<![CDATA[The complexity of task and workflow management in the digital era necessitates intelligent assistive tools. This study designs an Intelligent Personal Productivity Assistant System by integrating three primary components: (1) A Large Language Model (LLM) GPT-4o serving as the processing core for natural language understanding and contextual response generation; (2) The n8n low-code automation platform, acting as an orchestrator to manage workflows and connect the LLM to external APIs such as Google Calendar; and (3) A Telegram Bot utilized as the primary user interface due to its high accessibility. The design methodology employs a Prototyping approach adapted from Pressman & Maxim (2020), enabling iterative development and functional validation. Black Box Testing was applied for functional evaluation, while the System Usability Scale (SUS) was used to measure usability, involving 20 active university students in the Bogor region selected through Purposive Sampling. The research resulted in a functional prototype capable of managing schedules end-to-end via conversational natural language. The system achieved an average SUS score of 88.5 from 20 respondents, classified as Excellent with a Grade A rating, indicating the significant potential of integrating LLMs and workflow automation to effectively enhance personal productivity.]]>
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