Antecedents of Perceived Usefulness in the Continuance Usage Intention of QRIS Merchant Services: An Empirical Study of Users in Solo City, Indonesia

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This study aimed to investigate the antecedents of perceived usefulness in the continuance usage intention of QRIS merchant services in Solo City, drawing upon the Expectation Confirmation Model (ECM) and extending it with facilitating conditions and perceived risk. The cross-sectional survey data from 201 merchants using QRIS in Solo City and analyzed it using Smart-PLS. This study showed that confirmation significantly impacts perceived usefulness but does not directly affect satisfaction, indicating that the influence of confirmation on satisfaction operates through the mediating mechanism of perceived usefulness. Satisfaction emerges as the primary driver of continuance usage intention, confirming its central role in post-adoption behavior. Facilitating conditions significantly enhance perceived usefulness, while perceived risk diminishes it. Perceived usefulness significantly influences satisfaction but does not directly affect continuance usage intention. The mediating analysis revealed that perceived usefulness fully mediates the confirmation-satisfaction relationship, while satisfaction does not mediate the perceived usefulness-continuance intention relationship. The results of this study confirmed the substantial role of cognitive and affective factors in shaping continuance usage intention. This research contributes to the development of theory by validating the relationships drawn from the Expectation Confirmation Model, and the novelty is the introduction of facilitating conditions and perceived risk as antecedents of perceived usefulness in the ECM framework. The research findings provide some practical implications and valuable insights for QRIS service providers, policymakers, and merchants in the environmental condition where technological and economic changes may occur at any time without certainty.

Enhancing Customer Satisfaction through Talent Management and Innovative Work Behavior: The Mediating Role of Operational Performance in Reducing Lightning-Induced Claims in a Telecommunication Tower Company

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This study examines the effect of talent management and innovative work behavior on customer satisfaction through operational performance in reducing lightning-induced claims at XYZ Company in Area 1 Sumatra. A quantitative approach with a causal and cross-sectional research design was employed. The population consisted of 11,260 operational sites, from which 400 sites were selected using the Slovin formula. The unit of analysis was the operational site, while the units of observation included managerial representatives and customer representatives. Data were collected using a structured questionnaire measured on a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS). The results show that talent management has a positive and significant effect on operational performance and innovative work behavior. Operational performance also has a positive and significant effect on customer satisfaction. However, innovative work behavior does not directly affect operational performance or customer satisfaction. The direct effect of talent management on customer satisfaction is significant but negative, indicating that internal talent practices do not automatically improve customer satisfaction unless they are translated into better operational outcomes. Furthermore, operational performance significantly mediates the relationship between talent management, innovative work behavior, and customer satisfaction. These findings emphasize that customer satisfaction in lightning-induced claim management is more effectively improved through reliable operational performance supported by strong talent management and structured innovation practices.

Consumer Preferences for Purchasing Local Fruits at The Farmers Market Supermarket in Palembang City

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Fruit consumption in Palembang City remains below the World Health Organization (WHO) recommendation and has not reached an optimal level. Meanwhile, the increasing availability of imported fruits creates competition and affects consumer preferences toward local fruits. This study aims to identify the attributes influencing consumer preferences, analyze the dominance of physical and non-physical attributes, and examine the effect of imported fruit presence. A quantitative survey was conducted involving 100 respondents selected through accidental sampling. Data were analyzed using the Fishbein multi-attribute model, multiple linear regression, and path analysis. The results show that freshness, taste, and price are the main attributes shaping consumer attitudes. The attitude score (Ao) is categorized as high (101.167 or 67.44%), indicating that stronger positive attitudes are associated with higher consumer preferences for local fruits. Regression analysis reveals that product attributes significantly influence preferences, with physical attributes as the dominant factor. Meanwhile, the presence of imported fruits does not have a significant effect. These findings indicate that consumer preferences are mainly driven by product attributes and attitudes, suggesting that improving the quality of local fruits is essential to enhance their competitiveness.

