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The 2025 UINSU Community Service Program research in Sempajaya Village was carried out through training in making ecoprint tote bags as an effort to develop creativity while strengthening the economic literacy of elementary school children. This activity utilized natural materials such as leaves and flowers commonly found in the village environment, so that children could learn to recognize the potential of their natural surroundings as a source of creative works with practical and economic value. The training was conducted in three main stages: preparation, implementation, and reflection. During the implementation stage, children were introduced to the concept of ecoprinting, pounding techniques, and the steps involved in making ecoprinted tote bags through lectures, question and answer sessions, and hands-on practice. The results of the activity showed an increase in the participants' creativity, as evident in their ability to arrange motifs, select natural materials, and produce tote bags with attractive patterns. In addition, this activity also fostered a basic understanding of the economic value of creative products, thereby supporting the formation of independence and an entrepreneurial spirit from an early age. The reflection stage showed that the children were able to identify the process, benefits, and challenges they faced. Thus, ecoprint tote bags proved to be an effective educational tool in integrating creativity, economic literacy, and love for the environment for the children of Sempajaya Village.

This parenting seminar was designed to increase parents' capacity to understand and implement integrative holistic parenting, encompassing education, health, nutrition, and psychosocial aspects of children. An integrative holistic approach is considered important because child growth and development are influenced not only by formal education but also by health conditions, adequate nutrition, and emotional and social support from the family environment. This activity aims to provide comprehensive education to parents so they can carry out their parenting role optimally and sustainably. The activity was implemented using an interactive seminar method involving the delivery of material by competent speakers in the fields of childcare, education, and health, accompanied by discussions and a question-and-answer session that encouraged active participant participation. The material presented focused on understanding the concept of integrative holistic parenting, strategies for implementing positive parenting patterns, and the role of parents in supporting children's physical, emotional, social, and cognitive development. The results of the activity showed a significant increase in parents' knowledge, understanding, and attitudes regarding the importance of comprehensive and integrated parenting. Parents became more aware of their role as key figures in shaping character and optimizing children's potential from an early age. This seminar made a real contribution in equipping parents to be able to create a healthy, safe, and supportive parenting environment. This activity is expected to become a model for developing parental capacity in a sustainable manner with an integrative holistic approach as the key to successful child growth and development.

The use of Large Language Models (LLMs) in scientific research is becoming increasingly widespread, but presents epistemic risks that are not yet fully understood. This article discusses how the probabilistic mechanisms of LLM can produce outputs that appear correct and justified but are actually dependent on epistemic luck, thus resembling the Gettier case pattern. Through a conceptual study approach, this research clarifies concepts, analytically reconstructs the generative structure of LLM, and conducts a normative analysis of its implications for scientific accountability and authorship. The results of the analysis show that Algorithmic Gettier Cases (AGCs) occur when linguistic coherence deceives users and creates the impression of justification, even though the truth that emerges is statistical coincidence and is not supported by valid causal relationships. This condition poses a serious challenge to the attribution of knowledge and author responsibility in the production of academic texts. To address this issue, this article proposes the principle of Hyper-Justification Obligation, which is the ethical obligation for researchers to actively verify and causally reason every AI output before using it in scientific works. This research provides a theoretical contribution to understanding the epistemic risks of LLM and offers an ethical foundation for academic practice in the era of generative AI.

On the effectiveness of using social media as a marketing channel for MSMEs in Balikpapan. MSMEs play a major role in the national and regional economy, but developments in digital technology have caused marketing patterns to change drastically from traditional methods to online media. Social media such as Instagram, Facebook, TikTok, and X are now effective marketing tools because they are able to reach a wide audience at a low cost. However, this effectiveness is highly dependent on content quality, consistent interaction, and the right marketing strategy. Although social media offers many opportunities, MSMEs in Balikpapan still face various obstacles, such as limited human resources with an understanding of digital content, a lack of ability to read marketing metrics, and limited advertising budgets. In addition, research on the effectiveness of social media on MSMEs, especially in Balikpapan, is still minimal. Therefore, this study was conducted to analyze the extent to which MSMEs utilize social media, assess its effectiveness through indicators such as reach, interaction, and sales, and identify supporting and inhibiting factors. The results of this study are expected to provide strategic recommendations for MSMEs and theoretical contributions to the development of digital marketing models.

