Mental health disorders, particularly depression, are becoming an increasing concern among university students. This study aims to analyze the comparative levels of depression among students from the 2022 and 2024 cohorts in the Biology Department at Universitas Negeri Medan. The research employs a mixed-method approach, integrating both quantitative and qualitative methodologies. Data were collected through questionnaires distributed to 51 active students. The results indicate that students from the 2022 cohort exhibit higher levels of depression compared to those from the 2024 cohort, due to academic pressure, social factors, and lifestyle habits. These findings highlight the importance of preventive strategies and interventions to mitigate depression risks, such as stress management, promoting healthy lifestyle habits, and strengthening social support in academic environments. Therefore, educational institutions must prioritize student mental health to foster a more conducive learning environment.

Copyright of songs or music has proven to be able to open up almost limitless financial opportunities. It is difficult to determine how much a song is used for commercial purposes by parties other than the original creator. A major challenge in the music industry is the fair and transparent distribution of copyright royalties for songs. To address this issue, it is important to explore international best practices and case studies that reveal successful royalty distribution models and technology-based solutions for accurate tracking and payment. Important aspects of a fair and transparent royalty distribution model include transparency in revenue sharing, fair allocation based on popularity and usage of songs, and simplification of the royalty collection process through technology. The research method used is normative legal research. This research is prescriptive and applied. The method of collecting legal materials is by means of literature study and the legal materials used are primary legal materials and secondary legal materials. This study evaluates the effectiveness of the implementation of Law Number 28 of 2014 concerning Copyright and Government Regulation Number 56 of 2021. This study also identifies the legal, technical, and social challenges faced in protecting the rights of songwriters and ensuring fair royalty payments.

Good financial management at the village level is crucial to support development and the welfare of the village community. However, many villages still face challenges in financial management, one of which is the low quality of human resources (HR) and the lack of integrity among village officials. This study aims to analyze and evaluate the influence of integrity on village financial management, as well as to analyze and evaluate the influence of human resource quality on village financial management, using 35 village governments in Sidoarjo Regency as the research location. This research was conducted by distributing questionnaires to 232 respondents, with the study population being the entire population of Sidoarjo Regency, totaling 1.977 million people, to examine the independent variables (integrity and HR quality) in relation to the dependent variable (village financial management). The method used in this research is Structural Equation Modeling (SEM), with data processing conducted using Smart-PLS 4 software. The results of the study show that the integrity of village officials has a positive influence on village financial management at a rate of 0.444, meaning that the higher the integrity of village officials, the better the financial management of the village. Additionally, HR quality was also found to have a significant impact on village financial management at a rate of 0.420. This research contributes to improving village financial management by emphasizing the importance of integrity and the quality of human resources in village governance.

This research aims to explore how cybercrime occurs in Indonesia and the government's efforts to tackle it. By understanding the various incidents that occur and the factors that cause them, it is hoped that this research can contribute to the development of policies and prevention strategies that are more effective in dealing with cybercrime threats.  The research method used is descriptive qualitative research with a normative juridical approach. Qualitative descriptive research with a normative juridical approach is research that tries to describe an event or incident that occurs directly, real, realistic, actual in the existing rules. The results and discussion of this research show that cybercrime, especially data theft and hacking of government websites, is a serious problem that requires immediate attention and action. With the right measures, it is hoped that Indonesia can reduce cybercrime incidents and improve overall cybersecurity. Overall, the results of this study provide a comprehensive overview of the challenges Indonesia faces in dealing with cybercrime threats as well as the steps that need to be taken to improve cybersecurity in the country.

Health risk stratification is crucial for preventive healthcare, yet existing models often rely on binary classification generalized disease prediction, neglecting personalized health indicators and graded risk levels. Many studies apply feature selection techniques like Relief and Univariate Selection without quantifying the weighted impact of features. To address these gaps, this study introduces a Big Data-driven Health Index (HI) framework using PySpark for scalable health risk stratification. The HI is computed as a weighted sum of health-related features using SHAP Analysis, XGBoost, Random Forest, and Correlation Analysis. PySpark enables efficient processing of large-scale health data, and individuals are classified into Low and High Risk. Optimal classification thresholds are determined using the Youden Index from the ROC curve to balance sensitivity and specificity. Personalized health recommendations are generated based on risk categories to guide preventive interventions. Performance evaluation reveals that Correlation Analysis achieves 100% precision and 98.90% recall, outperforming other methods. SHAP prioritizes recall but has low precision, while XGBoost and Random Forest improve precision but struggle with recall. By leveraging Big Data techniques with PySpark, this study enhances computational efficiency, scalability, and classification accuracy, addressing prior research limitations and providing a robust data-driven approach to personalized health monitoring.

