PREDICTING SERVICE GAPS IN INDONESIAN MIGRANT WORKER PROTECTION USING ARTIFICIAL INTELLIGENCE AND THE CIPPO EVALUATION MODEL
Indonesia Indonesia Indonesia 
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Artificial Intelligence, CIPPO Evaluation, Migrant Workers
Abstract
Indonesian migrant workers continue to face various challenges throughout the migration cycle, including high placement costs, limited access to information, exploitation, legal problems, and inadequate reintegration services upon returning home. This study aimed to evaluate the effectiveness of Indonesian migrant worker protection services using the Context, Input, Process, Product, and Outcome (CIPPO) evaluation model and to develop an artificial intelligence-based prediction model for identifying service gaps. This study employed a qualitative evaluative approach using the CIPPO framework. Data were collected from 57 informants comprising government officials, prospective migrant workers, migrant workers abroad, and returned migrant workers through in-depth interviews, observations, and document analysis. The evaluation revealed that the Context dimension achieved 40.43%, Input 77.92%, Process 100%, Product 59%, and Outcome only 11.1%. The findings indicate that although program implementation was administratively effective, its impact on improving protection services remained limited. The artificial intelligence models successfully predicted service deficiencies and identified critical areas requiring policy intervention. Integrating artificial intelligence with the CIPPO evaluation model provides a comprehensive and evidence-based approach for predicting service gaps and strengthening Indonesian migrant worker protection policies through data-driven decision-making and digital governance strategies.
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References
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