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The rapid development of financial technology (fintech) and crypto assets has created a paradox in the global financial system: while promoting inclusion and efficiency, it also opens new vulnerabilities for money laundering offenses. Notaries, as public officials and legal professionals involved in various financial and business transactions, are at the forefront of preventing this misuse. This article analyzes the specific risks of money laundering through fintech and crypto assets and formulates the due diligence framework required by notaries in carrying out their preventive functions. The research method uses a normative juridical approach with doctrinal analysis of regulations and international standards, enriched with case studies and best practice references. The findings indicate that notaries face three main challenges: (1) difficulty in identifying parties in virtual transactions, (2) the volatility and relative anonymity of crypto assets, and (3) the gap between the speed of technological innovation and regulatory adaptation. This article proposes an Enhanced Digital Due Diligence (ED3) model that integrates digital verification technology, blockchain forensic analysis, and a dynamic risk-based approach. This framework is expected to strengthen the role of notaries as gatekeepers in the rapidly evolving digital financial ecosystem while maintaining the profession's relevance in the era of digital transformation.

Arrhythmias are generally classified into two groups: disorders of impulse formation and disorders of impulse conduction. Cardiac arrhythmias can also be categorized into three main types: supraventricular arrhythmias, ventricular arrhythmias, and bradyarrhythmias. One of the most commonly encountered arrhythmias in clinical practice is Atrial fibrillation. This study employed a case study method using a nursing process approach. The subjects consisted of two patients: Mrs. S, a 74-year-old female, and Mr. R, a 44-year-old male. The nursing intervention administered was antiarrhythmic therapy using intravenous digoxin. Evaluation was conducted by monitoring the patients’ heart rate and cardiac rhythm using an electrocardiogram (ECG) monitor. The results of the case studies in both patients showed that digoxin therapy produced a positive therapeutic response, as evidenced by a gradual reduction in heart rate. However, ECG monitoring revealed no significant changes in cardiac rhythm. These findings suggest that digoxin is effective for rate control, although it does not restore sinus rhythm.

Malpractice in dental health services has become a complex legal issue as public awareness of service quality and patient rights continues to increase. The enactment of Law Number 17 of 2023 on Health introduces important changes in regulating the legal accountability of medical personnel, including dentists, particularly in relation to criminal liability for malpractice. This study analyzes the forms of dental malpractice and the criminal responsibility of dentists under the new Health Law. The research uses an empirical legal method with sociological and statutory approaches. Data were obtained through interviews and a review of relevant legislation, legal doctrines, and court decisions. The results show that dental malpractice can arise from negligence, failure to comply with professional standards, violations of standard operating procedures, and the absence or disregard of informed consent. Law Number 17 of 2023 stipulates that criminal liability for dentists can only be applied after a recommendation from the Professional Discipline Board, ensuring a clear distinction between inherent medical risks and professional misconduct. This framework is designed to provide balanced legal protection for both patients and dentists while strengthening legal certainty in malpractice case resolution. Consequently, dentists must understand professional standards and criminal liability mechanisms to practice responsibly and professionally.

Infant nutritional status is an important indicator of child health that is influenced by various factors, including maternal characteristics. Maternal education is often considered to play a role in determining infant care and feeding practices. This study aims to analyze the relationship between the mother's education level and infant nutritional status. The study uses a quantitative approach with a cross-sectional analytical observational design. The study sample consisted of 30 mothers and infants aged 0–12 months selected using purposive sampling. Maternal education level was categorized as elementary, secondary, and higher education, while infant nutritional status was determined based on anthropometric indicators according to WHO standards and the position of the Health Card (KMS). Data analysis was performed bivariately using Spearman's rho correlation test with a significance level of p < 0.05. The results of the study indicate that there is no significant relationship between the mother's level of education and the nutritional status of the infant (p = 0.575; r = −0.098). The correlation coefficient value indicates a very weak and statistically insignificant relationship. The conclusion of this study indicates that the mother's education level is not a factor directly related to the nutritional status of infants. The nutritional status of infants is likely to be more influenced by other more proximal factors, such as feeding practices, the quality and quantity of food intake, the child's health condition, and the family's environmental and socioeconomic factors.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

