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AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.

This study aims to investigate the impact of social media advertising on clothing choices at Universitas Dinamika Bangsa Jambi students. In today's world, where many people, especially young people who frequently shop online, often struggle to accurately determine the quality of items. A quantitative approach was employed, with a survey as the primary method of data collection. A questionnaire was distributed online via Google Forms and successfully elicited responses from 102 active students who are also social media users. The sampling technique used was purposive sampling, with participants selected based on criteria that matched the focus of the study. The data were then processed using SmartPLS 4 software with the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to test the relationship between variables. The main findings indicate that social media promotions have a strong positive influence on students' clothing purchasing decisions. This underscores the crucial role of targeted advertising strategies in the digital world in shaping consumer preferences. This research is expected to serve as a guide for clothing entrepreneurs in developing online marketing plans that better suit the tastes and needs of students as their target market.

This research aims to examine how prices and reviews influence consumers 'purchasing decisions on the Shopee platform in Jambi City. The rapid development of e-commerce has encouraged consumers to be more careful and discerning when selecting products, making price and product reviews important considerations in buying decisions. This study employed a quantitative approach via a survey method for data collection. The questionnaires were distributed online and successfully gathered responses from active Shopee users in Jambi City. Purposive sampling was employed to make sure that respondents met research criteria. The information gathered underwent analysis using IBM SPSS Statistics 27, which involved conducting assessments of validity, reliability, and classical assumptions, and multiple regression tests to see the impact of each variable on buying decisions. The study revealed that product price and reviews hold an important and relevant impact on consumer buying decisions. This research indicates that the more competitive the price and the better the quality, the bigger the possibility of customers buying products on Shopee.

This study aims to compare the effectiveness of QRIS with other cashless payment methods in driving the development of the digital economy in Indonesia. The background to this study stems from the increasing use of digital transactions and the strategic role of QRIS as a national standard for QR-based payments. Google Forms was used to disseminate the survey online and successfully collected 100 respondents who were users of cashless digital payment services. A purposive sampling technique was used to ensure that respondents had experience using QRIS and other digital payment methods. Data were examined using the SmartPLS 4 program using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to test the relationships between variables. The results indicate that perceived ease of use, transaction security, and influence on digital behavior have a positive or significant influence on QRIS effectiveness. However, availability and accessibility variables did not have a significant effect. This finding indicates that QRIS effectiveness is more influenced by user experience and perceptions, rather than availability or ease of access. This research is expected to contribute to the development of strategies to increase digital payment adoption in Indonesia.

Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.

Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

Software development effort estimation is crucial as it is one of the key factors for successful software development. This research employs Random Forest to estimate software development effort. To achieve better results, the study combines the Random Forest method with Genetic Algorithm. The results show that the China dataset provides more accurate estimation compared to the Desharnais dataset, because the China dataset uses relevant feature selection for estimation.

This Community Intervention activity aims to optimize the identity of Ujung Piring Village through place branding, which includes creating a village logo, profile video, and designing a site plan for the village square. The main problems faced by the village are the lack of a strong visual identity to optimise its potential and structured planning of village space/facilities. The methods used in the village site plan development activity were observation, interviews, and focus group discussions (FGDs). The results of the activity showed that the village logo successfully represented local values and history symbolically; the profile video was an effective medium for public communication; and the village square site plan provided direction for the development of a functional, aesthetic public space that supports social interaction. This activity proves that place branding can strengthen village identity, increase community engagement, and open up opportunities for village economic development. Further evaluation and monitoring are needed to assess the long-term impact of the implementation of this work program.

This study examines the video content production process at Digital Hub Sinar Mas Land as a visual communication strategy to support branding for a digital innovation area. The background of this research stems from the increasing need for organizations to produce creative and informative content as social media becomes a primary channel for information distribution. The study aims to describe the stages of pre-production, production, and post-production, and to identify challenges that affect the effectiveness of video content creation. A qualitative descriptive method was used through active participation, observation, interviews, and literature review. The findings show that concept planning, scriptwriting, and storyboard development form the foundation of successful production. The production stage emphasizes camera techniques, talent coordination, and visual consistency aligned with communication goals. The post-production stage focuses on technical editing to strengthen the audiovisual message structure and prepare content for publication. Key challenges were found in storyboard development, talent performance during filming, and audio processing that required detailed adjustments. The results highlight the importance of thorough planning and coordinated teamwork to ensure that video content effectively supports branding strategies.

