Supattra Puttinaovarat

This study develops a scalable customer segmentation framework for translating large-scale retail transaction data into actionable marketing and operational insights. The dataset comprised 3,150,173 point-of-sale records collected from 37 branches of a Thai retailer between January 2022 and March 2023. After preprocessing, 3,140,395 valid records were retained and aggregated into RFM profiles for 162,234 customers. Continuous Recency, Frequency, and Monetary variables were standardized using StandardScaler and clustered using K-Means. Candidate solutions from k = 2 to k = 8 were evaluated using WCSS and mean Silhouette Scores. A four-cluster solution, with a Silhouette Score of 0.3381, was retained based on the elbow pattern and business interpretability. The resulting segments were Champions, Potential Loyalists, At-Risk Customers, and Hibernating Customers. Champions represented 20.5% of customers and generated 57.0% of revenue, while Champions and At-Risk Customers jointly accounted for 52.3% of customers and 88.5% of revenue, indicating substantial revenue concentration rather than a strict 80/20 distribution. Product composition, weekday and monthly sales patterns, and branch-level performance provided additional operational insights. Unlike ranked RFM scoring, the framework retains continuous behavioral information and avoids predetermined score boundaries. The findings support segment-specific customer management, although validation across other retailers, longer periods, and alternative outlier treatments is required.

Handwoven textiles constitute a form of intangible craft heritage whose cultural value resides not only in finished artifacts but also in the embodied skills, motif knowledge, practitioner narratives, and community-based meanings through which they are produced and transmitted. However, many digital heritage platforms remain oriented toward visual presentation, product cataloguing, and archival access, offering limited support for communicating the interpretive, practice-based, and relational dimensions of craft knowledge. Addressing this gap, this study designed, developed, and evaluated a four-room digital showroom for Nakhon Si Thammarat Brocade, a traditional brocade textile of southern Thailand. Guided by Design Science Research methodology and heritage interpretation theory, the digital showroom was conceptualized as a cultural communication interface rather than as an online product display. Knowledge elicitation with master weavers and a user-needs survey informed the development of four connected spaces: the Historical Room, Aesthetic Room, Livelihood and Craft Value Room, and Learning-by-Doing Room. Together, these rooms organize user engagement through a progressive interpretive pathway, moving from cultural orientation and motif understanding to artisan-value recognition and guided creative participation. An end-user evaluation with 100 participants showed positive perceptions across functionality and usability (M = 4.13, SD = 0.72), content structure and organization (M = 4.06, SD = 0.79), cultural content quality (M = 4.34, SD = 0.73), and interactive learning-by-doing experience (M = 4.17, SD = 0.81). Users particularly valued the culturally grounded content, practitioner-centered narratives, and opportunities to apply cultural knowledge through virtual design activities. The findings suggest that a practitioner-informed and theoretically grounded digital showroom can extend digital heritage practice beyond passive product representation by supporting contextualized, interpretive, and participatory engagement with intangible craft heritage.

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making.

The preservation of tacit knowledge embedded in Southern Thai textiles remains a significant challenge because much of this knowledge is dispersed across private households and is inadequately supported by conventional object-centered documentation systems. This study developed a crowdsourcing-based digital storytelling platform that enabled communities to document, organize, and disseminate knowledge related to Southern Thai textile heritage. The platform integrated community participation, structured narrative authoring, and knowledge organization within a socio-technical system designed for cultural heritage documentation. To guide its development, the study proposed the Crowdsourced-Storytelling Intangible Cultural Heritage Framework (CS-ICH Framework) and operationalizes it through requirements analysis, iterative prototyping, and empirical user evaluation. The evaluation results indicated high levels of user satisfaction and positive user perceptions regarding knowledge accessibility, content organization, and the platform’s support for heritage preservation. These findings suggested that participatory digital platforms can effectively facilitate the documentation and dissemination of locally embedded cultural knowledge that is difficult to capture through conventional institutional systems. This study contributed to digital heritage research by providing a potentially transferable framework and design principles for integrating crowdsourcing and digital storytelling within platforms designed to preserve and transmit intangible and community-held cultural heritage.

