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Analysis of AI Algorithm Development: From Machine Learning to Deep Learning Jompon Pitaksantayothin; Hariyadi Fajar Nugroho
Artificial Intelligence Systems and Its Applications Vol. 1 No. 1 (2025): Vol. 1, No. 1, June 2025
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v1i1.18

Abstract

The development of Artificial Intelligence (AI) is currently very rapid, but there is still much confusion regarding the differences and evolution of the main algorithms, namely machine learning (ML) and deep learning (DL). This study aims to analyze the development of AI algorithms conceptually and technically from conventional ML to DL, and to provide a structured understanding of the paradigm shift in AI development. The method used is a systematic literature study of 10 recent scientific articles discussing aspects of ML and DL algorithms. The results of the analysis show that ML relies on manual feature extraction with the advantages of computational efficiency and interpretability, while DL is able to process large and complex data automatically with better performance, although it requires high computing resources and faces interpretability challenges. The discussion also identifies the main challenges that AI still faces as well as innovation opportunities to overcome these limitations. In conclusion, a deep understanding of the evolution of AI algorithms is essential as a foundation for the development of more adaptive, effective, and transparent AI technology in the future.
Community Empowerment Multiculturalism Through the Digitalization of Village Websites as a Medium For Religious Moderation and MSMEs Promotion Dewi Iriani; Hariyadi Fajar Nugroho; Ahmad Muhammad; Elyza Devi Pramest; Salsa Alvalinnas Alitsa Minha; Shelly Choirunisa; Syafira Khoirunnisa
JARDIRA – Jurnal Pengabdian Digital dan Rekayasa Informatika Vol. 2, No. 1, January 2026
Publisher : CogniSpectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/jardira.v2i1.49

Abstract

ABSTRACT Background: Digital-based community service is a strategic approach to addressing the social and economic challenges facing villages in the era of digital transformation. This program aims to empower the multicultural community of Gedongrejo Village by utilizing the village website as a public information medium, strengthening religious moderation, and promoting information technology-based MSMEs. Contribution: This community service activity contributes to improving digital literacy among village officials and the community, strengthening social cohesion through religious moderation content in digital spaces, and opening up access to digital promotion for local MSMEs. Method: The community service method is carried out in a participatory manner with stages of needs analysis, design and development of village websites based on informatics engineering, training in system management and digital literacy, assistance in content production, and monitoring and evaluation of website utilization. Results: The community service results indicate that the village website was successfully implemented as a public information system and community empowerment tool. The website was used to disseminate village information, publish content on religious moderation, and serve as a digital showcase for MSMEs, resulting in increased access to information and community utilization of digital technology. Conclusion: This community service program proves that digitalization of village websites based on information technology engineering can be an effective instrument in empowering multicultural communities, strengthening religious moderation, and providing initial support for the sustainable development of local MSMEs.
Personal Data Protection in the Era of Artificial Intelligence: A Critical Review of Indonesia's Regulatory Readiness Based on OECD Principles Nendy Akbar Rozaq Rois; Hariyadi Fajar Nugroho
Global Journal of Law, AI & Ethics Vol. 1, No. 1, August 2025
Publisher : CV. Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/gjlae.v1.i1.15

Abstract

Purpose of the Study: This study aims to evaluate the readiness of Indonesian law in regulating personal data protection in the era of artificial intelligence (AI), by highlighting the conformity of national regulations to international standards, especially the OECD Principles and regulations of developed countries such as the European Union. Methodology: A normative-juridical approach with a qualitative comparative method was used, analyzing Law No. 27 of 2022 (PDP Law) and related instruments. The OECD Principles on AI served as an evaluative framework. The analysis was structured in four stages: (1) identifying OECD principles; (2) mapping provisions in Indonesian law; (3) analyzing regulatory gaps; and (4) comparing with selected jurisdictions (EU, Japan, Singapore, Brazil, India). Case studies were used to illustrate practical implications. Results: The results of the study show that although several aspects such as the principle of consent and data security have been accommodated in the PDP Law, there is still a legal gap in terms of liability for AI violations, algorithm audits, and transparency of automated decisions. Comparison with regulations of developed countries highlights substantial gaps in the protection of data subject rights. Applications of This Study: This study can be used as a normative and practical reference for policy makers in designing a legal framework that is adaptive to the development of AI technology, while increasing legal awareness among technology developers and other stakeholders. Novelty/Originality of This Study: This study offers a critical approach to AI regulation in Indonesia using the OECD Principles as an evaluative parameter, and identifies aspects of the law that have not been widely studied, such as the mechanism for objecting to automated decisions and the right to be forgotten in the context of artificial intelligence