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Financial literacy plays an important role in supporting the ability of Micro, Small, and Medium Enterprises to adapt to the development of digital payments in Indonesia. The increasing use of QRIS, e-wallets, mobile banking, and technology-based payment services requires MSME actors to understand the benefits, risks, and proper use of digital financial services. This study aims to examine the role of financial literacy in the use of digital payments among MSMEs in Indonesia, including supporting factors, obstacles, and implications for strengthening MSME digitalization. This research uses a descriptive qualitative approach with a systematic literature study method. The data were obtained from 13 scientific articles indexed in nationally accredited journals at least Sinta 4, published between 2021 and 2026, and supported by official documents from relevant authorities. The findings show that financial literacy plays a role in increasing the readiness, trust, and decisions of MSME actors to use digital payments. However, low financial literacy, perceived risk, limited infrastructure, and conventional transaction habits remain obstacles to digital payment adoption. The implications of this study indicate the need for inclusive digital financial education, continuous assistance, and collaboration among the government, regulators, financial institutions, and fintech providers in strengthening the digital transformation of MSMEs.

This article analyzes the application of the hierarchy of norms in the decisions of the Constitutional Court concerning Law Number 17 of 2023 on Health using the perspective of Hans Kelsen’s Stufenbau theory. The enactment of the Health Law has generated significant legal debate and several constitutional review petitions submitted to the Constitutional Court, raising questions about the consistency between statutory norms and constitutional principles. This study aims to examine how the Constitutional Court applies the principle of hierarchical norms in reviewing the constitutionality of the Health Law and to assess the relevance of Stufenbau theory in explaining the interpretation of legal norms in constitutional adjudication. This research employs a normative juridical method using statute, conceptual, and case approaches. Primary legal materials consist of the 1945 Constitution, Law Number 17 of 2023 on Health, and relevant Constitutional Court decisions, supported by secondary legal materials from academic literature and legal studies. The findings show that the Constitutional Court consistently positions the 1945 Constitution as the highest norm within the Indonesian legal system and uses the hierarchy of norms as the basis for evaluating the validity of statutory provisions. The Court maintains the legal force of the Health Law when no normative conflict with the Constitution is identified, while in certain cases it provides constitutional interpretation to ensure the compatibility of statutory norms with constitutional principles. This study demonstrates that Stufenbau theory remains relevant as an analytical framework for understanding the hierarchical structure of legal norms and the operation of constitutional review within the Indonesian legal system.

This study examines the integration of technology in the process of Human Resource (HR) transformation through the perspective of employee data analytics as a strategic approach to modern HR management. The primary focus of the study is to analyze the impact of the simultaneous integration of digital HR systems and organizational digital transformation on improving the efficiency of HR functions, with organizational agility positioned as a moderating variable that strengthens this relationship. In addition, the study explores the potential optimization of Artificial Intelligence (AI) technologies and predictive analytics methods, such as Bayesian Optimization, in predicting workforce dynamics, including employee attrition risk and competency development needs, while also bridging the analytical skills gap among HR practitioners. The research method employed is a systematic literature review of relevant scientific publications from 2021 to 2025, selecting sources that address digital HR transformation, HR analytics, and the application of AI in organizational contexts. The findings indicate that digital HR systems have a strong and significant effect on enhancing operational efficiency and the quality of HR decision-making, and this effect becomes more optimal when supported by a high level of organizational agility. Furthermore, AI and predictive analytics are proven to generate more accurate predictions and simplify technical complexity, making them easier for HR practitioners to adopt. This study concludes that the success of HR transformation requires a holistic approach that aligns the use of advanced technologies with organizational capabilities, human resource readiness, and ethical considerations to create sustainable organizational value.