The assessment of students’ academic performance in higher education is generally still dominated by conventional numerical approaches, which are less capable of representing qualitative and subjective variables such as classroom activeness and student participation. These approaches often result in evaluations that are not holistic and do not fully reflect students’ overall academic achievements. Therefore, this study aims to analyze the concept of fuzzy logic as a support tool for assessing students’ academic performance in higher education, with a case study of students at Universitas Asahan. The research method employs a descriptive qualitative and quantitative approach by applying Mamdani fuzzy logic. The input variables consist of exam scores, assignment scores, and classroom activeness, while the system output is the category of academic performance, namely sufficient, good, and very good. The sample data consist of ten active undergraduate students from Universitas Asahan. The data processing stages include fuzzification, the construction of fuzzy rules (rule base), fuzzy inference, and defuzzification using the centroid method. The results indicate that fuzzy logic is able to integrate quantitative and qualitative variables and accommodate uncertainty in academic assessment. The resulting evaluation is more proportional and realistic compared to conventional assessment methods based solely on average scores. Therefore, fuzzy logic can be considered an effective and flexible alternative approach to support student academic performance assessment systems in higher education.

The determination of scholarship recipients is a very important process in supporting students’ educational success, particularly in providing fair opportunities for high-achieving students who require financial assistance. However, in practice, this process often faces various challenges, such as assessor subjectivity and uncertainty in evaluating the applied criteria. Therefore, a decision support system is needed to assist decision-making in an objective and measurable manner. This study aims to implement the Fuzzy Tsukamoto method as a decision support system for determining scholarship eligibility. The criteria used in this study include Grade Point Average (GPA) as an indicator of academic achievement and parents’ income as an indicator of students’ economic conditions. The Fuzzy Tsukamoto method was selected because it is capable of producing crisp output values based on predefined fuzzy rules. Student data were processed through several stages, namely fuzzification to transform input data into fuzzy values, inference using the minimum operator, and defuzzification using the weighted average method. The results of the study indicate that the application of the Fuzzy Tsukamoto method is able to generate more objective, consistent, and measurable decisions. Based on the calculation results, a scholarship eligibility score of 63.9 was obtained, which falls into the eligible category. Thus, the Fuzzy Tsukamoto method can be considered an effective alternative to support fair, systematic, and transparent decision-making in determining scholarship recipients.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

The Human Resource Department (HRD) plays a very important role in supporting the growth and sustainability of an organization or company. Employees represent the most valuable asset owned by the company, which means that HRD can be considered the most critical component that must be managed properly. The primary function of HRD encompasses the management of all aspects related to the workforce, including ensuring that every employee fully receives their rights. To enhance work effectiveness and efficiency, HRD requires support in the form of a computer-based technology system. Therefore, it is necessary to develop an application capable of accelerating and simplifying employee data management. This study aims to improve HRD performance through the creation of a Human Resource Information System (HRIS). The application development process adopts the Waterfall method. The final outcome of this research is an application that successfully facilitates the HRD department in carrying out its various tasks more effectively.

Savings and Loan Cooperatives (KSP) play a vital role in expanding community access to capital, especially within the informal sector. Nevertheless, non-performing loans remain a persistent challenge that can threaten liquidity and long-term institutional sustainability. KSP CU Mera Ndi Ate faces similar issues, which are assumed to stem not only from administrative weaknesses but also from members’ perceptions and behavioral factors. This research aims to examine the potential causes of non-performing loans through text-based sentiment analysis using an unsupervised learning approach. A quantitative method with a data mining framework was applied. Data were gathered through interviews, observations, documentation, and 200 customer opinion texts processed using the Orange Data Mining application. The analytical stages included preprocessing, corpus development, feature extraction, sentiment clustering, and visualization. Because the dataset lacked predefined labels, unsupervised learning was used to identify naturally emerging sentiment patterns. Findings reveal a predominance of critical sentiments related to credit assessment procedures and service quality. The highest sentiment score (75) concerned insufficient creditworthiness evaluation, followed by concerns about service efficiency (66.6667). These insights suggest that improving assessment accuracy and service quality may help reduce non-performing loans.

