Journal of Multimedia Trend and Technology

3D Bumper Animation Design as a Visual Identity Media for Wanarata Fest in Karangmangu Village

2026-01-11T12:29:52+00:00

Wanarata Fest is a cultural event held in Karangmangu Village with the aim of introducing the potential of local culture and wisdom to the wider community. To support the delivery of the village's identity and potential, visual media are needed that can create a strong and representative first impression. This research aims to design a 3D bumper animation as a visual identity for Wanarata Fest, showcasing the cultural elements and character of Karangmangu Village. The method used in this research is the Multimedia Development Life Cycle (MDLC), which includes the stages of concept, design, material collection, assembly, and testing. During the design stage, the selection of visual elements such as gunungan wayang (wayang puppet mountains), kuda kepang (horse-drawn carriages), joglo houses, and teak trees was based on observations and interviews with village officials to represent local cultural identity. Testing was conducted using the User Acceptance Testing (UAT) method, involving the committee and village officials as respondents. The test results showed that the 3D bumper animation received very positive ratings and acceptance, with all assessment indicators falling within the "very appropriate" category. This demonstrates that the designed 3D bumper animation is capable of representing the visual identity of Wanarata Fest and is suitable for use as an opening media for the event, with the potential for reuse in future events. Thus, 3D bumper animation can be an effective visual media solution in supporting the image and identity of local culture-based events.

Effectiveness of QRIS Implementation in Supporting Digital Transactions for MSMEs

2026-01-11T12:51:01+00:00

This study evaluates the effectiveness of QRIS implementation for MSMEs by examining its impact on transaction efficiency, business growth, financial transparency, customer adoption, and challenges encountered. Data were gathered using structured questionnaires and analyzed using descriptive statistics as part of a quantitative descriptive approach. The results show that QRIS positively impacts MSME performance. More than 90% of respondents believed that QRIS enables digital payments, and 61.3% of respondents embraced QRIS primarily for transaction simplicity. In terms of company success, 80.7% of respondents said that the number of transactions increased, with 35.5% reporting notable increases and 45.2% reporting just modest gains. With 96.8% of respondents saying that QRIS helps or substantially helps with transaction recording, it also promotes financial transparency. Given that 48.4% of their consumers noted that only 40–70% of them utilize QRIS, the frequency of consumer use remains moderate. Notwithstanding the advantages, a number of obstacles still exist, such as erratic internet connections (35.5%), a lack of consumer adaptation (22.6%), technical malfunctions (16.1%), and worries about Merchant Discount Rate (MDR) costs, which over half of MSMEs said were onerous. In conclusion, QRIS has proven effective in supporting MSMEs' digital transformation, yet improvements in infrastructure, customer education, and fee structures are required to maximize its impact.

Digital Vital Signs: Decision Trees as Behavioral Tripwires for Adolescent Smartphone Overuse

2026-01-11T12:59:06+00:00

Smartphones are a double-edged sword for teenagers; on the one hand, these devices provide a window to vast knowledge. However, the dark side of smartphones emerges when uncontrolled use is linked to mental health and exposure to negative content. Problematic smartphone use (PSU) occurs in 12–37% of adolescents and has been associated with sleep disturbances, depressive symptoms, and deterioration in academic functioning. Methods: We have trained an interpretable decision tree over a 1,000-participant dataset using stratified 80:20 splitting, class balancing, one-hot encoding, and grid search using cross-validation. Results: The model achieved 85.2% test accuracy (CV mean 85.0% ± 1.5%). Primary predictors were screen time per day (risk for >5.3 h/day associated with 4.3× increased risk), social media exposure (more than >2 h/day), and app variety (more than >5 apps/day). Extractable rules (e.g., >6.5 h screen time ∧ >2 h social media 92% precision for "high" addiction) permit tiered intervention thresholds. Conclusions: An interpretable decision tree provides strong prediction and converts insights into actionable behavioral thresholds for parents, schools, and developers for the purpose of early PSU intervention.

User Interface Design for Discrepancy-Simple Additive Weighting Evaluation Instrument

2026-01-11T13:12:18+00:00

In an effort to make it easier for users to understand how DSS-based instruments function without having to run the actual application, a user interface design is required. The purpose of this study was to show the quality of the user interface design of the Discrepancy-SAW evaluation instrument. The research approach used the R&D development model, which focuses on three stages of development. The three stages were design creation, initial design trial, and revision of the initial design trial. The subjects involved in the initial trial of the user interface design were 40 respondents. The trial tool was a questionnaire consisting of 10 questions related to the user interface design. Analysis of the trial data was carried out by comparing the percentage of user interface design quality with quality standards that refer to the eleven-scale categorization. The results of this study show the quality of the user interface design of the Discrepancy-SAW evaluation instrument in the good quality category by a quality percentage of 83.38%. The impact of the results of this study is that stakeholders in the field of education gain knowledge about the existence of the user interface design of the Discrepancy-SAW evaluation instrument. It is appropriate for use in determining the dominant factors that trigger inequality in the lecture process in the research methodology course.

Analysis of Factors Affecting Student Interest in Using Mobile Banking

2026-01-11T13:27:20+00:00

Study aims to analyze the effect of trust, compatibility, perceived risk, risk acceptance, and satisfaction on students’ interest in using mobile banking applications. Mobile banking has become one of the most widely used digital financial solutions due to its convenience and practicality, especially among young users who are familiar with technology. This research employs a quantitative approach using multiple linear regression analysis. The data are derived from a secondary dataset titled “COGENT ADOPTION M-BANKING PLS,” which contains survey responses from 219 students who use mobile banking services. The analysis results reveal that satisfaction, compatibility, and trust significantly influence students’ interest in using mobile banking, while perceived risk and risk acceptance show no significant effect. The coefficient of determination (R²) value of 0.570 indicates that the model explains 57% of the variation in students’ interest in mobile banking adoption. These findings highlight the importance of user trust, system suitability, and satisfaction as key factors in sustaining mobile banking usage. The study contributes both theoretically and practically by enriching the understanding of technology acceptance behavior among students and providing insights for banking institutions to enhance application design, strengthen security systems, and develop more user-oriented mobile banking strategies.

Classification of Hate Speech in TikTok Social Media Comments Using Naive Bayes Algorithm and TF-IDF Weighting

2026-01-11T13:37:31+00:00

This research focuses on the classification of hate speech in Indonesian Tik Tok comments. Tik Tok, as a social media platform with high interaction intensity, generates a large volume of comments with diverse linguistic characteristics, including the use of formal and informal language. This linguistic variation poses challenges in the content moderation process, particularly in automatically identifying hate speech. The research dataset is secondary data obtained by combining public datasets and scraped Tik Tok comments, with an initial total of 5,698 comments. The collected data represent general user comments with variations in formal and informal language. To improve data quality, pre-processing stages were carried out including text cleaning, tokenization, normalization, stop-word removal, and stemming. After pre-processing, 4,542 comments were obtained that were suitable for use in the modeling process. Experimental results show that the Multinomial Naïve Bayes model with TF-IDF weighting is able to classify hate speech with high performance. Model accuracy reached 93% before parameter optimization and increased to 95% after hyperparameter tuning with an alpha value of 0.5. The confusion matrix results show a relatively low misclassification rate, although the class distribution in the dataset still shows imbalance. The findings of this study indicate that the Multinomial Naïve Bayes approach is effective in recognizing linguistic patterns of hate speech in Indonesian TikTok comments, including text with informal language characteristics.