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Ahmad Gamal
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Smart City Center Universitas Indonesia, Gedung ILRC Lantai 3, Kampus Universitas Indonesia, Depok 16424
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Smart City
Published by Universitas Indonesia
ISSN : -     EISSN : 2962780X     DOI : 10.56940/sc
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SMART CITY focuses on four main topics: energy and environment, infrastructure, ICT and mobility, and quality of life. Published biannually, it serves as a comprehensive platform for researchers and practitioners alike to access latest issues, findings, and best practices in the fields of smart cities. SMART CITY is published by CCR Smart City at Universitas Indonesia.
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Articles 52 Documents
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SPATIAL TAX INTELLIGENCE: IMPLEMENTING INTEGRATED SPATIAL DECISION SUPPORT SYSTEMS FOR SMART URBAN GOVERNANCE Irfana, Wildan R; Bahar, Haldis A; Candra, Muhammad H
Smart City
Publisher : UI Scholars Hub

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Abstract

The transition toward smart governance requires local governments to adopt data-driven policy approaches, particularly in managing urban tax capacity. However, tax administration in many developing regions remains predominantly tabular and administrative, limiting the ability to capture the spatial dynamics of economic activities. This reveals a critical knowledge gap, as integrated frameworks combining spatial and tax data to identify geographic disparities in tax performance remain limited. Consequently, areas with high economic activity but low tax compliance, referred to as tax blind spots, often remain undetected. This study aims to analyze the effectiveness of integrating spatial and tax data in identifying spatial-tax mismatches and transforming local tax governance. Using Garut Regency, Indonesia, as a case study, the research examines the spatial distribution of taxable objects, in relation to their compliance status, and identifies areas where economic potential does not correspond with tax contribution. This study employs a mixed-methods approach with a Research and Development (R&D) design by integrating geospatial analysis and system development within a unified workflow. Spatial and tax data are consolidated through data integration and preprocessing, followed by spatial analysis to identify patterns of compliance and detect under-taxed areas. The processed data are then deployed within a web-based geospatial platform, enabling real-time access and interactive mapping. The analytical outputs are operationalized through an interactive dashboard that integrates spatial and tax indicators into a user-centered decision-support interface. The results show that geo-visual analytics effectively reveals tax blind spots, improves tax potential mapping, and enhances the identification of unregistered taxable objects. The novelty lies in the operationalization of Spatial Tax Intelligence as an integrated geospatial decision support system that combines real-time data integration, spatial analysis, and interactive visualization to support adaptive, evidence-based tax governance in developing regions.
A Smart Contract Framework for Project Financing in Gold Mining Operations: Implications for Improved Financial Forecasting Priskilla, Silvia Lydia; Berawi, Mohammed Ali; Sari, Mustika, Dr.
Smart City
Publisher : UI Scholars Hub

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Abstract

Gold mining projects are characterized by high capital intensity and significant operational variability, making financial forecasting particularly sensitive to the quality and timing of cash-flow data. In practice, conventional financing systems rely on multi-stage administrative processes that create delays between work completion and payment realization, reducing the reliability of financial records used for investment evaluation. This study aims to develop a smart contract-based financing framework to improve the accuracy of profitability projections in gold mining projects. A case study approach is employed using operational and financial data from a mining project in Sumatra over the period 2023–2026. The analysis combines archival data evaluation, expert survey, and financial simulation using Net Present Value (NPV) as the primary performance indicator. The results show that the existing system generates payment delays of approximately 28–30 days, contributing to unstable cash-flow patterns. By implementing a smart contract mechanism, verification time can be reduced by 30–60%, resulting in approximately 25% improvement in cash-inflow timing. Although total revenue remains unchanged, the earlier realization of cash flows leads to an increase in NPV from USD 114.07 million under the conventional system to between USD 115.01 million and USD 118.41 million, representing an improvement of up to 3.81%. These findings demonstrate that improvements in financial performance are not solely dependent on increasing production or revenue but can also be achieved by optimizing the timing and reliability of financial transactions. The proposed framework highlights the potential of smart contract-based systems to enhance both operational efficiency and financial accuracy in complex project environments.

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