The rapid expansion of digital technology has intensified the circulation of pornographic content, making it increasingly accessible across various online platforms and posing significant risks to social, psychological, and moral stability. This study aims to examine the underlying factors contributing to the spread of pornographic content, its impact on individuals and society, and the urgency of strengthening regulatory and preventive measures. Using a qualitative approach supported by interviews and questionnaires distributed to university students in Jatinangor and Bandung, the research explores public perceptions, access patterns, and social responses regarding pornography in digital spaces. The findings reveal that most respondents consider pornography dissemination a deviant behavior, with economic motives and revenge-driven actions emerging as predominant driving factors. The study also shows that existing legal regulations are perceived as insufficient in mitigating the rapid growth of pornographic content online. These results highlight the importance of enhancing digital literacy, strengthening law enforcement, and promoting moral education to reduce the risks associated with pornography exposure. The study contributes to a deeper criminological understanding of deviant behavior in digital environments and underscores the need for collaborative societal efforts to address this phenomenon.

This study is motivated by the increasing dynamics of criminal activity within the jurisdiction of the East Nusa Tenggara Regional Police (Polda NTT), characterized by regional variations and complex causal factors. Conventional crimes such as assault, theft, and mob violence dominate the crime landscape and significantly affect social stability. The purpose of this research is to analyze the patterns, causes, and crime control strategies implemented by Polda NTT in maintaining public security and order. The study employs an empirical legal approach using a mixed-methods design, combining quantitative analysis of crime statistics with qualitative interviews involving police officers. Data were obtained from the Directorate of General Criminal Investigation (Ditreskrimum) of Polda NTT and cover all police jurisdictions, including one city police department and twenty-one district police offices. Findings reveal that crime rates in NTT are strongly influenced by social, economic, cultural, and geographical factors. The most prevalent crimes include assault, ordinary theft, traffic accidents, and mob violence. Major contributing factors consist of a local culture of violence, alcohol consumption, economic hardship, and low legal literacy. Polda NTT’s strategies involve preventive measures (routine patrols and public legal education), repressive actions (law enforcement and offender guidance), and humanistic approaches such as the Jumat Curhat program, which facilitates direct dialogue with the community.

One of the various salted egg products with smoking methods is smoked salted eggs which have a distinctive aroma and taste. This study aims to determine the effect of smoking and storage time  and its  interaction on  smoked salted  eggs  with on  physical  and  chemical  properties, total microbes as well. This research is experimental by using ducks eggs and a mixture of coconut shells and fibers, and rice husks as smoke fuel. The experimental design used is Randomized Complete Block Design (RCBD) with a 3x5 factorial pattern. As the first factor (P) is the smoking time which consists of P0= 0 hours, P1= 12 hours, P2= 15 hours while the second factor (H) is the storage time which consists of H0= 0 days, H1=7 days, H2= 14 days, H3= 21 days, and H4= 28 days. The variables observed were egg weight, albumen and yolk pH, moisture content, protein, and total microbial colony of smoked salted eggs. The results of the study were that the smoking time of 15 hours resulted in the lowest weight of smoked salted eggs (56.13g), while the storage time decreased the pH of albumen. Smoked salted duck eggs have a moisture content of 58.435-67.149%. The length of smoking increases the protein level. Salted duck eggs with a smoking time of 15 hours have the highest protein content, which is 15.39%. however, the duration of smoking and storage did not affect the total microbes of smoked salted eggs and there was no interaction between the duration of smoking and the duration of storage on the physical, chemical and total microbial properties.

This study aims to design and test a Dual Axis Solar Tracker to improve the energy absorption efficiency of solar panels on ships. The system is designed with a two-axis movement mechanism (horizontal and vertical) using a linear actuator motor controlled by Arduino Nano and ESP32. Testing was conducted on a 20 WP solar panel in Surabaya for 30 days, divided into three methods: 10 days using an LDR sensor, 10 days using an RTC, and 10 days in static conditions without a sensor. Voltage, current, and power data were measured every 30 minutes at 07.00–17.00 WIB. The test results show that the RTC method provides the highest and most stable output power, according to the sun's movement patterns in tropical areas, while the LDR method responds quickly to changes in light intensity but is less stable in changing weather. Static installation produces the lowest power. This system is able to maintain the panel orientation perpendicular to the sun's rays, thus increasing energy efficiency compared to static systems. These findings prove that dual-axis solar tracker technology, especially with an RTC sensor, is effective in dynamic maritime environments and can be a practical solution for optimizing renewable energy on ships. The most effective results using RTC sensors demonstrated the most stable and high power output, especially since the sun in tropical areas like Surabaya moves fairly consistently following a cyclical pattern. The success of this system not only increases the energy output of solar panels but also provides a practical solution for renewable energy applications in tropical climates.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