 Road infrastructure development is an important factor in supporting community mobility and equitable regional development. However, road construction data management in Palembang City still faces various problems, such as scattered data, lack of integration, and not yet presented in a map-based visual form. This study aims to implement a Geographic Information System (GIS) as a medium for location mapping and presenting road construction data information in Sematang Borang and Kalidoni Districts, Palembang City. The methods used include field observation, road construction data collection and verification, GPS coordinate point retrieval, spatial and non-spatial data processing, and the development of a GIS system based on interactive digital maps. The results of the study indicate that the system is able to present road construction information in a structured, accurate, and easily accessible manner. This GIS helps improve the efficiency of the monitoring process, data management, and preparation of road construction reports at the Palembang City Public Works and Housing Agency. Thus, the application of GIS can be a supporting solution in decision-making and encourage the digitalization of road infrastructure data management.

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.

Hospitals often face difficulties in determining patient treatment priorities due to limited medical resources and the uncertainty of patient conditions. Conventional prioritization methods tend to rely on subjective judgment, which can lead to inconsistent decisions and delays in treatment. This study aims to apply fuzzy logic in a decision support system to determine patient priority levels more objectively and systematically. The proposed method utilizes a fuzzy inference system that processes several criteria, including the severity of symptoms, vital signs, patient age, and waiting time. These criteria are represented as fuzzy sets and evaluated using a set of inference rules to generate priority classifications. The results indicate that the fuzzy logic–based system is able to classify patient priorities more consistently and transparently compared to manual assessment. The system provides clear priority categories that can support medical staff in making faster and more accurate decisions. The findings imply that the implementation of fuzzy logic in hospital decision support systems can improve the quality of healthcare services, enhance fairness in patient handling, and optimize the allocation of medical resources, particularly in emergency and high-demand situations.

The attendance process at SMK Swasta Dwitunggal 2 Tanjung Morawa is still carried out manually, potentially causing inefficiency, delays, and inaccuracies in attendance data. This condition results in the difficulty of managing attendance data and less than optimal support for managerial decision-making in the school environment. This study aims to design an Enterprise Architecture (EA) for a school attendance system using the TOGAF Architecture Development Method (ADM) framework. The research method used is a qualitative approach with a case study design. The analysis is carried out through mapping existing conditions (AS-IS) and designing expected conditions (TO-BE) in several TOGAF ADM phases, including business architecture, information system architecture, technology architecture, as well as the Opportunities and Solutions and Migration Planning phases. The results of the study are an enterprise architecture blueprint and a migration roadmap that can serve as guidelines for the gradual implementation of a technology based attendance system. This design is expected to improve the efficiency of attendance management, data accuracy, and support the decision-making process in schools.

This research aimed to analyze the role of the secretariat in maximizing office administrative service funtions within the secretariat division of the departement of trade, industry, and cooperatives and SMEs of sikka regency. This research used a qualitative approach with data colletion techniques including interviews, observations, and document studies. The key informant in this research was the head of the secretariat division. Data analysis was conducted interactively through the stages of data colletion, data reduction, data presentation, and conclusion drawing. The result showed that the secretriat has carried out office administrative functions systematically and in a structured manner, covering routine, technical, analytical, interpersonal, and managerial functions. The management of correspondence and archives has been implemented following the established procedures, although it remained largely manual and faced challenges such as delays in letter numbering and limited supporting facilities. The utilization of information technology through the srikandi application has proven to improve work efficiency, however, it still requires enhanced human resource competencies and stronger technological infrastructure support. In terms of analysis and reporting, the secretariat played an important role in preparing administrativereports and providing accurate data to support managerial decision-making. Furthermore, the secretariat also held a strategic role in fostering coordination, communication, and harmonious working relationships within the organization.

