Amar Ma'ruf

The skincare business in Southeast Asia is rapidly expanding, requiring SMEs to adopt digital tools that support more accurate and data-driven decision-making. This study presents the integration of the Trend Moment forecasting method into a web-based e-commerce system developed for a skincare SME. Using 24 months of historical sales data, the system automatically generates short-term demand forecasts to assist inventory planning, stock replenishment, and production scheduling. The model achieved a forecasting accuracy of 9.54% MAPE, demonstrating that simple statistical methods can provide reliable operational insights when embedded directly into digital platforms. By linking transactional data with predictive analytics, the system enables business owners to transition from intuition-based decision-making to structured, data-informed workflows. The proposed solution illustrates a practical, lightweight approach to initiating digital transformation for skincare SMEs, offering scalability toward more advanced analytical capabilities in future developments.

Mobile reading applications are widely used to support digital learning and entertainment, yet most platforms still separate text-based reading from audiobook playback. This research proposes a cross-platform mobile application enabling synchronized reading and listening experiences through integrated text–audio interaction. Developed using the Flutter framework, the system incorporates Firebase Authentication, Cloud Firestore, and audiobook playback with adjustable speed and reading progress synchronization. A mixed engineering–UX evaluation was conducted, combining black-box testing and a user experience survey involving ten participants. Results show an 81.2% satisfaction score, indicating high acceptance of the multimodal reading experience. Findings highlight the potential of integrated audiobook–text applications to improve accessibility and flexibility for mobile readers. Additionally, this study proposes an extended architecture supporting AI-driven audiobook narration using neural text-to-speech models to enhance scalability and accessibility for future versions of the application.