The increasing availability of tourism information on digital platforms has improved tourists’ access to destination-related data. However, existing tourism information systems often lack effective integration between user preference information and geospatial data, limiting their ability to provide personalized and context-aware recommendations. This study proposes a personalized tourism recommendation system by integrating Geographic Information System (GIS) technology with association rule mining to analyze relationships between user preferences and spatial characteristics of tourist destinations. The proposed system provides map-based visualization, calculates distances between users and destinations, and generates personalized recommendations based on both user interests and spatial proximity. The implementation results demonstrate that the system can generate location-aware and personalized tourism recommendations, supporting users in identifying suitable destinations within their surrounding geographic context. The integration of geospatial processing with association rule mining improves recommendation relevance by incorporating both preference patterns and spatial proximity. Furthermore, the proposed framework has the potential to support more balanced spatial distribution of tourism activities by recommending geographically appropriate destinations rather than concentrating suggestions on highly popular locations. These findings highlight the value of combining geospatial technologies with data mining techniques to support tourism recommendation systems and spatially informed tourism planning.

Traditional methods for assessing sunflower yield across large agricultural fields are typically labor-intensive and time-consuming. This study explores the integration of unmanned aerial vehicle (UAV) imagery and the YOLOv11 deep learning model for automated sunflower head detection and yield estimation. Aerial imagery was collected from sunflower fields using UAVs, and a YOLOv11-based detection model was developed to identify sunflower heads efficiently. Model performance was optimized by tuning the Confidence Threshold and Intersection over Union (IoU) parameters. A total of 1290 image tiles derived from 215 UAV images were used for model training and evaluation. The dataset was divided into training and testing subsets with an 80:20 ratio. The optimal configuration, achieved at a Confidence Threshold of 0.50 and an IoU Threshold of 0.40, yielded balanced and accurate results, including a Precision of 0.84, Recall of 0.95, mAP@0.5 of 0.95, and an F1-score of 0.90. The findings demonstrate that parameter adjustment directly influences model detection accuracy and reliability. Overall, this study confirms that combining UAV remote sensing with YOLOv11 offers a robust and scalable approach for automated sunflower yield estimation, significantly reducing manual effort and processing time. Moreover, the proposed framework can be adapted for other high-value crops, contributing to the advancement of intelligent and data-driven agricultural management systems.

Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning.

Small local museums play a crucial role in safeguarding cultural heritage, but often lack the necessary resources and digital capabilities to engage younger visitors effectively. This study examines whether a mobile serious game can enhance engagement, intrinsic motivation, and cultural knowledge among Generation Z museum visitors. This study introduces Thai-Craft-To-Go, a mobile serious game that mediates intangible cultural heritage—specifically Thai textiles and handicrafts—for Generation Z. Grounded in Self-Determination Theory (SDT) and Flow Theory and operationalized through the Mechanics–Dynamics–Aesthetics (MDA) framework, the game translates cultural content into interactive play. We conducted an exploratory evaluation with 30 Generation Z participants using the Game Engagement Questionnaire (GEQ), the Intrinsic Motivation Inventory (IMI), and a 10-item knowledge test administered before and after gameplay. Results indicated high engagement—particularly Presence and Absorption on the GEQ—strong intrinsic motivation on the IMI (notably perceived competence and value), and significant knowledge gains (mean scores increased from 4.40 to 8.03; t(29) = 8.39, p < 0.001, d = 1.53). These findings suggest that a well-designed serious game can align museum learning with the digital habits of younger audiences, enhancing engagement, motivation, and cultural understanding. For small local museums, serious games provide a feasible and cost-conscious pathway to revitalize visitor experiences and support the intergenerational transmission of intangible cultural heritage in the digital age.