Digital portfolios are a crucial tool for professionally showcasing abilities and learning objectives. Nevertheless, the projects and certificates of participants in the Independent Study program at VINIX7 are still kept apart and are not controlled by an integrated system. This requirement is the basis for the research's design and development of a website-based digital portfolio system that will serve as participants' key platform. Requirements analysis, system design, implementation, and testing are all steps in the Waterfall approach of system development. The Laravel framework was used to create the VinixPort website, which is backed by a MySQL database. The system has tools for managing portfolio material, talent evaluation, user registration and login, and data presentation via analytics. The study's findings show that the VinixPort website was created successfully and that all of the system's primary features work as intended. This service helps users create organized digital portfolios that are readily available and prepared for both professional and academic use.

Corporate value plays a crucial role in evaluating the success and sustainability of a business entity, influenced by various internal factors such as special connections, transfer pricing practices, and the effective tax rate (ETR). This study aims to examine the impact of these variables on the value of companies in the property and real estate sector on the Indonesia Stock Exchange, and to examine the role of corporate social responsibility (CSR) as a moderating variable. The study's findings reveal that special relationships and less transparent transfer pricing practices generally decrease company value, while efficiency in ETR can increase it. CSR acts as a moderating factor, strengthening the positive relationship between internal variables and company value while reducing the negative risks of unethical practices. This study recommends the implementation of more solid governance and transparency in business activities to increase competitiveness and company value in the Indonesian property and real estate industry.

This study aims to examine the influence of investment, labor, and unemployment rates on economic growth in Aceh Province. Economic growth is one of the main indicators in assessing the success of a region's development, so it is important to understand the factors that can influence it. The data used in this study are secondary data in the form of annual data for the period 2009–2023 obtained from the Central Statistics Agency (BPS) of Aceh Province and several related institutions. Data analysis was conducted using the multiple linear regression method with a time series approach. The results of the analysis show that the investment variable has a positive and significant influence on economic growth in Aceh Province, indicating that increased investment can encourage increased regional economic output. The labor variable also has a positive influence on economic growth, although its significance is lower compared to investment. Meanwhile, the unemployment variable has a negative and significant influence on economic growth, which means that increasing unemployment rates can suppress the rate of economic growth in Aceh Province.

This study presents a meta-analysis of open-access accounting information systems (AIS) literature in Indonesia during the digital transition period of 2015-2025. The primary objective is to identify and map the taxonomy of Independent Variables (X) and Dependent Variables (Y) predominantly used in academic and practical research. Through a systematic review of 15 key accredited articles with Digital Object Identifiers (DOI), this research finds that AIS success determinants (Variable X) have evolved from purely technical factors to integrative clusters encompassing Human Capital (competence, training), Organizational (culture, management commitment), and Technological (infrastructure, internal control) aspects. Meanwhile, Dependent Variables (Y) have shifted from mere technical user satisfaction to strategic impacts such as financial report quality, operational efficiency, and MSME business performance. These findings indicate that AIS research in Indonesia is heavily influenced by public sector regulatory contexts and cloud technology adoption in the MSME sector. This report serves as a reference framework for future researchers to explore emerging variables such as artificial intelligence and cybersecurity behavior within the accounting ecosystem.

Schools serve as environments where children receive a formal education. In addition to serving as educational institutions, schools can also pose a risk for spreading diseases. Diarrhea accounts for up to 65% of deaths, while other illnesses contribute to 26%. Healthy and clean living practices involve initiatives to offer learning experiences or establish environments for individuals, families, groups, and communities by facilitating communication, sharing information, and arranging educational opportunities to enhance knowledge, attitudes, and actions. This community service seeks to impart knowledge and awareness of clean and healthy living habits to Sekolah Dsar children, enabling them to incorporate these practices into their everyday lives. This community service is conducted through counseling delivered via lectures, interactive discussions, and simulations utilizing LCD media and instructional materials. The event was successful, and everyone involved was excited about this service project.