Expressive feelings therapy is a form of mental health nursing modality therapy that aims to help individuals express their thoughts and emotions openly without violating the rights of others. This study aims to explore the effectiveness of cognitive therapy focused on “expressing feelings” in improving emotional expression abilities among female patients in the recovery phase at the Anggrek Ward of the Aceh Government Mental Hospital. This descriptive qualitative study was conducted through group therapy activities with structured stages, using media such as emotion expression picture cards and a spin-the-bottle game as interaction stimuli. The population consisted of 18 patient in the Anggrek ward, with a sample of 12 patients who were cooperative, psychologically stable, able to communicate bidirectionally, and willing to participate in group therapy. The results showed that participants experienced an improvement in their ability to express emotions such as happiness, sadness, anger, and anxiety more openly, accompanied by increased social interaction and reduced emotional tension. The therapy also encouraged greater self-confidence and a sense of emotional relief. These findings support that expressive feelings therapy can be incorporated as part of routine nursing interventions for patients who experience difficulties in emotional regulation. It is recommended that further research be conducted over a longer duration and with a broader scope to strengthen the validity of the findings.

This community service was carried out at the Sebesi Island Coconut Brown Sugar Artisans Group, South Lampung Regency, which faced the problem of low product competitiveness due to the use of conventional plastic packaging that was simple, unattractive, and environmentally unfriendly. This topic was chosen because it is relevant to the issue of the plastic waste crisis and the urgency of implementing a circular economy in island regions that are vulnerable to environmental pollution. The objective of this activity was to improve the hard and soft skills of partners through the innovation of environmentally friendly packaging designs based on local materials, while also increasing the economic value of coconut brown sugar products. The community service was carried out using a participatory approach through socialization, training, co-design of packaging, and assistance with production and marketing for 40 partners consisting of brown sugar craftsmen, BUMDes administrators, and local natural material weavers. The results of the activity showed an increase in the partners' skills in product design and packaging, strengthening of soft skills in the form of confidence and marketing readiness, as well as an increase in the selling value and image of the product in the local market. This activity confirms that environmentally friendly packaging innovation based on local resources is an effective strategy in empowering MSMEs as well as implementing a circular economy at the village level.

Rapid global environmental changes, especially due to advances in technology and information, have had a significant impact on various sectors of life, including the health sector. In the context of modern health services, hospitals are no longer just places of healing, but also institutions that are required to provide high-quality services, based on patient safety, and are able to adapt to the dynamics of community needs. This change requires a transformation in human resource management, especially related to the interpersonal and psychosocial competencies of nurses as the spearhead of medical services.Purposes:​ to analyze the effect of effective communication and Assertive Attitude on Nurses' Work Productivity in hospitals, with Work Fatigue as a mediating variable.Methods: This type of research is quantitative, using statistical figures to measure the influence of independent variables on dependent variables. The research design is a cross-sectional study.Results: The results of this study indicate that effective communication, assertiveness, and job fatigue collectively significantly influence nurses' work productivity. Communication and assertiveness positively contribute to job fatigue, which in turn affects productivity. Job fatigue is also shown to be a significant mediator in the relationship between communication and assertiveness on work productivity .Conclusion : The importance of strengthening effective communication as a strategy to increase productivity. Organizations need to design communication systems that support openness, transparency, and clarity of information and culture-based assertiveness training, so that employees can express their opinions appropriately without causing resistance in the team.

This study aims to analyze the profit of the banana chips home industry "Sukses Abadi Snack" owned by Mrs. Suyamiati in Unaaha City. The respondent was purposively selected, focusing on the owner of the business. Data were analyzed using the profit analysis method to assess the financial performance of the business. The results indicated that the total cost of the business amounted to Rp. 5,864,025 per month. The breakdown of costs included fixed costs of Rp. 76,025 per month and variable costs of Rp. 5,788,000 per month. The total income generated by the banana chips business was Rp. 13,392,000 per month. The profit was determined by subtracting the total costs from the income. The net profit earned by the "Sukses Abadi Snack" home industry in producing banana chips reached Rp. 7,604,000 per month. This shows that the banana chips business is profitable and generates a substantial monthly profit for the owner.

Mawang Village occupies a strategic position as a buffer zone integrating the administrative center of Gowa Regency with other growth centers, featuring the ecological asset of Lake Mawang, which holds significant potential for ecotourism development. However, a discrepancy exists between spatial planning policies and the actual development of facilities and infrastructure on the ground. This study aims to identify the availability, accessibility, and physical feasibility of regional facilities and infrastructure as a fundamental database for area development. The method employed is descriptive-quantitative with a comparative analysis approach referring to the SNI 03-1733-2004 standards and spatial analysis. The findings indicate that religious and commercial/service facilities exhibit very high service levels with surplus conditions, while basic health facilities and early childhood education remain below minimum service standards. Spatially, the distribution of facilities tends to be concentrated along the main corridor, creating accessibility disparities for residents in inner residential areas. The collector road infrastructure is functionally adequate but lacks pedestrian facilities, while local roads suffer from pavement quality degradation. Furthermore, Lake Mawang, as a primary tourist attraction, still lacks basic supporting facilities. The implications of this research emphasize the need for equitable distribution of health facilities, revitalization of road quality, and the formulation of a comprehensive spatial detail plan for the lake area to achieve sustainable regional development based on local potential.

