Loso Judijanto, Ardi Azhar Nampira, Urnika Mudhifatul Jannah, Muhammad Faisal

Intelligent Algorithms for Mapping the Financial Feasibility of Start-up Entrepreneurs in Indonesia

Introduction

Intelligent algorithms for mapping the financial feasibility of start-up entrepreneurs in indonesia. Explore intelligent algorithms for assessing start-up financial feasibility in Indonesia. Overcome data, cost, and expertise barriers with a hybrid framework for enhanced decision-making.

Abstract

This study explores the role of intelligent algorithms in assessing the financial feasibility of start-up entrepreneurs in Indonesia, employing a qualitative approach with insights from five key informants. Traditional financial assessment methods are found to be limited by factors such as market volatility, lack of historical data, and resource constraints. Intelligent algorithms offer transformative potential, with advantages in predictive accuracy, scenario simulations, and efficiency. However, barriers including technical expertise, cost constraints, and data availability hinder their adoption. A hybrid framework integrating qualitative insights and algorithmic tools is proposed to address these challenges and enhance decision-making for start-ups. The findings provide a practical roadmap for leveraging advanced technologies in the dynamic and diverse Indonesian start-up ecosystem.

Review

This study tackles a highly relevant and pressing issue: the application of intelligent algorithms to assess the financial feasibility of start-up entrepreneurs, specifically within the unique and dynamic Indonesian ecosystem. The abstract clearly articulates the shortcomings of conventional financial assessment methods, such as their susceptibility to market volatility and lack of historical data, thereby establishing a strong rationale for exploring advanced technological solutions. The presented advantages of intelligent algorithms, including enhanced predictive accuracy, scenario simulation capabilities, and operational efficiency, underscore their transformative potential, making the core premise of the research compelling and timely. The proposed hybrid framework, which aims to integrate qualitative insights with algorithmic tools, is particularly promising, suggesting a nuanced approach to overcome inherent challenges. However, certain methodological aspects outlined in the abstract warrant deeper consideration. The reliance on a qualitative approach with insights from only five key informants, while providing depth, raises questions about the representativeness and generalizability of the findings across the diverse Indonesian start-up landscape. While the abstract successfully identifies significant barriers to the adoption of intelligent algorithms, such as technical expertise, cost constraints, and data availability, it offers limited detail on precisely *how* the proposed hybrid framework addresses and mitigates these specific challenges. A more granular explanation in the full paper, detailing the practical mechanisms and strategies within this framework, would significantly enhance the study's practical utility and credibility. Ultimately, the study holds considerable promise for delivering a valuable "practical roadmap" for leveraging advanced technologies to foster the growth and sustainability of the Indonesian start-up ecosystem. The balanced perspective, acknowledging both the immense potential and the significant hurdles, is a strength. To fully realize its stated objectives and contribute meaningfully to the field, the full paper should provide comprehensive details on the qualitative methodology, including the rationale for informant selection and sample size, as well as an in-depth exposition of the hybrid framework’s components and its empirical or conceptual validation. Such elaboration would solidify the study's impact and provide actionable insights for stakeholders navigating the complex interplay of finance, technology, and entrepreneurship.

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