Recipient Feasibility Decision Support System Micro Small Medium Business Assistance Use Method Analytic Hierarchy Process and Simple Additives Weighting
DOI: http://dx.doi.org/10.62527/joiv.8.2.2321
Abstract
Study This aims To determine the eligibility of MSME assistance recipients with the method AHP (Analytic Hierarchy Process) And SAW (Simple Additives weighting). The AHP method is used to determine the weight of each criterion. Meanwhile, SAW is used To determine the rank selection of beneficiaries. This is very important for Indonesia's economy during the crisis, Where MSME's own Power stands to face a crisis economy. Criteria used in a way This uses six measures: type of business, Amount of power Work, turnover per month, amount of assets, sector MSME, And sector business. Decision support systems are designed to support someone who must make certain decisions. That is, interactive, Flexible, Data quality, and Expert Procedure. Study System Supporters Decision Appropriateness Recipient Help Business Micro Community Use Analytic Hierarchy Process (AHP) and Simple Additive Methods weighting (SAW), Study This done in Subdistrict Intersection Three Regency Pidie Aceh Province to facilitate the Selection of Eligibility of Government Assistance Recipients For Build a business Micro Society. Testing is done in this study, namely black box testing. Results Testing black box shows that the system can walk with Good by function, with results calculation method AHP and results calculation method SAW in determining eligibility selection MSME aid recipients. The results of the level of accuracy testing on the AHP and SAW methods with six criteria and alternatives the requirements is 75%.
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B. J. Que et al., “Decision Support System using Multi-Factor Evaluation Process Algorithm,” Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012016, Jun. 2021, doi: 10.1088/1742-6596/1933/1/012016.
D. Abdullah et al., “DEA Model with Hesitant Fuzzy Polyhedral Set in Benchmarking,” Journal of Physics: Conference Series, vol. 1361, no. 1, p. 012033, Nov. 2019, doi: 10.1088/1742-6596/1361/1/012033.
W. Hadikurniawati, E. Winarno, A. B. Prabowo, and D. Abdullah, “Implementation of Tahani Fuzzy Logic Method for Selection of Optimal Tourism Site,” Journal of Physics: Conference Series, vol. 1361, no. 1, p. 012051, Nov. 2019, doi: 10.1088/1742-6596/1361/1/012051.
G. Zhao, J. Suklan, S. Liu, C. Lopez, and L. Hunter, “Does the transcultural problem really matter? An integrated approach to analyze barriers to eHealth SMEs’ development,” International Journal of Entrepreneurial Behaviour and Research, vol. ahead-of-print, no. ahead-of-print, 2023, doi: 10.1108/IJEBR-08-2022-0740/FULL/XML.
L. Tarifu, M. A. Equatora, Romindo, D. Abdullah, Herianto, and Y. M. Saragih, “Decision Support System Simulation Application with MFEP Method,” Journal of Physics: Conference Series, vol. 1845, no. 1, p. 012027, Mar. 2021, doi: 10.1088/1742-6596/1845/1/012027.
X. Guan and J. Zhao, “A Two-Step Fuzzy MCDM Method for Implementation of Sustainable Precision Manufacturing: Evidence from China,” Sustainability 2022, Vol. 14, Page 8085, vol. 14, no. 13, p. 8085, Jul. 2022, doi: 10.3390/SU14138085.
A. E. Andargoli, N. Ulapane, T. A. Nguyen, N. Shuakat, J. Zelcer, and N. Wickramasinghe, “Intelligent decision support systems for dementia care: A scoping review,” Artificial Intelligence in Medicine, vol. 150, p. 102815, Apr. 2024, doi: 10.1016/j.artmed.2024.102815.
X. Mi, M. Tang, H. Liao, W. Shen, and B. Lev, “The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next?,” Omega, vol. 87, pp. 205–225, Sep. 2019, doi: 10.1016/J.OMEGA.2019.01.009.
D. Abdullah, - Hartono, and C. I. Erliana, “Hesitant Fuzzy-Stochastic Data Envelopment Analysis (HF-SDEA) Model for Benchmarking,” JOIV : International Journal on Informatics Visualization, vol. 5, no. 1, pp. 94–98, Mar. 2021, doi: 10.30630/joiv.5.1.405.
H. Hartono and E. Ongko, “Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 1, pp. 131–138, Feb. 2023, doi: 10.30630/joiv.7.1.1292.
F. Hak, J. Duarte, T. Guimarães, and M. Santos, “Towards an Effective Clinical Decision Support System in Intensive Medicine,” Procedia Computer Science, vol. 210, pp. 236–241, Jan. 2022, doi: 10.1016/j.procs.2022.10.143.
A. Dovbysh, I. Shelehov, A. Romaniuk, R. Moskalenko, and T. Savchenko, “Decision-making support system for diagnosis of oncopathologies by histological images,” Journal of Pathology Informatics, vol. 14, p. 100193, Jan. 2023, doi: 10.1016/j.jpi.2023.100193.
E. Ongko and H. Hartono, “Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 3, Art. no. 3, Jun. 2021, doi: 10.11591/eei.v10i3.3057.
Z. Zhai, J. F. Martínez, V. Beltran, and N. L. Martínez, “Decision support systems for agriculture 4.0: Survey and challenges,” Computers and Electronics in Agriculture, vol. 170, p. 105256, Mar. 2020, doi: 10.1016/j.compag.2020.105256.