This literature review synthesizes a wide range of research findings that examine the role of auxin distribution direction in regulating leaf primordium patterning, known as phyllotaxis, in Arabidopsis thaliana. The reviewed studies consistently indicate that phyllotaxis represents a highly coordinated growth regulation mechanism that is primarily governed by Polar Auxin Transport (PAT), which is mediated by Pin-Formed (PIN) auxin efflux proteins. The polarity and spatial localization of PIN proteins generate dynamic auxin gradients within the shoot apical meristem, and these gradients function as key positional signals that determine the precise sites where new leaf primordia are initiated. Furthermore, auxin distribution is not regulated solely by PAT but is tightly integrated with genetic, cellular, and mechanical cues. Several studies highlight the role of transcription factors such as TMO5 in influencing PIN1 convergence and reinforcing auxin maxima at primordium initiation sites. In addition, the interaction between PIN polarity and the orientation of cortical microtubules suggests that mechanical stress and cytoskeletal organization contribute to the stability and directionality of auxin flow. Comparative analysis of the literature demonstrates that PAT and PIN proteins operate through interconnected mechanisms to control auxin distribution direction, which ultimately shapes the spatial arrangement and regularity of leaf primordium patterns. Overall, this review emphasizes the complexity and integration of hormonal, genetic, and mechanical signals in controlling phyllotaxis in plants.

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication

This study aims to evaluate and compare the performance of two neural network architectures under the Recurrent Neural Network (RNN) category, namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), in predicting earthquake magnitude in Indonesia. The dataset used consists of daily earthquake magnitude records from 2008 to 2023, preprocessed into time series format and normalized using the MinMax method. The training process was conducted using various combinations of batch size and epoch, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and relative prediction accuracy. The evaluation results show that LSTM with a batch size of 32 and 50 epochs provides the best prediction performance, achieving a MAE of 0.2227 and 93.65% accuracy. Meanwhile, GRU performed optimally at a batch size of 64 and 50 epochs, with a MAE of 0.2229 and 93.66% accuracy. The prediction visualization shows that LSTM offers greater stability and precision in tracking actual data patterns. These findings indicate that LSTM holds stronger potential for supporting earthquake prediction systems based on time series data.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

Drug abuse in Indonesia is a form of social deviance influenced by psychological pressure, structural conditions, and weak social ties. This phenomenon is not only related to legal aspects but also arises as a response to stress, anxiety, and negative emotional experiences. Theories such as self-medication, escapism, Durkheim and Merton's Anomie, and Agnew's General Strain Theory suggest that drug abuse behavior can develop due to imbalanced norms, social tension, and an individual's inability to cope with life's pressures. In inmates, these factors are exacerbated by challenging social and economic experiences prior to their incarceration, increasing the risk of involvement in drug use and trafficking. This study emphasizes the need for a multidimensional approach to understanding this deviance and supporting rehabilitation and social reintegration efforts. Strengthening the roles of families, communities, and educational institutions in preventing drug abuse is crucial for providing ongoing support. A deeper understanding of the patterns of individual involvement in drug abuse is key to designing more effective prevention programs, based on psychological, social, and structural approaches to reduce the long-term impact of drug abuse on society.

This study aimed to improve the speaking skills of sixth-grade students at SDN Negeri Kelapa Dua Wetan 01 Pagi in using the Simple Past Tense pattern (was/were) through the implementation of the Role Playing method. The research was conducted in two cycles involving 32 students as the subjects. The research method used was Classroom Action Research, consisting of the stages of planning, action implementation, observation, and reflection in each cycle. Data were collected through oral pre-tests and post-tests, as well as observations of students’ learning activities during the lessons. The results showed a significant improvement in students’ speaking skills. Their average score increased from 60.3 on the pre-test to 74.1 on the post-test. The percentage of students who achieved the Minimum Mastery Criteria also rose from 31% to 75%. The Role Playing method proved effective in enhancing students’ accuracy in using was/were, improving speaking fluency, and boosting self-confidence in oral communication. In conclusion, the application of the Role Playing method had a positive and significant impact on English learning outcomes among the sixth-grade students at SDN Negeri Kelapa Dua Wetan 01 Pagi. It is recommended that teachers continue to apply this method consistently, and that schools provide additional supporting facilities to optimize the learning process. For future studies, teachers and students are encouraged to expand the variety of role-play scenarios and include a control group to obtain a deeper and more comprehensive understanding of the effectiveness of the Role Playing method.