The increasing use of social media has significantly impacted wireless network performance, particularly in data transmission efficiency. This study aims to analyze the impact of social media usage on network congestion and wireless data transmission efficiency. Experimental and quantitative methods were employed, utilizing direct network traffic measurement and simulation techniques. The study assessed key parameters, including throughput, latency, packet loss, and jitter, to evaluate network congestion levels caused by social media activities such as video streaming, image uploads, and real-time communication. The results indicate a 15–30% decrease in throughput, an increase in latency from 30 ms to 120 ms, a packet loss surge from 0.5% to 4–7%, and a jitter increase from 10 ms to 50 ms during peak hours of social media usage. The study also found that channel bonding techniques can improve throughput by up to 63.6% under controlled interference conditions. These findings highlight the necessity of effective traffic management strategies, including 5G network deployment, machine learning-based traffic optimization, and Quality of Service (QoS) implementation, to mitigate network congestion issues caused by the exponential growth of social media users.

The Student Activity Unit (UKM) for Futsal at Multi Data Palembang University often experiences obstacles in tournament management, such as delays in match information updates, difficulty accessing information by participants, data recording errors, and inefficient time for compiling match brackets. This research aims to design a mobile-based futsal tournament management application called TeamKickOff using the Design Thinking method through five stages: empathize, define, ideate, prototype, and test. In the empathize stage, in-depth interviews were conducted with 12 respondents, participatory observation, and document analysis. The define stage produced an empathy map, user persona, point of view, and user journey map. The ideate stage involved brainstorming, card sorting, and wireframe development. Prototypes were developed in three versions (low, mid, and high-fidelity) with features for schedule management, real-time score recording, automatic match brackets, and video documentation. Usability testing with 32 respondents resulted in a SUS score of 78.6 with a task completion rate of 91.3% and a reduction in management time of up to 65% compared to manual methods. The research results show that the TeamKickOff application successfully improved the efficiency of futsal tournament organization and information access for participants, with advantages in video documentation integration and focus on the local campus context in Indonesia.

The Agricultural Office of Central Lombok Regency is a government institution that utilizes a website to provide information regarding various agricultural services to the public. This website is used by both the staff of the Agricultural Office and the public. To assess the level of user satisfaction with the website of the Agricultural Office of Central Lombok Regency, this study employs the End User Computing Satisfaction (EUCS) method, which measures satisfaction through five dimensions: Content, Accuracy, Format, Ease of Use, and Timeliness. The purpose of this study is to evaluate the extent of user satisfaction with the website using a Likert scale as the measurement tool. Data were collected through a questionnaire distributed to 55 respondents, consisting of both the staff of the Agricultural Office and the public who use the website. The results show that the level of user satisfaction with the website of the Agricultural Office of Central Lombok Regency falls into the "agree" category for all dimensions examined. The average satisfaction percentages for each dimension are as follows: Content (78.18%), Accuracy (75.2%), Format (76%), Ease of Use (74%), and Timeliness (75%). The Content dimension showed the highest percentage, indicating that the completeness and relevance of the information on the website significantly influence user satisfaction. Based on these findings, it is recommended to improve the website’s interface and update the information regularly to enhance the user experience. This study provides a clear picture of user satisfaction with the website of the Agricultural Office of Central Lombok Regency and can serve as a reference for future website development.

Financial statements are an important means of communicating financial information from accounting activities and assisting decision making. General Financial Accounting Standards are often considered too complex for entities without public accountability, so that SAK ETAP is present as a simpler solution. This study aims to analyze the application of SAK ETAP in presenting financial statements at the Nur Insani Sharia Savings and Loans and Financing Cooperative. The method used is descriptive qualitative with analysis of cooperative financial statement documents. The results of the study indicate that the cooperative has attempted to implement SAK ETAP, but several shortcomings were still found, such as incomplete notes on financial statements and inconsistencies in account classification. This study recommends improving staff understanding and completeness of reports according to standards to support transparency and accountability of cooperatives.

In the digital era, personal data protection has become a crucial issue due to the growing risks of privacy breaches. This study aims to explore the role of digital education in enhancing privacy literacy and mitigating the social impacts of data violations. Utilizing a mixed-methods approach, primary data was collected through online surveys and in-depth interviews with users and experts, while secondary data was analyzed from academic journals and industry reports. Results reveal that only 42.7% of respondents understood how to safely manage their personal information, reflecting a significant gap in privacy awareness. Moreover, a positive correlation (R = 0.72) was identified between improved privacy literacy and reduced social risks, such as identity theft and cyberbullying. Simulations proved to be effective educational tools, with every 10% increase in digital privacy literacy reducing social risks by 7%. The findings highlight the need for integrating privacy education into formal curricula at all educational levels, complemented by community-based initiatives for underserved populations. Collaborative efforts between governments, educational institutions, and technology organizations are essential to develop relevant and effective learning resources. The study underscores the importance of adopting holistic educational strategies to empower individuals in navigating the challenges of data protection in the digital age.