Explainable Pneumonia Detection in Chest X-Rays: A Comparative Study of CNNs and Vision Transformers

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Pneumonia is a leading cause of global mortality, especially among children and the elderly, and chest radiography (CXR) remains the most widely used modality for its diagnosis. While deep learning has reached or exceeded radiologist-level performance on this task, the resulting models are still treated as opaque black boxes, which is a critical barrier to clinical deployment. In this work, we present a comparative and interpretable computer-aided-diagnosis (CAD) framework for pneumonia detection that combines three modern image-recognition backbones—a convolutional ResNet-50, a Swin Transformer (Swin-T), and a modernised convolutional network (ConvNeXt-T)—with Gradient-weighted Class Activation Mapping++ (Grad-CAM++) explanations. The three backbones were fine-tuned on the public Kermany chest X-ray dataset using a class-balanced training subset, weighted cross-entropy and an early-stopping protocol, and then evaluated on the held-out test set of 624 images. The Swin-T backbone achieved the best overall performance with a test accuracy of 95.51%, an F1-score of 0.95 and only 11 false negatives out of 234 normal cases, outperforming both ResNet-50 (93.11%) and ConvNeXt-T (88.94%). Grad-CAM++ heatmaps generated from the convolutional and transformer feature maps consistently localised on the affected pulmonary regions, providing radiologically plausible visual evidence for each prediction. Compared with five recent state-of-the-art pneumonia detectors, our Swin-T-based pipeline reaches a competitive accuracy while delivering layer-faithful visual explanations, supporting its use as a transparent decision-support tool in clinical workflows.

Effectiveness of Health Education in Improving Pregnant Women’s Knowledge of Triple Elimination Screening for HIV, Syphilis, and Hepatitis B

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Background: Triple elimination screening for HIV, syphilis, and hepatitis B among pregnant women is a strategic program in maternal and child health services to prevent intrauterine and mother-to-child transmission. The success of this program is determined not only by the availability of testing services but also by pregnant women’s level of knowledge regarding the benefits, procedures, and timing of triple elimination screening.

Objective: This study aimed to assess the effectiveness of health education in improving pregnant women’s knowledge of triple elimination screening for HIV, syphilis, and hepatitis B.

Methods: This study used a pre-experimental design with a one-group pretest-posttest approach. The study sample consisted of 196 pregnant women. Data were collected through interviews using a structured questionnaire to assess pregnant women’s knowledge. Respondent characteristics were analyzed descriptively. Knowledge was assessed before and after health education using a score ranging from 0 to 100. Differences in mean scores were analyzed using the paired sample t-test with a significance level of 0.05.

Results: Most respondents were aged 20-35 years (144 respondents; 73.5%), came from Gulak Galik Village (91 respondents; 46.4%), were unemployed (182 respondents; 92.9%), and underwent screening in the first trimester (150 respondents; 76.5%). Triple elimination screening results showed no reactive HIV or syphilis cases, whereas reactive HBsAg was found in 4 respondents (2.0%). The mean knowledge score increased from 57.59 ± 10.11 before education to 81.02 ± 11.11 after education.

Conclusion: There was a statistically significant difference in mean knowledge scores based on the paired sample t-test (t = 49.37; p < 0.001).

Digital Transformation and Bank Profitability in Indonesia: The Role of Firm Size as a Moderating Variable

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This study investigates the effect of digital transformation on bank profitability in Indonesia, with firm size examined as a moderating variable. Specifically, it assesses whether digital transformation enhances financial performance and whether larger banks are better positioned to benefit from digital initiatives. Using a quantitative research design, the study analyzes panel data from 41 banks operating in Indonesia. A fixed-effects regression model is employed to estimate the direct effect of digital transformation on profitability, measured by return on assets (ROA) and return on equity (ROE), as well as the moderating effect of firm size. The results show that digital transformation has a positive and statistically significant effect on both ROA and ROE, indicating that investments in digital technologies improve banking performance. In addition, firm size positively moderates the relationship between digital transformation and profitability. Larger banks appear to derive greater benefits from digitalization due to their stronger technological infrastructure, greater financial capacity, and higher ability to invest in innovation and human capital. The study concludes that digital transformation is a key driver of bank profitability and that organizational scale enhances its effectiveness. These findings contribute to the literature on digital transformation and banking performance and offer practical implications for bank managers and regulators in formulating digital strategies that align with institutional size and capabilities.

Effectiveness of Mozart Classical Music Therapy in Reducing Stress Levels Among Final-Year Medical Students at Nusa Cendana University

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Background: Stress is common among final-year medical students due to academic demands, thesis preparation, and clinical responsibilities. Mozart classical music therapy may serve as a simple non-pharmacological relaxation method to reduce stress.

Objective: To determine the effectiveness of Mozart classical music therapy in reducing stress levels among final-year medical students at Nusa Cendana University.