S. G. Fashoto, O. Amaonwu, and A. Afolorunsho, “Development of A Decision Support System on Employee Performance Appraisal using AHP Model,” JOIV : International Journal on Informatics Visualization, vol. 2, no. 4, pp. 262–267, Aug. 2018, doi: 10.30630/joiv.2.4.160.
D. Meidelfi, - Yulherniwati, F. Sukma, D. Chandra, and A. H. S. Jonas, “The Implementation of SAW and BORDA Method to Determine the Eligibility of Studentsâ€TM Final Project Topic,” JOIV : International Journal on Informatics Visualization, vol. 5, no. 2, pp. 144–149, May 2021, doi: 10.30630/joiv.5.1.447.
G. Büyüközkan and M. Güler, “Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique,” Measurement, vol. 153, p. 107353, Mar. 2020, doi: 10.1016/j.measurement.2019.107353.
N. Vafaei, R. A. Ribeiro, and L. M. Camarinha-Matos, “Assessing Normalization Techniques for Simple Additive Weighting Method,” Procedia Computer Science, vol. 199, pp. 1229–1236, Jan. 2022, doi: 10.1016/j.procs.2022.01.156.
S. Sutrisno, W. Wulandari, V. Violin, A. Supriyadi, and M. R. Tawil, “Prioritization of the Best Online Platform for MSMEs Using Simple Additive Weighting Method,” Journal on Education, vol. 5, no. 3, pp. 10265–10275, Feb. 2023.
V. K. Sharma, S. K. Sharma, and A. P. Singh, “Assessment for risk of logistics infrastructure projects using analytic network process,” International Journal of Process Management and Benchmarking, vol. 11, no. 5, pp. 725–756, 2021, doi: 10.1504/IJPMB.2021.117341.
A. J. Naeemah and K. Y. Wong, “Selection methods of lean management tools: a review,” International Journal of Productivity and Performance Management, vol. 72, no. 4, pp. 1077–1110, Mar. 2023, doi: 10.1108/IJPPM-04-2021-0198/FULL/XML.
Okfalisa, Mahyarni, W. Anggraini, Saktioto, and B. Pranggono, “Assessing digital readiness of small medium enterprises: intelligent dashboard decision support system,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 98–108, Apr. 2022, doi: 10.14569/IJACSA.2022.0130412.
S. Santosa, Y. P. Santosa, G. L. Goro, Wahjoedi, and J. Mahbub, “Computational of Concrete Slump Model Based on H2O Deep Learning framework and Bagging to reduce Effects of Noise and Overfitting,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 2, pp. 370–376, Jun. 2023, doi: 10.30630/JOIV.7.2.1201.
R. D. Raut, B. B. Gardas, B. E. Narkhede, and V. S. Narwane, “To investigate the determinants of cloud computing adoption in the manufacturing micro, small and medium enterprises: A DEMATEL-based approach,” Benchmarking, vol. 26, no. 3, pp. 990–1019, Mar. 2019, doi: 10.1108/BIJ-03-2018-0060/FULL/XML.
M. Ikhlas and L. Jafnihirda, “Comparative Analysis of Strategic Location Selection Decisions for MSMEs (UMKM) Using the MFEP and SAW Method,” Proceedings - 2nd International Conference on Computer Science and Engineering: The Effects of the Digital World After Pandemic (EDWAP), IC2SE 2021, 2021, doi: 10.1109/IC2SE52832.2021.9792054.
S. S. Panpatil, H. Prajapati, and R. Kant, “Analysing a GSCM Enabler–Based Model for Implementation of Its Practices: a Pythagorean Fuzzy AHP and CoCoSo Approach,” Process Integration and Optimization for Sustainability 2022 7:3, vol. 7, no. 3, pp. 523–543, Oct. 2022, doi: 10.1007/S41660-022-00289-5.
N. Bhatt, S. Guru, S. Thanki, and G. Sood, “Analysing the factors affecting the selection of ERP package: a fuzzy AHP approach,” Information Systems and e-Business Management, vol. 19, no. 2, pp. 641–682, Jun. 2021, doi: 10.1007/S10257-021-00521-8/METRICS.
P. Bhatia and N. Diaz-Elsayed, “Facilitating decision-making for the adoption of smart manufacturing technologies by SMEs via fuzzy TOPSIS,” International Journal of Production Economics, vol. 257, p. 108762, Mar. 2023, doi: 10.1016/J.IJPE.2022.108762.
R. Aghlmand, M. Gheibi, A. Takhtravan, and Z. Kian, “Implementation of green marketing frameworks based on conceptual system designing by integration of PESTLE, classical Delphi and MCDM modeling,” SN Business & Economics 2022 2:8, vol. 2, no. 8, pp. 1–33, Jul. 2022, doi: 10.1007/S43546-022-00273-8.
M. Andarwati and G. Swalaganata, “Analysis of Promotional Media Selection Based on Modified Analytical Hierarchy Process (AHP) to Increase Halal Product Sales Volume,” IQTISHODUNA: Jurnal Ekonomi Islam, vol. 12, no. 1, pp. 1–16, Apr. 2023, doi: 10.54471/IQTISHODUNA.V12I1.2270.
S. V. Bhaskar and H. N. Kudal, “Multi-criteria decision-making approach to material selection in tribological application,” International Journal of Operational Research, vol. 36, no. 1, pp. 92–122, 2019, doi: 10.1504/IJOR.2019.102072.
A. O. Omotola, C. W. Shiang, M. A. Bin Khairuddin, N. B. Jalil, and E. Phang, “Value-based modeling and simulation for sustainable ICT4D,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 2, pp. 279–286, May 2023, doi: 10.30630/JOIV.7.2.1314.