Intelligent Mobile-Spatial System for Small Business Digital Innovation in Lubuk Linggau

A Taqwa Martadinata; Joni Karman; Herman Paleni; Aptrilia Nadiella; Ariel Pratama

doi:10.33019/

Authors

A Taqwa Martadinata Department of Informatics, Faculty of Engineering, Universitas Bina Insan
Joni Karman Department of Information Systems, Faculty of Engineering, Universitas Bina Insan
Herman Paleni Department of Management, Faculty of Economics and Social Sciences and Humanities, Universitas Bina Insan
Aptrilia Nadiella Department of Informatics, Faculty of Engineering, Universitas Bina Insan,
Ariel Pratama Department of Informatics, Faculty of Engineering, Universitas Bina Insan,

DOI:

https://doi.org/10.33019/

Keywords:

Decision Support System, Digital Readiness, Geographic Information System, Intelligent Information System, MSME, Simple Additive Weighting

Abstract

Micro, small, and medium enterprises (MSMEs) in Lubuklinggau City face digitalization barriers caused by the absence of an integrated, location-based business database and an objective basis for prioritizing digitalization programs. This study designed, built, and tested a mobile- and spatial-based information-system prototype integrating a Geographic Information System, a mobile-first responsive web application, and a Simple Additive Weighting (SAW)-based decision-support module with five digital-readiness criteria, deployed and populated with 50 MSME records across six sub-districts (about 0.51% of the 9,746 MSMEs on the city’s official business portal). Three kinds of evidence are reported separately: computational verification, in which system-generated preference values agreed exactly with an independent manual recalculation for all 50 records; software testing, in which 18 functional cases and 8 role-based access-control scenarios passed after two defects found in development-team testing were corrected and re-verified; and preliminary, non-generalizable feedback from a single development-team respondent. Because the active weights are an unelicited uniform placeholder, a sensitivity analysis recomputed rankings under four alternative weight scenarios: 66% of MSMEs changed maturity class under at least one scenario, and one cost-priority scenario retained only 2 of the baseline top-10 MSMEs, showing that the ranking is materially sensitive to weight choice. These results demonstrate implementation correctness and functional behavior under the tested conditions; they do not establish the criteria, weights, or thresholds as a validated policy instrument, nor system-wide technical readiness, both requiring further evaluation described in the conclusion.

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