Methods: This quasi-experimental study used a one-group pretest-posttest design. Respondents were final-year students of the Medical Education Study Program, Faculty of Medicine and Veterinary Medicine, Nusa Cendana University. Stress levels were measured using the Perceived Stress Scale-10 before and after Mozart classical music therapy. The intervention was given for 15 minutes daily for seven consecutive days. Pretest and posttest stress scores were analyzed using a paired t-test.

Results: Before the intervention, most respondents had moderate stress (89.1%), followed by mild stress (7.3%) and severe stress (3.6%). After the intervention, mild stress increased to 30.9%, moderate stress decreased to 63.6%, and one respondent (1.8%) reported no stress. A significant reduction in stress level was found after therapy (p < 0.001).

Conclusion: Mozart classical music therapy significantly reduced stress levels among final-year medical students.

Explainable, Evidence-Based Verification of Arabic Claims via Multi-Source Retrieval and Cross-Lingual NLI

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We present a training-free, explainable system for verifying Arabic-language claims that combines Arabic Named-Entity Recognition (NER), parallel multi-source evidence retrieval, dense semantic reranking, and cross-lingual Natural Language Inference (NLI) under a single weighted verdict aggregator. Entities are extracted with CAMeLBERT-mix-NER and used to bias a parallel search over trusted Arabic RSS feeds, Google News, a verified-account X (Twitter) endpoint, and DuckDuckGo. Retrieved snippets are reranked by a multilingual-E5 encoder and scored by an XLM-RoBERTa-large checkpoint fine-tuned on XNLI/ANLI; per-source entailment and contradiction probabilities are combined through a weighted aggregator with multiplicatively capped priors over source authority, learned domain reputation, author credibility, and recency. We evaluate on the AraFacts benchmark and make the following contributions, each of which a reader can rely on: (i) a corrected, openly unit-tested aggregator that lets all retrieved evidence—not only official sources—drive the verdict; (ii) a rigorous, reproducible baseline study showing that AraFacts’s natural class imbalance (94% of claims are false-labelled) makes accuracy misleading and that even a well-tuned classical text-only classifier reaches only 0.40 macro-F1; and (iii) an explainable system packaged for deployment as a Streamlit application, a FastAPI service, and a Telegram bot, each exposing a per-source evidence trail. We also document and correct an evaluation error in an earlier version of this work. Code, scripts, and unit tests are released for full reproducibility.

FundusSSM: A Hybrid CNN–State Space Model with Geometry-Aware Ring-Scan Tokenization for Retinal Disease Classification

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Automated retinal disease classification from colour fundus photographs is a critical screening tool for early diagnosis of sight-threatening conditions, especially in regions with limited access to ophthalmologists. Convolutional neural networks (CNNs) and vision transformers have achieved strong performance in this task; however, both families treat the fundus image as a generic two-dimensional grid and ignore the well-known circular geometry of fundus photography and the concentric anatomical organisation of the retina. In this paper, we propose FundusSSM, a hybrid architecture that combines a pretrained ConvNeXt-Tiny feature extractor with a geometry-aware Ring-Scan State Space Model. The Ring-Scan tokenizer partitions the CNN feature map into  equal-area concentric rings that align with the optic disc, the macula, and the peripheral retina; each ring is then processed by a bidirectional Mamba block, and information is exchanged across rings every two layers through a lightweight cross-ring attention module. We evaluate FundusSSM on a 4,217-image, four-class fundus dataset (cataract, diabetic retinopathy, glaucoma and normal) under stratified five-fold cross-validation. FundusSSM achieves the highest mean F1-score among the evaluated models (95.78%), with a low cross-fold standard deviation of 0.59% that is smaller than those of the closest baselines (ConvNeXt-Tiny and Swin-Tiny), and it outperforms ConvNeXt-Tiny, Swin-Tiny, EfficientNet-B4 and ResNet-50 in mean F1. An ablation study confirms that the proposed Ring-Scan ordering reduces the cross-fold variance by approximately 46% relative to a raster-scan ablation that uses the same architecture but a standard row-major token order. We further introduce a ring-level explainability analysis that produces per-ring feature-contribution scores aligned with clinical anatomical zones, and we observe that the model concentrates most on central optic-disc tokens for glaucoma while activating all rings nearly uniformly for cataract — patterns that agree with how clinicians read the same images. We believe that the approach followed in this research and the achieved findings could be useful to other researchers who are interested in geometry-aware deep-learning models for fundus screening tasks.