Expanding extensive and intricate social networks has required sophisticated community detection techniques. This study presents an innovative hybrid methodology that utilizes node space similarity and local knowledge to enhance community identification. Node space similarity is defined by integrating eigenvector centrality (EC), which quantifies a node’s influence inside the network, with proximity metrics, such as closeness, to evaluate the connectivity between nodes. This enables us to identify cohorts of individuals with analogous influence and connectivity. We use local knowledge by concentrating on these pivotal nodes' direct connections and attributes, allowing the technique to broaden community discovery (CD) outward effectively. Our five-phase methodology, grounded in an iterative seed expansion algorithm, commences with identifying highly central nodes and progressively develops communities by integrating nodes exhibiting high similarity and local connectivity. The method incorporates graph statistical inference and embedding features to improve accuracy and capture extensive network patterns. This integrated approach facilitates the precise and effective identification of communities within extensive social networks, exceeding the constraints of conventional techniques. This research attained a modularity of 95.05% on the DBLP dataset and 94.50% on the Amazon dataset. This study achieved a Normalized Mutual Information (NMI) of 91.80% on the DBLP dataset, 92.50% on the Amazon dataset, and 90.43% on the football dataset, demonstrating superior performance relative to previous methodologies. The findings indicate that the hybrid method outperforms other recognized methods in large-scale graphs, showcasing notable robustness and efficiency.

The economic position of a city is crucial for the sustainability of regional economic development. To enhance competitiveness and achieve economic excellence, a combination of simultaneous drivers is necessary. As the core city of the Kedungsepur agglomeration, Semarang City plays a strategic role in regional development. This study employs a quantitative methodology, utilizing the Location Quotient (LQ) technique with data sourced from the Central Bureau of Statistics (BPS). The findings indicate that Semarang City's economic transformation, characterized by competitiveness and specialization in the trade, real estate, and public service sectors, requires support from surrounding regions to maximize its role in driving sustainable economic growth. Additionally, the study identifies the city's prime sectors—trade, real estate, public services, and transportation and warehousing—highlighting their contribution to generating high-added value amid global economic dynamics. Despite its economic diversification, Semarang City remains heavily reliant on the manufacturing industry due to its substantial sectoral contribution. Therefore, while manufacturing remains a key pillar of the local economy, identifying new growth potentials and fostering collaborative strategies within the agglomeration area is essential for long-term economic resilience. Semarang City possesses significant potential for sustainable growth by distributing the benefits of development to its surrounding regions, reinforcing its role as a regional economic driver.

Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature.
Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation.
Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106.
Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.

PT Pelindo Petikemas Belawan. is a container service company. This paper aims to determine the role of the Planner in container loading and unloading activities in the operational division engaged in container loading and unloading. To support the smooth running of loading and unloading work is greatly influenced by the role of the planner in it as well as the loading and unloading equipment facilities and procedures related to the loading and unloading. The role of the planner in operational activities is to receive emails from the shipping company (agent) then the planner immediately makes a loading list that has been sent by the shipping company to the planner with that the planner also determines the place to stack containers on the ship and regulates the equipment that operates for loading and unloading containers. In the role of the planner in loading and unloading activities, better accuracy and supervision are required, because without supervision and accuracy in loading and unloading activities, it can result in errors in exporting/importing containers and damage to the cargo to be loaded/unloaded. In writing this paper, the method used by the author in obtaining data is based on direct observation in the field related to the object, literature analysis and direct interviews.

Character education that teaches moral values, ethics, and positive attitudes must be applied early in the learning process to form individuals who are not only academically intelligent, but also have integrity and social responsibility. The purpose of this study is to provide assistance to teachers in implementing character education amid the challenges of the globalization era. In addition. The research was conducted in Candi Village, Tanah Sepenggal Sub district, Bungo District, Jambi Province, involving the teachers' assembly. The results showed that the mentoring successfully improved teachers' understanding and readiness to implement character education. About 90% of the teachers involved reported improvements in their understanding of the basic principles of character education and ability to integrate them in learning. The implications are therefore important for the development of character education in Indonesia, especially in facing the challenges of globalization. Continued assistance to teachers is expected to strengthen the implementation of character education at all levels of education.