Purpose: The Identity Card (KTP) is a personal identification document mandatory for all Indonesian citizens. The Indonesian government, through Law No. 24 of 2013 on Population Administration and its implementing regulation, Permendagri No. 73 of 2022, regulates the detailed recording of identity in population documents to ensure official registration and protection of every citizen's identity. However, the issue of data leakage has become increasingly urgent in today's digital era. The case of the leak of 102 million KTP data from the Ministry of Social Affairs (Kemensos) raises significant concerns regarding the privacy and security of individual data. This data leak, allegedly spread on a hacker forum, highlights the vulnerabilities of the government's information technology infrastructure. Method: This research employs a document analysis method, collecting information from primary and secondary sources, including news reports, laws, and regulations related to data security, as well as case studies on cybercrime. An interdisciplinary approach is used, combining literature from law, information technology, and cybersecurity to gain a comprehensive understanding of the legal challenges in the E-KTP data leak case. Results: The findings emphasize the importance of strengthening regulations, raising public awareness, and enforcing strict legal measures to protect personal data. This research aims to provide insights into improving personal data protection and information security in Indonesia, offering recommendations for enhancing the protection of citizens' data in the future.

Purpose: This study aims to predict financial distress in transportation and logistics companies before, during, and after the Covid-19 pandemic. Research Methodology: The research subjects were 23 companies, and the study utilized an artificial neural network model. This study uses financial ratios, including the debt-to-asset ratio (DAR), Current Ratio (CR), and Return on Assets (ROA) as input variables in the artificial neural network architecture. The objectives of this study are to calculate the three ratios used as test data, determine the differences in financial ratios between companies reported as financially distressed and those not experiencing financial distress in the training data, identify the artificial neural network model architecture that produces good performance on the training data sample for use in testing predictions, and predict financial distress using an artificial neural network on transportation and logistics companies listed on the Indonesia Stock Exchange, which are part of the research sample. The research sample consisted of 20 transportation and logistics companies listed on the Indonesia Stock Exchange from 2015 to 2019. Results: The results reveal that companies reported as financially distressed have lower average values for the three ratios compared to companies not experiencing financial distress, making them suitable input variables. The best artificial neural network architecture in this study included an input layer with 60 neurons, a hidden layer with 15 neurons, and an output layer with one neuron. This architecture achieved a training performance mean square error (MSE) of 0.125004 and an R value of 50.00%. The study's findings suggest that 12 companies are predicted to experience financial distress.

Purpose: This study aimed to determine and analyze the influence of the e-performance assessment system, education, and competencmployee showsrformance on spiritual motivation at the Regional Office of the Ministry of Religion of the Riau Isla, Nds Province. Research Methodology: In this study, 104 respondents were used, namely, the State Civil Apparatus (PNS and PPPK) at the Regional Office of the Ministry of Religion of Riau Islands Province. The data obtained were analyzed using SmartPLS 3.0. Results: The results of this study indicate that the e-performance assessment system on employee performance has an original sample value of 0.161, a T statistic value of 2,013> 1.96 and a P value of 0.045 <0.05, it can be concluded that it has a significant effect, education on employee performance has an original sample value of 0.138, a T statistic value of 1,227 <1.96 and a P value of 0.220> 0.05, it can be concluded that it has no significant effect, competence on employee performance has an original sample value of 0.107, a T statistic value of 0.999 <1.96 and a P value of 0.318> 0.05, it can be concluded that it has no significant effect, spiritual motivation on employee performance has an original sample value of 0.551, a T statistic value of 5,636> 1.96 and a P value of 0.000 <0.05, it can be concluded that it has a significant effect, the e-performance assessment system towards spiritual motivation has an original sample value of 0.322, a T statistic value of 3.392> 1.96 and a P value of 0.001 <0.05, it can be concluded that there is a significant effect, education on spiritual motivation has an original sample value of 0.231, a T statistic value of 1.994> 1.96 and a P value of 0.047 <0.05, it can be concluded that there is a significant effect, competence on spiritual motivation has an original sample value of 0.299, a T statistic value of 2.792> 1.96 and a P value of 0.005 <0.05, it can be concluded that there is a significant effect, the e-performance assessment system on employee performance through spiritual motivation has an original sample of 0.178, a T statistic value of 3.059> 1.96 and a P Value of 0.002 <0.05, it can be concluded that there is a significant positive effect, education on employee performance through spiritual motivation has an original sample of 0.127, T statistic value 1.693 < 1.96 and P Value 0.091 > 0.05, it can be concluded that it has a positive but insignificant effect, competence on employee performance through spiritual motivation has an original sample of 0.165, T statistic value 2.507 > 1.96 and P Value 0.013 < 0.05, it can be concluded that it has a positive and significant effect.