The lompong flower is an ornamental plant that requires stable soil moisture conditions and a stable environment so that it can grow optimally. Moisture level mismatches often hinder growth and even have the potential to cause plant damage. This research aims to design and implement an Internet of Things (IoT)-based humidity monitoring system that is able to monitor the humidity condition of the pond flower in real-time. The system developed uses soil moisture sensors as input devices, microcontrollers as data processors, and internet networks as a medium for sending data to the monitoring platform. The data obtained is displayed directly so that users can know the actual humidity conditions and take appropriate maintenance actions. The results show that the system is able to display moisture data with a good level of accuracy, as well as provide relevant information for users in plant care decision-making. The implementation of this system has proven to be effective in supporting the maintenance of lompong flowers, especially in maintaining soil moisture stability. This research is expected to be a reference for the development of IoT-based ornamental plant monitoring technology, as well as contribute to improving the quality of plant care in a more efficient and measurable manner.

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.

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.

This research aims to design and develop a decision support system to determine the recipients of assistance in the Family Hope Program (PKH) in Pogo Tena Village. This system uses the (Technique for Order Preference by Similarity to Ideal Solution) method which aims to assist decision-makers in selecting families who meet the criteria to receive social assistance based on several predetermined factors, such as income level, number of family dependents, health conditions, and education. The method used in this study is a qualitative and quantitative method with a case study approach on PKH Pogo Tena Village. Data was obtained through interviews with related parties, field observations, and data collection from existing documents. In this system, the assessment is carried out by comparing alternative performance values based on pre-established criteria, and then using the TOPSIS method to determine the families who are eligible for assistance. The results of this study show that the designed decision support system can provide more objective and transparent recommendations for aid recipients. Using the TOPSIS method, the system can prioritize beneficiaries based on their proximity to the ideal solution, which helps minimize subjective errors in the beneficiary selection process. This research is expected to contribute to increasing the effectiveness and efficiency in the implementation of the Family Hope Program in Pogo Tena Village, as well as as a reference for other agencies that want to apply similar methods in social assistance programs.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.

This study examines the effectiveness of problem-solving skills in enhancing decision-making processes among teachers at State Junior High Schools in Jambi Province. The research was motivated by the increasing demands placed on educators to make timely, accurate, and contextually appropriate decisions in academic, administrative, and student-related matters. A quantitative approach was employed using a survey method, involving teachers from several public junior high schools across the province. Data were collected through validated questionnaires measuring levels of problem-solving competence and decision-making quality. The results indicate a significant positive relationship between problem-solving skills and decision-making effectiveness. Teachers with higher levels of analytical thinking, alternative evaluation, and solution implementation were found to make decisions more systematically, responsively, and with greater accuracy. Moreover, the findings reveal that problem-solving skills contribute not only to improving daily pedagogical decisions but also to enhancing school governance and conflict resolution. This research highlights the importance of continuous professional development programs aimed at strengthening teachers’ cognitive and strategic abilities. The study concludes that integrating structured problem-solving training into teacher development initiatives can substantially improve decision-making quality in junior high schools, ultimately supporting better educational outcomes in Jambi Province.

Makan Bergizi Gratis (MBG) program is a national policy designed to improve the nutritional quality of children and support human resource development. However, its implementation at the regional level still faces various institutional and coordination issues. This study aims to analyze the implementation of the Free Nutritious Meals program in Jambi City, focusing on the dynamics of program implementation and the inhibiting factors. This study uses a qualitative approach with a case study method. Data were collected through in-depth interviews with policy implementers and  documentation with informants determined through purposive sampling. Data analysis was conducted using Merilee S. Grindlee policy implementation theory through data reduction, presentation, and conclusion drawing. The results of the study indicate that the implementation of the MBG program in Jambi City has not been running optimally. This condition is characterized by the strong dominance of the central government in the decision making process, weak coordination between actors at the regional level, and inadequate readiness of supporting institutions, especially in aspects of human resources, monitoring system, and clarity of operational standards for implementation.

