P. P. Das, Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) Method, 1st Editio. Taylor & Francis, 2023.
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