In demographic studies, population growth refers to changes in the number of inhabitants over a given period, measured by calculating numerical differences in population size and expressing them within specific time units to illustrate patterns of increase or decline. Based on data from Tembalang District in Figures 2025, the population growth rate in Kedungmundu Village rose by 0.51% between 2016 and 2025, with most residents relying on clean water services provided by PDAM Tirta Moedal as their primary source of drinking water. This study aims to estimate clean water demand based on projected population growth and to design a pipeline network system capable of meeting future needs. The analysis of water demand applies population projection methods, including arithmetic, geometric, and least square approaches, to compare their levels of accuracy, while the clean water distribution network is modeled using EPANET 2.0. One of the main challenges faced by PDAM is that service coverage has not yet been fully optimized. The ten-year projection results indicate that the arithmetic method provides a correlation value closest to 1, estimating a future population of 14,904 people with a total clean water requirement of 3.48 liters per second. To support this projected demand, the proposed network design utilizes High Density Polyethylene (HDPE) pipes with diameters of 12, 10, and 8 inches to ensure efficient and sustainable water distribution.

The development of bioinformatics has led to the availability of large amounts of genetic data through public databases such as NCBI Gene, OMIM, and Ensembl. However, the complexity of data presentation and the dominance of English language hinder students, novice researchers, and the general public in understanding the relationship between genes and disease. This research aims to develop a simple web-based information system to identify disease-causing genes with concise, Indonesian-language, and user-friendly information presentation. The method used is Research and Development (R&D), which includes literature study, needs analysis, system design, implementation, testing, and evaluation. The system was developed using a MySQL relational database with a web interface that displays basic gene information, chromosome location, biological function, and gene-disease relationships, complete with simple visualizations. Black Box testing results indicate that all main functions run according to user requirements. This system is expected to improve bioinformatics literacy and become an effective learning medium.

This research presents the development of a transaction data protection mechanism for a bouquet sales application by utilizing the Advanced Encryption Standard (AES) algorithm. The rapid growth of digital commerce has led to an increase in online transactions, which in turn raises serious concerns regarding the security of sensitive transaction data. Information such as customer identities, order details, delivery addresses, and payment data are vulnerable to unauthorized access, data leakage, and manipulation if not properly secured. To address these issues, this study applies the AES-128 encryption algorithm using a 128-bit secret key to secure transaction data before it is stored in the system database. The encryption process follows the standard AES workflow, including key expansion, initial transformation, multiple encryption rounds, and a final transformation stage. Decryption is restricted exclusively to authorized users who possess the correct encryption key. The research methodology includes system analysis, AES integration into the application, and functional testing of the encryption and decryption processes. Data integrity is validated by comparing the original plaintext with the decrypted output, while system performance is evaluated based on processing time and decryption accuracy. Experimental results indicate that the average encryption and decryption time remains under 10 milliseconds per transaction, without affecting system performance. The findings confirm that AES-128 effectively enhances transaction data confidentiality and integrity in the bouquet sales application

The development of information technology has significantly improved the efficiency of application-based delivery services, including Shopee Partner Driver couriers. However, several issues such as delays in information updates, system errors, and inaccurate delivery data still affect courier performance. This study aims to analyze the effect of Information Quality and System Quality on the Performance of Shopee Partner Driver Couriers in Medan City. This research used a quantitative method with a questionnaire survey distributed to 100 active couriers. The instrument applied a fivepoint Likert scale, and data were analyzed using validity and reliability tests, descriptive statistics, and multiple linear regression analysis with SPSS software. The results indicate that both Information Quality and System Quality have a positive and significant effect on Courier Performance. The coefficient of determination (R²) of 0.625 shows that these two variables explain 62.5% of courier performance variance, while the remaining 37.5% is influenced by other factors. These findings support the DeLone and McLean (2003) model of information system success, emphasizing the importance of system and information quality in enhancing user performance.