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The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.

This study aims to investigate the factors influencing the continuance intention of Al-Kautsar Senior High School students in using metaverse-based learning media. The background of this research lies in the rapid adoption of immersive technologies in education, while students’ levels of acceptance have not yet been fully understood. The objective is to identify the antecedents of satisfaction, which subsequently influence continuous intention. The research model examines the effects of perceived interactivity, perceived sociability, perceived enjoyment, perceived ease of use, perceived security, and social influence on satisfaction. A quantitative approach was employed by distributing questionnaires to students, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that satisfaction is a very strong and statistically significant predictor of continuous intention to use metaverse applications (β = 0.716, p < 0.001). The six hypothesized antecedent variables were not found to have a significant individual effect on satisfaction. In conclusion, for digital native students at Al-Kautsar Senior High School, factors such as ease of use, interactivity, and enjoyment have shifted from being drivers of satisfaction to becoming basic expectations (hygiene factors). Satisfaction itself emerges as the primary determinant, likely influenced by more substantive elements such as content quality or pedagogical design rather than merely the technical features of the platform.

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

This study compares two service management models to evaluate the governance of the Wi-Fi network in Dusun Gita Nagari Baru. The main objective is to measure user satisfaction and service quality following the implementation of the COBIT 2019 framework, particularly the DSS02 domain (Manage Service Requests and Incidents). The research employed a mixed methods approach, using historical-comparative document analysis and Likert scale questionnaires distributed to 21 active users. The data were analysed through gap analysis, capability level mapping, and descriptive statistical analysis to identify performance differences between two periods. The results indicate that most indicators in the COBIT 2019 capability model are at Level 4 (Predictable), one indicator reaches Level 5 (Optimising), and another indicator is at Level 3. Indicators directly related to the DSS02 domain, such as ease of reporting, response speed, schedule accuracy, and repair time, demonstrate the most significant improvements. These findings support the hypothesis that implementing COBIT 2019-based governance for DSS02 can enhance user satisfaction and the quality of Wi-Fi network services in rural areas. This study also provides practical recommendations for the sustainable management of digital infrastructure in areas with limited access.

ABSTRACT Information services on campus are often delayed due to reliance on staff, resulting in long queues and inefficient waiting times. This study aims to design and develop a robotic interaction system based on speech recognition and Natural Language Processing (NLP), equipped with a virtual button as an alternative activation method. The system allows users to interact with the robot using voice, while the virtual button provides an additional option for users who are more comfortable with touch-based interaction. The research method employed is prototype development, which includes the design, implementation, and evaluation of the system. Testing was conducted to assess the effectiveness of the system in delivering information services quickly and accurately. The results show that the developed system can enhance service efficiency, reduce dependence on staff, and facilitate faster and more practical interactions between users and the robot. This study is expected to contribute to the development of technology-based public service systems, especially in the campus environment. Keywords: robotic interaction, speech recognition, NLP, virtual button, public service

Human daily life depends on air, so air quality needs to be protected especially against pollution. Decreasing air quality due to dust pollution can result in ARI. Makes it easier to measure ambient air and air temperature using internet-based technology. Designing internet-based dust and temperature measuring devices using the gp2y1010au0f type dust sensor, and DHS11 sensor as a temperature sensor, amplifier circuit, NodemCU microcontroller, and LCD (Liquid Crystal Display). The measurement application is carried out at the location of the PT. Djabus Tunas Utama Ngoro Mojokerto East Java at 10 sampling points around the mixing tube. The measurement results show the concentration carried by ambient air quality standards when the engine stops, so it is safe for employees. When the machine is mixing and the machine is not mixing (ordinary conditions) the concentration of particulates and the temperature exceeds the ambient air quality standard, for that all employees are required to use PPE.