International Journal For Multidisciplinary Research

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Business Intelligence Data-driven Assistant (BIDDA) for MSMEs using Setup Predictive Performance Model

Author(s) Russell N. Aquino, Rose Mary A. Velasco, Arnel C. Fajardo, Betchie E. Aguinaldo
Country Philippines
Abstract The Department of Science and Technology (DOST) is offering soft loan service under the Small Enterprise Technology Upgrading Program (SETUP), to assist Micro Small and Medium Enterprises (MSME) to avail useful technologies and machineries in order to improve their operations. Low rate of repayments affects the agency’s operations and the government. This paper aimed to create a predictive system analysis to foresee the success of loan repayment in the agency, particularly in SETUP Program. MSMEs may enroll in this program through project proposal, indicating the marketing aspect; technological aspect; and financial aspect of the firm. The DOST evaluates the firm’s positivity and its repayment capability through the abovementioned three aspects. BIDDA aims to determine the significant attributes in the development of data sets of SETUP adopter selection criteria in terms of: a. demographic profile; b. pre-performance business profile; and c. post-performance business profile after S&T intervention. There are also three foundations on the predictive computation for success rate: The Financial which covers the 50%; the Sector which covers the 30%; and the Location which covers the 20% of the total 100% of the success rate. This also helps the MSMEs to determine not only in terms of SETUP program success rate but also in their business success rate. We used Multi-Criteria Decision-Making (MCDM) machine learning model for the predictive analysis in the BIDDA application.
Keywords Predictive System Analysis, BIDDA, Loan Repayment, MCDM
Field Computer > Data / Information
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-08
Cite This Business Intelligence Data-driven Assistant (BIDDA) for MSMEs using Setup Predictive Performance Model - Russell N. Aquino, Rose Mary A. Velasco, Arnel C. Fajardo, Betchie E. Aguinaldo - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.13780
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.13780
Short DOI https://doi.org/gtmbmd
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