LEGAL AND TECHNOLOGICAL ANALYSIS OF DAPODIK DATA FORGERY IN PKBM NON-FORMAL EDUCATIONAL UNITS
Keywords:
Dapodik, PKBM, data falsification, technology law, education information systemsAbstract
The digitalization of Indonesia's education system has provided the Basic Education Data (Dapodik) as a national database that supports administration, planning, and the distribution of educational assistance. In practice, the use of the Dapodik system in non-formal education units, such as Community Learning Centers (PKBM), still faces various issues, one of which is administrative data falsification. This data manipulation can include the inclusion of fictitious students, the manipulation of study group numbers, and the use of educator data that does not reflect actual conditions. These actions not only impact the validity of education policies but also have the potential to cause state losses and legal violations. This study aims to analyze the forms of Dapodik data falsification in PKBM, examine the legal accountability of data falsifiers, and analyze the role of technology in detecting and preventing educational data manipulation. The study uses a qualitative method with a normative juridical approach and an information technology approach. Data sources were obtained through library research, including legislation, legal literature, scientific journals, and various studies on educational information systems and cases of Dapodik data falsification. The research results indicate that the practice of falsifying Dapodik data occurs due to a weak monitoring system, suboptimal data validation, and low integrity of educational administration managers. From a legal perspective, these actions can be categorized as a form of document falsification and misuse of electronic systems, which are potentially subject to sanctions under criminal law and the Electronic Information and Transactions Law. Furthermore, advances in information technology can be utilized as a preventative measure through the implementation of automated validation systems, population data integration, digital audits, and artificial intelligence-based monitoring to detect irregular data.
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References
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