The rapid growth of e-commerce in Indonesia has encouraged businesses to adopt digital marketing strategies supported by effective information management. Marketplace platforms such as Shopee provide extensive marketing data with strategic value; however, its utilization requires a structured system. This study aims to analyze the implementation of Marketing Information Systems in supporting digital marketing strategies on the Shopee e-commerce platform. The research employs a qualitative descriptive approach through literature review and indirect observation using internet-based scholarly sources. Data were collected from relevant national and international journals and analyzed descriptively to identify the roles, components, and influencing factors of Marketing Information Systems. The results indicate that Marketing Information Systems play a significant role in supporting digital marketing decision-making through the management of internal records, marketing intelligence, marketing research, and decision support systems. Furthermore, the implementation of Marketing Information Systems on Shopee is influenced by information technology development, company size and scale, consumer behavior, competitive environment, and corporate strategy and objectives. This study is expected to contribute theoretically to the development of digital marketing studies and provide practical references for e-commerce businesses in optimizing data-driven marketing strategies.

The economic valuation of natural resources (NR) is an important instrument in supporting evidence-based decision-making, particularly in sustainable environmental management. Cost–Benefit Analysis (CBA) serves as a primary approach to assess the economic feasibility of programs or policies by integrating all benefits and costs, including non-market values. This article presents a systematic literature review of studies employing CBA for NR valuation during the period 2010–2024, based on searches in Scopus, Web of Science, ScienceDirect, SpringerLink, Taylor & Francis, and Google Scholar, using stringent selection criteria. The review findings indicate that CBA has been widely applied in forest management, biodiversity conservation, land rehabilitation, water and air pollution control, and ecotourism development, providing a quantitative depiction of economic feasibility through indicators such as Net Present Value (NPV), Benefit–Cost Ratio (BCR), and Internal Rate of Return (IRR). Key challenges were identified in non-market valuation, long-term uncertainty, data limitations, and sensitivity to discount rate assumptions. These findings underscore the importance of integrating environmental valuation methods, conducting comprehensive sensitivity analyses, and adopting multidisciplinary approaches to strengthen the application of CBA in sustainable NR management, while also offering strategic recommendations and directions for future research for policymakers and environmental economics scholars.

The process of determining scholarship eligibility often faces challenges related to subjectivity and uncertainty in assessment criteria, which can result in inaccurate and unfair decisions. Scholarship selection generally involves multiple criteria, such as academic achievement, family economic conditions, and supporting factors that are difficult to evaluate using conventional decision-making methods. Therefore, an appropriate decision support approach is required to handle such uncertainty effectively. This study aims to implement the Fuzzy Mamdani method in a decision support system to determine scholarship eligibility more objectively and accurately. The research method consists of data collection, fuzzification of input variables, formulation of fuzzy rules, inference using the Mamdani approach, and defuzzification using the centroid method to obtain a crisp eligibility value. The results show that the Fuzzy Mamdani method is capable of producing flexible eligibility scores by considering the degree of membership of each criterion. The generated output reflects real conditions more comprehensively compared to traditional methods. The implementation of this method can assist decision-makers in improving transparency, consistency, and fairness in scholarship selection. This research is expected to contribute to the development of intelligent decision support systems in the field of educational assessment.

The process of determining eligibility for social assistance recipients is often constrained by subjective assessments and uncertainty in decision-making criteria. This condition can lead to inaccurate targeting and unfair distribution of aid. Therefore, an appropriate decision support method is required to handle data uncertainty effectively. This study aims to apply the Fuzzy Mamdani method to determine the eligibility of social assistance recipients based on several assessment criteria. The criteria used in this study include monthly income, number of dependents, and housing conditions. The research method consists of data collection, fuzzification, formulation of fuzzy rules, inference using the Mamdani approach, and defuzzification to obtain a crisp output value. The results show that the Fuzzy Mamdani method is able to classify recipients into eligible and non-eligible categories more flexibly compared to conventional methods. The generated eligibility values reflect real conditions more accurately by considering degrees of membership for each criterion. The implementation of this method can assist decision-makers in improving the accuracy, objectivity, and fairness of social assistance distribution. This research is expected to contribute to the development of intelligent decision support systems in the social welfare sector.