This study aims to analyze the role of Village-Owned Enterprises (BUMDes) in improving community welfare in Tanarawa Village, Waiblama District, Sikka Regency, and to evaluate the implementation of BUMDes programs. Tanarawa Village has primary economic potential in the plantation and food crop sectors, managed largely by the community as farmers. BUMDes was established as an instrument for village economic empowerment with the aim of increasing Village Original Income (PAD), strengthening the local economy, and maximizing the utilization of natural resources and human resources. This study used a qualitative descriptive approach with data collection techniques including observation, interviews, and documentation. The results indicate that BUMDes Tanarawa is still in the development stage, although it has been able to contribute to community welfare through the provision of basic necessities, training, outreach, and consultations related to business management. The success of BUMDes is influenced by government support, quality leadership, community participation, and transparent and accountable management. However, limited understanding by the community and village officials, as well as obstacles in implementing outreach, are inhibiting factors that need to be addressed. This study confirms that BUMDes plays a strategic role in local economic empowerment, improving community welfare, and equitable distribution of village economic growth.

Public speaking is an essential communication skill that plays a crucial role in enhancing self-confidence, delivering ideas effectively, and fostering social participation. However, many women in rural areas still lack opportunities to develop this skill due to limited access to training, gender stereotypes, and minimal experience in public forums. These challenges lead to low levels of women’s involvement, particularly housewives who are members of PKK, in community activities and decision-making processes at the local level. This study aims to examine the impact of public speaking training on improving the knowledge, self-confidence, and social participation of PKK mothers in Nagari Sumpur Kudus. The research employed a descriptive qualitative method with a participatory approach through interactive lectures, speaking practice simulations, reflective discussions, participatory observations, and qualitative evaluations. Data were analyzed using data reduction, data display, and conclusion drawing based on field notes, observations, and participants’ feedback. The findings indicate that the training successfully improved participants’ understanding of basic public speaking techniques, including intonation, eye contact, body language, and structured idea delivery. Furthermore, the participants experienced significant growth in self-confidence, courage to speak in front of groups, and motivation to continuously practice their communication skills. The interactive and supportive learning atmosphere also strengthened collaboration and solidarity among PKK members. Thus, public speaking training not only enhances individual competencies but also contributes to women’s empowerment and their active participation in community development at the village level.  

Job placement is a brief and concise summary of the process of placing employees in positions that match their expertise, skills, and knowledge within an organization. Human capital refers to the knowledge, skills, competencies, and attributes of individuals that contribute to economic and social performance. Teamwork is one of the important factors in increasing effectiveness and productivity in an organizational environment. Employee performance is one of the main indicators in determining the success and competitiveness of an organization. This study aims to analyze the effect of job placement, human capital, and teamwork on employee performance at PT. Faithfull The Brand. This study was conducted at PT. Faithfull The Brand. The research population was employees of PT. Faithfull The Brand. The sample in this study was 87 respondents who were determined based on the Slovin formula. The data analysis technique used was multiple linear regression analysis using the SPSS program. The results of testing the hypothesis stated that job placement had a positive and significant effect on employee performance at PT. Faithfull The Brand, human capital had a positive and significant effect on employee performance at PT. Faithfull The Brand, and teamwork had a positive and significant effect on employee performance at PT. Faithfull The Brand.

In an increasingly competitive workplace, employee performance is one of the important factors determining a company’s success. PT Gadjahmada Nusantarajaya, a company engaged in the service and trade sector, faces the challenge of maintaining and improving its employees’ performance. Internal factors such as organizational culture, work communication, and work discipline are thought to have a significant influence on employee performance. Based on this, this study was conducted to determine the influence of organizational culture, work communication, and work discipline on employee performance at PT Gadjahmada Nusantarajaya. The research questions in this study are: (1) Does organizational culture influence employee performance? (2) Does work communication influence employee performance? (3) Does work discipline influence employee performance? and (4) Do organizational culture, work communication, and work discipline simultaneously influence employee performance at PT Gadjahmada Nusantarajaya? This research is quantitative. Data were obtained through primary data collected using a questionnaire, as well as secondary data from company documents. The study population was all 49 employees of PT Gadjahmada Nusantarajaya. The sampling technique used saturated sampling; thus, the entire population was used as the research sample. Data analysis used validity tests, reliability tests, classical assumption tests, multiple linear regression analysis, and hypothesis tests (t-tests and F-tests). The results showed that partially (t-tests) the variables of organizational culture, work communication, and work discipline had a significant effect on employee performance. Simultaneously (F-tests), these three variables also had a significant effect on employee performance at PT Gadjahmada Nusantarajaya.