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All Journal Jurnal Sosial dan Sains Insight : International Journal of social research
Ghina Fauziyyah
Sekolah Tinggi Manajemen Informatika dan Komputer LIKMI, Indonesia
Humanities Astronomy Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Physics

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A Business Intelligence-Based Data Governance Model to Reduce the Risk of AI Implementation Failure in Organizations Ghina Fauziyyah; Mar'atus Solikhah
International Journal of Social Research Vol. 4 No. 2 (2026): Insight : International Journal of Social Research
Publisher : Worldwide Research Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/insight.v4i2.94

Abstract

The implementation of Artificial Intelligence (AI) in organizations is increasing along with the development of digital transformation and the need for data-driven decision-making. However, various studies show that many AI projects fail due to poor data quality, lack of data integration, and weak data governance within the organization. In this context, Business Intelligence (BI) has the potential to support systematic data management through data integration, analytics, and information visualization. Therefore, a data governance model integrated with Business Intelligence is needed to improve the quality of data management and reduce the risk of AI implementation failure. This study aims to develop a Business Intelligence-based Data Governance model that can reduce the risk of Artificial Intelligence implementation failure in organizations and increase the effectiveness of data-driven decision-making. This study uses a quantitative approach with an explanatory research method. Research data were obtained by distributing questionnaires to respondents involved in data management and the implementation of organizational analytical systems. Data analysis was conducted using the Structural Equation Modeling (SEM) method with the Partial Least Squares (PLS) approach to examine the relationship between data governance variables, Business Intelligence capability, and AI implementation risk. The results of this study indicate that data governance significantly impacts Business Intelligence capabilities, which in turn contributes to reducing the risk of AI implementation failure within organizations. Furthermore, data quality is shown to be a crucial mediating factor linking data governance to successful AI implementation. This study produces a Business Intelligence-based Data Governance conceptual model that can be used as a framework for organizational data management to support more effective AI implementation.
Generative AI Integration in the Startup Ecosystem: A Technopreneurship Strategy to Increase Global Competitiveness Ghina Fauziyyah; Mar’atus Solikhah
International Journal of Social Research Vol. 4 No. 1 (2026): Insight : International Journal of Social Research
Publisher : Worldwide Research Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/insight.v4i1.96

Abstract

The rapid development of Generative Artificial Intelligence (Generative AI) technology has driven significant transformations in the global startup ecosystem. This technology enables the automation of creative processes, faster data analysis, and more efficient digital product development. In the context of technopreneurship, the integration of generative AI is a strategic factor that can increase startups' innovation capacity while strengthening global competitiveness. However, understanding how the integration of generative AI technology can be optimized in technopreneurship strategies to improve startup competitiveness still requires more in-depth empirical studies. This study aims to analyze the influence of generative AI integration in the startup ecosystem on technopreneurship strategies and its impact on increasing startups' global competitiveness. This study uses a quantitative approach with an explanatory research method. Data were collected through a survey of founders and managers of technology-based startups that utilize digital innovation in their business activities. The sampling technique used purposive sampling with a total of 150 startups as respondents. Data analysis was conducted using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method to test the relationship between the research variables. The results of the study indicate that the integration of generative AI has a positive and significant impact on technopreneurship strategies and startups' global competitiveness. Furthermore, technopreneurship strategies are proven to act as a mediating factor, strengthening the relationship between the use of generative AI technology and increased startup competitiveness in the global market. These findings suggest that the use of generative AI, supported by innovative technological entrepreneurship strategies, can be a source of competitive advantage for startups in facing the dynamics of the global digital economy.
Strategic Planning of Cybersecurity Governance in Supporting National Digital Infrastructure Resilience Ghina Fauziyyah
International Journal of Social Research Vol. 3 No. 5 (2025): Insight : International Journal of Social Research
Publisher : Worldwide Research Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/insight.v3i5.104

Abstract

The development of digital transformation has increased the dependence of various sectors on national digital infrastructure to support government, economic, and public service activities. This condition is accompanied by an increasing cyber security threat that has the potential to disrupt the stability of a country's information system and digital resilience. Therefore, it is necessary to have strategic planning for integrated cybersecurity governance to ensure the sustainability and security of national digital infrastructure. This research aims to analyze strategic planning of cybersecurity governance and formulate a governance model that can support increasing the resilience of national digital infrastructure. This research uses a qualitative approach with a descriptive-analytical method. Data collection was carried out through interviews, observations, questionnaires, and documentation studies on organizations that manage information systems and digital infrastructure. Data analysis was carried out using a framework analysis approach with reference to information technology governance frameworks such as COBIT and the NIST Cybersecurity Framework. The results of the study show that the implementation of cybersecurity governance in some organizations is still not fully integrated with the organization's strategic planning. The implementation of IT governance frameworks such as COBIT and NIST can help organizations identify cybersecurity risks, improve threat monitoring, and strengthen inter-agency coordination. This research also produces a strategic planning model for cybersecurity governance that emphasizes the integration of organizational strategies, risk management, security technology, and national policies to improve the resilience of national digital infrastructure
Komparasi Profil Higrotermal dan Kinetika Pertumbuhan Jamur pada Permukaan Dinding Bangunan: Studi Jaringan Sensor IoT Real-Time pada Perumahan Pesisir [Perumahan ABC] vs Perumahan Dataran Tinggi [Perumahan XYZ] Mohamad Nasir; Ghina Fauziyyah; Rafi Farizki
Jurnal sosial dan sains Vol. 6 No. 7 (2026): Jurnal Sosial dan Sains
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsosains.v6i7.32778

Abstract

Degradasi higrotermal pada selubung dinding bangunan di iklim mikro tropis menunjukkan variasi signifikan di sepanjang gradien topografi yang curam, namun pemantauan langsung secara kontinu yang berfokus pada kondisi permukaan batas material masih terbatas. Penelitian ini mengkaji variasi kinetika kondensasi permukaan dan pertumbuhan jamur biogenik pada dua tipologi perumahan yang berbeda: dataran rendah pesisir dan lereng dataran tinggi di Cirebon, Indonesia. Penelitian ini mengintegrasikan indeks higrotermal dinamis berbasis komputasi lokal pada node IoT, memproses parameter ambien dan suhu permukaan non-kontak untuk menghitung margin titik embun dan kelembapan relatif permukaan secara real-time. Pemantauan telemetri kontinu selama 30 hari mengungkap perbedaan deskriptif yang jelas antara kedua lokasi penelitian. Berdasarkan periode pengamatan, analisis deret waktu mengungkapkan pola degradasi yang kontras pada perumahan. Perumahan XYZ mengalami kondisi kritis selama 7,8 ± 1,6 jam per hari dan kelembapan relatif permukaan 88,7 ± 5,2%, dengan akumulasi indeks jamur 2,45 ± 0,31. Perumahan ABC mencatat akumulasi akhir 0,12 ± 0,05. Penerapan komputasi lokal mereduksi volume transmisi data sebesar 83,3% dengan waktu latensi kurang dari 2,4 detik. Temuan ini memberikan bukti empiris mengenai mekanisme fisika bangunan lokal dan menawarkan kerangka kerja IoT untuk pemeliharaan prediktif serta pemilihan material arsitektur adaptif iklim di permukiman tropis dengan kondisi geografis yang beragam.
Co-Authors Mar'atus Solikhah Mar’atus Solikhah Mohamad Nasir Rafi Farizki