Afnan, Hafsha Amalia and , Wardah Yuspin, S.H.,M.Kn.,Ph.D. (2022) Perlindungan Hukum Penyalahgunaan Artificial Intelligence Deepfake Pada Layanan Pinjaman Online. Skripsi thesis, Universitas Muhammadiyah Surakarta.
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Abstract
Technological advances in the digital era are very rapid, affecting various fields, one of which is the economic or financial sector, namely the increasing number of online loan services, both legal and illegal. With the ease of loan application requirements offered by various online loan services, you only need to use an ID card, job information, access to borrower contacts, bank accounts, and self-portraits in the form of selfies, causing new problems that arise, namely the misuse of other people's personal data for register and apply for a loan with the help of Artificial Intelligence deepfake technology so that there are several people who claim to get a debt bill and the threat of sharing their personal data on online loan services even though they have never registered or applied for a loan. This misuse of personal data certainly causes losses and violates the privacy rights of others. The purpose of this study is to examine the legal protection for victims of falsification of personal data on online loan services according to UU No. 11 of 2008 concerning Information and Electronic Transactions, using normative juridical legal research methods. The results of the study indicate that legal protection and sanctions against falsification of personal data have generally been regulated in Law No. 11 of 2008 in particular article 35, namely the prohibition of manipulating, creating, changing, deleting, destroying Electronic Information and/or Electronic Documents with the aim that Electronic Information and/or the Electronic Document is considered as if the data is authentic.
Item Type: | Thesis (Skripsi) |
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Uncontrolled Keywords: | Deepfake, Artificial Intelligence, Artificial Intelligence, Legal Protection, Online Loans. |
Subjects: | K Law > K Law (General) |
Divisions: | Fakultas Hukum > Hukum |
Depositing User: | HAFSHA AMALIA AFNAN |
Date Deposited: | 17 Mar 2022 03:37 |
Last Modified: | 17 Mar 2022 03:37 |
URI: | http://eprints.ums.ac.id/id/eprint/99046 |
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