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This research was conducted with the aim of examining the factors that influence Non-Performing Loan (NPL), such as Return on Assets (ROA), Capital Adequacy Ratio (CAR), Bank efficiency, Diversification Income, Current Account Saving Account (CASA). The population in this research is from the banking subsector listed on the IDX during the period 2018 to 2022, using data from the annual financial statements of conventional banks. The data collection technique in this research uses the Purposive sampling method using secondary data, so that 75 data from 25 companies in the banking subsector in Indonesia are obtained. This research is processed by multiple linear regression analysis using JASP statistical data processing application. The research results can be interpreted that return on assets (ROA) has a negative but insignificant effect on NPL. Capital Adequacy Ratio (CAR), Diversification Income and Current Account Saving Account (CASA) have a negative and significant effect on NPL. Meanwhile, bank efficiency has a positive and significant effect on NPL.

Generative artificial intelligence tools have recently attracted a great deal of attention. This is because of their huge advantages, which include ease of usage, quick generation of answers to requests, and the human-like intelligence they possess. This paper presents a vivid comparative analysis of the top 9 generative artificial intelligence (AI) tools, namely ChatGPT, Perplexity AI, YouChat, ChatSonic, Google's Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora's Poe, paying attention to the Pros and Cons each of the AI tools presents. This comparative analysis shows that the generative AI tools have several Pros that outweigh the Cons. Further, we explore the transformative impact of generative AI in Natural Language Processing (NLP), focusing on its integration with search engines, privacy concerns, and ethical implications. A comparative analysis categorizes generative AI tools based on popularity and evaluates challenges in development, including data limitations and computational costs. The study highlights ethical considerations such as technology misuse and regulatory challenges. Additionally, we delved into AI Planning techniques in NLP, covering classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. These planning approaches are vital in achieving specific goals in NLP tasks. In conclusion, we provide a concise overview of the current state of generative AI, including its challenges, ethical considerations, and potential applications, contributing to the academic discourse on human-computer interaction.