The rapid evolution of digital technology has reshaped workforce training paradigms, with Artificial Intelligence (AI) emerging as a transformative tool. This study investigates the opportunities and challenges of implementing AI-based training systems to enhance workforce skills in the digital era. Data were collected through surveys, in-depth interviews, and focus group discussions involving industry practitioners, AI developers, and workers. The results indicate a training effectiveness average of 70.2% (SD = 9.8) and satisfaction levels averaging 75.4% (SD = 8.5). Logistic regression analysis revealed a significant positive relationship between AI adoption and skill improvement, with a success probability of 68.9%. Despite these promising outcomes, challenges such as limited digital infrastructure, low digital literacy, and resistance to technology adoption persist, particularly in developing regions. This study provides strategic recommendations for inclusive digital transformation, emphasizing collaborative efforts between governments, private sectors, and educational institutions. These findings contribute to theoretical and practical knowledge for advancing AI-based training systems and fostering a sustainable, equitable workforce transformation.

The increasing use of mobile devices has increased the risk of data theft, posing significant security challenges for individuals and organizations. This study proposes an early detection system for data theft on mobile devices using machine learning algorithms. The system is designed to identify suspicious patterns in application usage, network access, and CPU/memory activity, providing early warnings to prevent potential data loss. By employing algorithms such as Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the developed models demonstrated significant performance: CNN achieved the highest accuracy of 95.1%, with a precision of 94.2%, recall of 93.5% , F1-score of 93.8%, and AUC-ROC of 0.96. Random Forest and SVM also showed competitive performance with accuracy rates of 94.7% and 92.5%, respectively. These findings highlight the high potential of machine learning algorithms for real-time detection of data theft threats, providing adaptive protection against evolving cyberattack methods. This approach offers a promising solution to strengthen mobile device security frameworks and safeguard user data against increasingly sophisticated cyber threats.

Interpersonal communication is a fundamental aspect in maintaining harmonious relationships between family members. In broken home families, the communication process often experiences obstacles due to division, conflict, or the absence of one of the parental figures, which has an impact on the psychological condition of the child and the dynamics of family relationships. This phenomenon is important to study in order to understand how communication can continue amidst these limitations. This study aims to determine the form of interpersonal communication that occurs in broken home families, especially in terms of six main aspects: openness, empathy, support, positive feelings, equality, and similarity. This study uses a qualitative approach with a descriptive study method. Data collection techniques were carried out through in-depth interviews with three informants from broken home families in Lembang Village, West Bandung Regency. The results of the study showed that interpersonal communication in broken home families tends not to run optimally. Most informants have difficulty in building open and equal communication. Even so, there are efforts from each individual to maintain relationships through aspects of empathy and emotional support, although in a limited form. In conclusion, interpersonal communication in broken home families is greatly influenced by emotional conditions, parenting patterns, and closeness between family members. Even though it is not completely harmonious, some aspects of communication can still be formed, especially if there is a willingness from the parties involved to understand and support each other.

Oil palm is a key commodity supporting Indonesia’s economy through exports and employment. The industry’s success depends heavily on the selection of superior seedlings, which determine productivity, crop quality, and resistance to pests and diseases. Manual selection, however, often leads to subjectivity and inconsistency due to limited human resources and genetic variation. To address this, the study applies the Naïve Bayes algorithm for classifying oil palm seedlings based on seven variables: height, stem diameter, number of leaves, leaf color, disease resistance, root growth, and fruit yield. Using an explanatory quantitative method, the study follows seven stages: identifying problems, literature review, collecting 1,000 data entries from PT Intitama Berlian Perkebunan, data pre-processing, system modeling (UML), algorithm implementation, and evaluation using a confusion matrix and black box testing. Data was split into 80% training and 20% testing. The Naïve Bayes-based classification achieved 95% accuracy and perfect recall (1.00) for the superior seedling class. However, its performance on the minority class (non-superior seedlings) was weaker due to dataset imbalance. Black box testing verified all system functions worked correctly, enabling effective and efficient use by administrators. The study concludes that Naïve Bayes improves objectivity, efficiency, and accuracy in seedling selection. Nonetheless, attention is needed on data balancing and optimization to maintain consistent performance across classes. This system shows strong potential as a decision-support tool in plantations and promotes digital transformation in agricultural processes.

Criminal liability is a form of imposing punishment on the perpetrator of a crime because of his/her unlawful actions. In other words, criminal liability determines whether a person will be acquitted or convicted of a crime that has been committed. This process involves the transfer of the existing punishment for the crime to the perpetrator. This research is a normative research. The results of the research show that Criminal liability for perpetrators who participate in the crime of murder, anyone who intentionally helps, plans, or plays an active role in murder can be held criminally responsible, either as the main perpetrator or as a participant (medeplichtige or medepleger). The sanctions imposed depend on the role and level of involvement of each perpetrator, in accordance with the principle of criminal responsibility and the element of error in criminal law. This must be reviewed carefully by the judge so that in making a decision on the defendant, it can have a deterrent effect on the perpetrator and not cause unrest for the victim's family regarding the decision handed down by the judge. The judge's decision to impose a light sentence on the perpetrator who participated in a crime that resulted in the death of a person shows an imbalance in law enforcement. This injustice can be caused by the judge's non-objective considerations, weak evidence, or lack of bias towards justice for the victim. This kind of decision risks weakening the deterrent effect and public trust in the justice system.