This study examines the influence of personal selling and service quality in the Jenius app on user satisfaction and loyalty in Indonesia, in the context of increasing internet usage reaching 78.19% by 2023. Bank BTPN, through the Jenius app, capitalizes on technological developments to provide secure and efficient mobile banking services. Although the Jenius app is growing in popularity, various complaints related to bugs, OTP code issues, login difficulties, and transaction barriers have emerged, which may affect user satisfaction and loyalty. This study uses a quantitative method with the E-ServQual framework and Cognition Affective Behavior (CAB) theory to analyze data from 301 Jenius user respondents, which were collected through an online questionnaire using purposive sampling technique. The data was analyzed using partial least squares-structural equation model (PLS-SEM) with the help of SmartPLS 4. Of the nine hypotheses proposed, seven hypotheses were declared significant (accepted). So it can be concluded that the factors that influence the satisfaction and loyalty of Jenius application users are service quality (SQ) which consists of efficiency (EF), fulfillment (FUL), and system availability (SA). In addition, personal selling (PS) only directly affects user satisfaction (SAT), and user satisfaction affects loyalty (LO). Based on the accepted hypothesis, the research shows that application service quality and personal selling have a direct influence on Jenius application user satisfaction. However, user loyalty is only influenced by service quality, and not by personal selling.

Tantangan yang saat ini dibutuhkan oleh guru SMA adalah menciptakan inovasi pembelajaran berbasis teknologi. Saat ini kecerdasan buatan (AI) telah muncul sebagai solusi potensial untuk meningkatkan kualitas pembelajaran di tengah pesatnya kemajuan teknologi. Namun, geografi Indonesia yang luas membuat fasilitas teknologi pendidikan belum merata. Oleh karena itu, guru di SMA Muhammadiyah 2 Semarang perlu meningkatkan kompetensi literasi digitalnya, terutama untuk teknologi terkini. Program ini telah berhasil membuka wawasan guru terhadap teknologi baru dan memberikan keterampilan praktis dalam mengintegrasikan teknologi Kecerdasan Buatan Generatif ke dalam proses pembelajaran. Para guru diberikan pembekalan penggunaan teknologi ChatGPT dan Gemini untuk mempersiapkan bahan ajar. Hasil evaluasi menunjukkan bahwa guru mampu memahami dan mulai menerapkan teknologi AI dalam pembuatan materi ajar, serta merasa termotivasi untuk terus menggunakannya secara berkelanjutan dalam pembelajaran.

Self-service technology (SST) is a technological interface that allows customers to obtain services independently. SST includes self-checkout machines, food or ticket ordering kiosks, internet services, automated gas stations, and others. The adoption of SST has been an endeavor for firms to improve service quality, enhance operational efficiency, and increase the likelihood of achieving competitive advantage gains. However, empirical evidence is needed to better understand customer expectations of SST service quality and how SST service quality impacts customer loyalty. Therefore, the purpose of this study is to examine SST service quality and its impact on customer satisfaction and customer loyalty. This study adopts the quantitative method and the self-service technology service quality (SSTQUAL) framework and cognition affective behavior theory. Data collection in this study was carried out by distributing questionnaires via google form with a purposive sampling technique, namely the target respondents were SST users at McDonald's aged 18 to 55 years. The results of the questionnaire collection obtained 285 data analyzed by partial least squares-structural equation model (PLS-SEM) with the help of SmartPLS 4 tools. Based on the results of the analysis conducted, 4 hypotheses were accepted. Self-service technology service quality variables are influenced by 5 quality dimensions such as functionality, enjoyment, assurance, design, and convenience. In addition, the self-service technology service quality variable has a positive influence on the customer satisfaction variable, which in turn positively affects customer loyalty. In other words, the service quality of self-service technology has a positive effect on customer loyalty through customer satisfaction.

Cloud computing, a versatile computing service offering processing power and data storage, presents numerous advantages including flexible payment models, expedited marketing processes, adaptable costs and capacities, cost-effective disaster recovery solutions, and enhanced global collaboration efficiency. Despite its merits, assessing cloud computing service adoption remains a challenge, especially concerning long-term factors like customer loyalty. This study endeavors to identify the determinants of user loyalty towards cloud services and assesses the predictive strength of the model. Employing a quantitative approach, the research integrates the SERVQUAL framework and the stimulus organism response (SOR) model, incorporating cloud service quality variables. Data collection, utilizing Google Forms and purposive sampling, targeted cloud service users aged 18 to 60. Analysis of responses from 286 participants was conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS 4. The findings reveal the acceptance of 10 hypotheses, indicating significant influence of cloud service quality variables agility, service responsiveness, reliability, scalability, security, and assurance of service on user loyalty. Additionally, customer loyalty is influenced by factors including perceived brand image, customer satisfaction, and electronic word of mouth. The PLS-SEM model tested using PLSPredict that showed good strength for overall prediction variables. This study contributes to a deeper understanding of user loyalty dynamics within the context of cloud computing services, offering insights for service providers and researchers alike.

QRIS services on mobile payment applications are one of Indonesia's most popular payment methods, and they allow transactions by scanning or displaying the QR code. This enables mobile-based payments to be easy and flexible anywhere and anytime. As it grows and benefits, some obstacles exist regarding users' desire to continue using it. Some users decided to stop using the QRIS service for mobile payments due to indications of potential risks associated with the service. This research aims to find out what variables support or influence the intention to continue using QRIS services for mobile payments using quantitative methods and the information system success model (ISSM) and expectation confirmation model (ECM) frameworks by adding perceived risk and trust variables. The data collection technique used in this study was a questionnaires survey using Google Forms and applying purposive sampling techniques. The survey targeted QRIS service users aged 17 to 65 who experienced transactions using mobile payments (e-wallets and mobile banking). The data was collected from 513 respondents and analyzed using the partial least squares structural equation model (PLS-SEM) by Smart-PLS 4 software tools. The findings were that 10 hypotheses were declared accepted and five hypotheses were rejected. Based on the accepted hypothesis, research shows that satisfaction and trust influence the intention to continue using QRIS services for mobile payments. Satisfaction is a key factor that supports or influences a user's decision to continue using or utilizing QRIS payment services. The findings of this research can be an essential consideration for developers and companies providing QRIS services in mobile payments.

This research aims to predict customer churn in a telecommunications company using Logistic Regression (LR) and Gradient Boosting Classifier (GBC) algorithms. Customer churn poses a significant challenge as acquiring new customers is costlier than retaining existing ones. The dataset from Kaggle comprises 7043 records and 21 attributes. The process includes data pre-processing, cleaning, transformation, and normalization using a Min-Max Scaler. The data is split into features (X) and target (y), then divided into training and testing sets with an 80:20 ratio. Both models were trained and evaluated using a confusion matrix. Results show that the GBC model outperforms the LR model, with an accuracy of 83% compared to LR's 81%. This study demonstrates the effectiveness of GBC in predicting customer churn.