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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 aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

Social media has become a strategic tool for the government to disseminate public information quickly, interactively, and efficiently in the digital era. The Lampung Provincial Government utilizes various social media platforms such as Facebook, Instagram, and TikTok to support public communication activities. This study aims to analyze the effectiveness of public communication by measuring the level of activity of Regional Apparatus Organization (OPD) social media accounts using the logistic regression method. Data were collected through web scraping techniques on the official OPD social media accounts and then processed using a quantitative approach. The results show that the level of social media activity influences the effectiveness of public communication and the transparency of government information. These findings are expected to serve as a basis for local governments in designing public communication strategies that are more optimal and adaptive to developments in digital technology.

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.

The bodywork industry in Indonesia is under high competitive pressure, requiring companies to be more adaptive in understanding customer behavior in order to maintain business continuity. PT. Bengawan Karya Sakti as one of the national bodywork companies, has not optimally utilized historical transaction data to assess customer loyalty. This study aims to identify customer loyalty segmentation through the application of the RFM (Recency, Frequency, Monetary) method, which is used to analyze sales transaction data in 2022 and 2023. The study uses the CRISP-DM approach which includes the stages of business understanding, data exploration, data cleaning and processing, modeling, evaluation, and implementation of results. The transaction data analyzed includes attributes of transaction date, customer, number of transactions, and transaction value, which are then processed into RFM scores based on the transaction year and classified into categories such as Very Loyal, Loyal, At Risk, and others. The segmentation results show an increase in the number of very loyal customers from 2022 to 2023, as well as a significant decrease in inactive and at-risk customers. The chi-square statistical test shows that the difference in customer distribution between years is statistically significant (p-value <0.05), indicating a real influence from the company's strategy or external factors. The main conclusion of this study is that the RFM method is effective in the bodywork industry to support data-based marketing decision making and more targeted customer retention strategies.

Aset sangat penting bagi suatu organisasi karena dapat menunjang kegiatan yang dilakukan seperti pada Yayasan Assanusiyah. Banyaknya lembaga pendidikan dan perusahaan yang dikelola, aset yang dimiliki Yayasan pun cukup banyak. Supaya aset dapat bernilai tinggi, perlu adanya pengelolaan aset. Kegiatan inventarisasi merupakan salah satu pengelolaan aset yang telah dilaksanakan oleh Yayasan Assanusiyah. Namun kegiatan inventarisasi masih dilakukan secara manual dan tidak terintegrasi. Sehingga data aset Yayasan tidak realtime dan sering terjadi kehilangan maupun penumpukan aset pada satu lokasi. Tujuan dari penelitian ini yaitu untuk menganalisis kebutuhan sistem informasi yang tepat untuk membantu kegiatan inventarisasi aset Yayasan dengan menggunakan teori The Unified of Acceptance and Use of Technology (UTAUT) yang terdiri dari empat dimensi, diantaranya performance expectancy, effort expectancy, social influence, dan facilitating conditions. Metode yang digunakan dalam penelitian ini yaitu metode penelitian deskriptif dengan menggunakan pendekatan kualitatif dan kuantitatif. Teknik pengumpulan data yang digunakan yaitu wawancara, observasi, dan studi dokumentasi. Penelitian ini menghasilkan data mengenai kebutuhan sistem informasi yang sesuai untuk digunakan dalam pelaksanaan inventarisasi aset di Yayasan Assanusiyah.

This paper presents n conceptual model of Internet-based business-consumer relationship marketing with a focus on the context, content, and the process of relationship development from the consumer's perspective. The model con be divided into two broad groups. The first group is a set of contextual factors (environment, parties to the relationship, consumption task) that influence relationship development. The second group is the content and process of relationship development that can be viewed via three interrelated conceptual phases: consumer motives to seek interaction, exploration and interaction, and relationship bonding. With the rapid growth of E-commerce and on-line consumer shopping trends, the importance of building and maintaining customer loyalty in electronic marketplaces has come into sharper focus in marketing theory and practice. This paper also to present a conceptual framework of "e-loyalty" and its underlying drives.

Study for the effect of share trading days to the daily share returns in Stock Exchange has been carried out most invarious countries. Overseas studies produce consistent findings that the daily share returns results in negative impact on Monday (Monday Effect) and resulting inpositive impact on Friday (Week End Effect). Those effects are called as day of the week effect. The similar studies carried out Indonesia have not obtained consistent findings as similar as overseas including overseas studies make use of Ordinary Least Squares (OLS). Whereas this study does not utilize this approach, as most of the assumptions could not be fulfilled. Technical Approaches dealt with the share returns calculation dominantly affect to the amount of daily share return. These are due to the facts that the irrational factors also used by the investors to do the transactions. Consequently, the demand and supply of the share are also influenced by the behaviour of the investors them elves. Statistical approach used for examining the day effects is Autoregressive Moving Average (ARIMA) Approach. This approach is chosen as there is no hetero scedasticity or there is an existence of stationary data. To examine the difference of share trading day effects to the share returns this study utilizes Analysis of Variance (ANOVA), 244 daily aggregate share price indexes (IHSG) and LQ45 Indexes are used for this study. The findings of this study suggest that (1) all of the share trading days give a positive impact on the share returns, (2) daily aggregate share price indexes (IHSG) do significantly affect the share returns on Thursday and (3) LQ45 Indexes do significantly affect the share returns on Thursday and Friday. These findings imply that the investors should consider the day effects in deciding to be involved in share trading so that they could maximize their future returns.