Jurnal Teknologi Informasi dan Pendidikan

The Role of Digital Platforms and Online User Interaction in Shaping Tourist Emotional Satisfaction

2026-01-23T07:49:02+00:00

Tourist experiences in the digital era are shaped not only by on-site physical interactions but are increasingly influenced by digital platforms, online reviews, and social media. Although the literature on digital tourism is growing rapidly, a gap remains in understanding how digital interactions holistically and integratively influence tourists' emotional satisfaction, particularly in coastal destinations. This study aims to examine the role of digital and information technology-based factors in shaping tourists' emotional satisfaction in coastal destinations by developing an integrated model that links the physical and social environments, digital experience quality, electronic word of mouth (e-WOM), digital destination image, and tourist engagement. Data were collected from 339 tourists who actively used digital platforms during their visits to a coastal destination in Indonesia. A mixed-method analytical approach was employed, combining Partial Least Squares Structural Equation Modeling (PLS-SEM) to test linear relationships and fuzzy-set Qualitative Comparative Analysis (fsQCA) to uncover complex configurational pathways leading to high emotional satisfaction. The findings indicate that digital experience quality and e-WOM significantly strengthen digital destination image, which in turn enhances tourist engagement and emotional satisfaction. Tourist engagement emerged as the strongest direct predictor of emotional satisfaction. The configurational analysis revealed multiple alternative pathways leading to high satisfaction, with digital destination image identified as a necessary condition across all configurations. This study offers theoretical contributions by integrating environmental psychology and digital tourism literature into a comprehensive model and by applying a mixed-method approach (SEM-fsQCA) capable of capturing causal complexity. Practically, the findings underscore the importance for destination managers to not only manage physical attractions but also to proactively build a strong digital destination image, encourage positive e-WOM, and create interactive and engaging digital experiences to enhance tourists' emotional satisfaction.

Lightweight YOLO Models for Robust Facial Expression Detection

2026-03-31T05:47:45+00:00

Facial expression recognition is a fundamental component of artificial intelligence systems, particularly in human–machine interaction. However, achieving robust detection accuracy remains challenging due to variations in lighting, facial orientation, and limited training data diversity. While recent lightweight YOLO architectures—YOLOv8n, YOLOv10n, and YOLO11n—have demonstrated strong performance in general object detection, comparative studies evaluating these models specifically for facial expression detection remain limited. This study addresses this gap by systematically comparing these three nano-variant models on a dataset of 2,000 labeled facial images across four expression categories: flat face, angry, sad, and smile. The dataset was divided into training (70%), validation (20%), and test (10%) subsets. Experiments were conducted under two scenarios—with and without data augmentation—using identical training configurations. Augmentation techniques included mosaic composition, HSV variation, geometric transformations, and flipping. Results show that augmentation improved the F1 score of YOLOv10n from 0.68 to 0.72 and YOLO11n from 0.65 to 0.72, with the latter achieving the highest overall precision of 0.82. YOLOv8n exhibited stable performance with an F1 score of 0.75 under both conditions. Confidence threshold optimization revealed distinct optimal operating points for each model, ranging from 0.1 to 0.6, confirming that per-model threshold tuning is necessary to maximize detection performance. These findings provide practical guidance for selecting and configuring lightweight YOLO models for facial expression detection in resource-constrained environments.

Design of Mobile Learning Application Interface Using Kansei Engineering Method (Case Study: Majelis Daur Ulang)

2026-03-07T08:30:47+00:00

Kansei Engineering is a method for realizing certain product designs based on a systematic exploration of human feelings and sensations (sight, touch, smell, hearing, taste). In mobile applications, the most important factor apart from the technical aspects of the system is the design. Design is an important factor in a mobile application because it becomes a liaison between the user and the existing system. The Mobile Learning application is not only enough to run the application and there are no errors, but the application must be built according to the wishes and interests of the user, this study aims to determine the emotional factors of the user, apply Kansei Engineering in designing and make recommendations for application display design according to Kansei Engineering. This methodology refers to Kansei Engineering Type I. This study uses Kansei Word to detect the user's feelings when looking at the specimen design. The list of Kansei Words used is 10 words related to the Majelis Daur Ulang mobile learning application. There are 5 specimens of similar Mobile Learning applications used. This study involved 32 participants, using multivariate statistical analysis, namely Cronbach's Alpha (CA), Correlation Coefficient Analysis (CCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Partial Least Square (PLS). This research resulted in 2 recommendations for the design Majelis Daur Ulang Mobile Learning display, namely "Professional" and "Unique”.