Expert Judgment Research Articles

The Implementation of Principal’s Academic Supervision in SD Negeri 1 Beti Jaya

Academic supervision is one of the main tasks and responsibilities of the principal. This study aims to describe the implementation of supervision of the principal of a public elementary school according to the perception of teachers. The study was conducted in Kayu Agung sub-district, Ogan Komering Ilir district, South Sumatra Province by distributing questionnaires to teachers. The research questionnaire used has been validated using the expert judgment technique. Data from the questionnaire were processed quantitatively using the percentage technique. The results of the study showed that the principal of SD Negeri 1 Beti Jaya studied had carried out academic supervision tasks. Of all the tasks, the task of motivating teachers to utilize information technology in learning in each subject is the task with the highest implementation. Conversely, the task with the lowest implementation is understanding the concepts, principles, theories/technologies, characteristics, and development trends of the learning/guidance process for each subject and is in the fourth indicator, namely planning supervision activities with teachers. Thus, the implementation of academic supervision of the principal at SD Negeri 1 Beti Jaya still needs to be improved.

Effects of extreme climate on agriculture in paddy field area in West Java Province (Indonesia) using multitemporal scenarios, GIS, and remote sensing

One of the factors of decreased productivity of rice fields is the agricultural hazards that occur in Indonesia due to climate and weather changes. One way to reduce the impact is by providing a need for information on areas affected by agricultural hazards so that it can be used for decision making in food security. This research conducts spatial modelling of agricultural hazards in rice fields integrates DEM, land cover, and precipitation data to determine flood and drought affected areas. This research calculates agriculture hazard using 4 different time scenarios: 5, 10, 25, and 50 years to obtain comprehensive agricultural hazards investigation and integrated based on two methods: expert judgment and frequency of occurrence in an area. The result shows, in the 2050 shows that the very high-class area from expert judgment and frequency of occurrence are 14131.23 and 11615.36 ha, respectively. This research is expected to identify areas that have agricultural hazards, especially in agricultural areas, and can be used for planning food security in the future.

Innovative Credit Risk Assessment: Leveraging Social Media Data for Inclusive Credit Scoring in Indonesia’s Fintech Sector

The financial technology domain has undertaken significant strides toward more inclusive credit scoring systems by integrating alternative data sources, prompting an exploration of how we can further simplify the process of efficiently assessing creditworthiness for the younger generation who lack traditional credit histories and collateral assets. This study introduces a novel approach leveraging social media analytics and advanced machine learning techniques to assess the creditworthiness of individuals without traditional credit histories and collateral assets. Conventional credit scoring methods tend to rely heavily on central bank credit information, especially traditional collateral assets such as property or savings accounts. We leverage demographics, personality, psycholinguistics, and social network data from LinkedIn profiles to develop predictive models for a comprehensive financial reliability assessment. Our credit scoring methods propose scoring models to produce continuous credit scores and classification models to categorize potential borrowers—particularly young individuals lacking traditional credit histories or collateral assets—as either good or bad credit risks based on expert judgment thresholds. This innovative approach questions conventional financial evaluation methods and enhances access to credit for marginalized communities. The research question addressed in this study is how to develop a credit scoring mechanism using social media data. This research contributes to the advancing fintech landscape by presenting a framework that has the potential to transform credit scoring practices to adapt to modern economic activities and digital footprints.

Building resilient urban drainage systems by integrated flood risk index for evidence-based planning.

Urban flooding poses a significant risk to cities worldwide, exacerbated by increasing urbanization and climate change. Effective flood risk management requires comprehensive assessments considering the complex interaction of social, economic, and environmental factors. This study developed an innovative Urban Flood Risk Index (FRI) to quantify and assess flood risk at the sub-catchment level, providing a tool for evidence-based planning and resilient infrastructure development. This study integrates Geographic Information System (GIS), Storm Water Management Model (SWMM), Analytic Hierarchy Process (AHP), and the Pressure-State-Response (PSR) framework. The FRI incorporates seven pressure and state indicators and three response indicators weighted by expert judgment. The FRI was calculated by combining the weighted sub-indices, classifying flood risk into five levels. Results showed that 51% of the study area experienced high pressure, with 26% facing very-high pressure. The state index indicated that 55% of the area falls under a moderate state, while 21% exhibits a high state. Importantly, the response index highlighted the effectiveness of Low Impact Development (LID) practices, with 20% of the area showing high to very-high response levels. The integrated FRI demonstrated an overall moderate flood risk level for maximum sub-catchments, emphasizing the positive impact of LID practices in mitigating flood risk despite existing pressures and system limitations. This evidence-based assessment provides a valuable tool for sub-catchment level flood risk assessment. It empowers decision-makers to prioritize investments, target interventions, and develop adaptive strategies to enhance urban resilience in a changing climate.

Psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic.

Evidence on the psychometric properties of satisfaction scales in telerehabilitation is limited, especially in specific populations such as caregivers of children. To determine the psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic. A total of 155 caregivers were evaluated between June and December 2020. Approximately 91% (141) were women. Evidence of content validity for the scale was obtained through evaluation by three expert judges, who confirmed the adaptation of the University of Washington Telemedicine Patient Satisfaction Survey, in which the word "telemedicine" was replaced with "telerehabilitation" and "physician" with "physical therapist." For the confirmatory factor analysis, two models were tested. The first one-factor model with nine items did not fit satisfactorily based on the goodness-of-fit indices (χ 2/df = 13.96, comparative fit index [CFI] = 0.963, non-normed fit index [NNFI] = 0.951, root mean square error of approximation [RMSEA] = 0.290 [0.265, 0.316], and standardized root mean square residual [SRMR] = 0.178). In contrast, the second one-factor model, which involved respecification of Items 6 and 7, was considered acceptable (χ 2/df = 1.60, CFI = 0.998, NNFI = 0.998, RMSEA = 0.062 [0.021, 0.096], and SRMR = 0.057). Reliability was acceptable, with a value of 0.888. Additionally, network analysis confirmed the direct relationship between the items, with Item 7 showing the greatest strength centrality. The instrument demonstrated sufficient evidence of validity and reliability in the Peruvian context, supporting its use with pediatric patients.

Large language models: Tools for new environmental decision-making.

This study represents the first exploration of Large Language Models (LLMs) in environmental decision-making, examining their potential benefits and limitations. To address environmental issues, we propose and compare two generalizable frameworks: an LLMs-assisted framework that leverages LLMs to augment human expertise and coding in traditional decision workflows, and an LLMs-driven framework that aims to automate optimization. Through a water engineering case study focusing on PFAS control, where Environmental Fluid Dynamics Code (EFDC) was used for water environment simulation, we illustrate how to instantiate these frameworks and assess their performance. The case study reveals generalizable insights about these frameworks. Results indicate that both frameworks can contribute to environmental decision-making optimization to varying degrees, though their applicability differs significantly when facing complex decision scenarios. The LLMs-assisted framework, which effectively regulates flow rates and achieves higher PFAS interception, demonstrates how AI can enhance human decision-making while preserving the essential role of domain expertise and professional judgment. In contrast, the LLMs-driven framework faces challenges in handling complex parameter optimization tasks due to constraints such as context window and maximum output length. The findings emphasize the advantages of integrating Artificial Intelligence (AI) with conventional environmental modeling and management practices. This work confirms a crucial principle: LLMs should enhance rather than replace human expertise, with the ultimate responsibility for environmental decisions remaining with humans. The originality of this research lies in its innovative methodological approach, which leverages process design and prompt engineering to integrate cutting-edge AI with conventional environmental models, establishing a foundation for responsible human-AI collaboration in environmental decision-making. While examining current strengths and limitations, this framework robustly generates optimized environmental decision strategies, marking a new exploration in the field.

A human-machine integrated optimization method for long walkway space

ABSTRACT With the development of cities, the design of long walkway spaces have exposed a series of issues. These spaces suffer from layout inefficiencies due to the complex interaction of multiple factors, affecting walking experiences. Optimization schemes for such spaces typically involve the calculation and evaluation of various influencing factors, necessitating comprehensive and systematic judgment. In recent years, the advancement of computer technology has made it possible to utilize genetic algorithms for multi-objective optimization of spatial layouts. Through human-machine collaboration based on genetic algorithms and expert judgment, diverse schemes can be generated more quickly and the optimal solution can be effectively selected. This article aims to explore how to use generative technology combined with expert judgment in a human-machine collaboration approach to address the multi-objective optimization of long walkway spaces. Research results indicate that this process can enhance the efficiency and quality of optimizing layouts for long walkway spaces. After further refinement through manual comparison, the rationality of the results is improved, achieving optimization effects based on the previous layouts. In the future, this method can be promoted to other areas and application scenarios within urban design and planning, demonstrating significant potential for development.

Predictive Equations for Modeling Shear Behavior in RC Columns with Non-Seismic Details for Enhanced Seismic Performance Assessment

Accurate modeling of the hysteretic behavior of reinforced concrete columns with non-seismic detailing is crucial for assessing the seismic performance of reinforced concrete structures. While shear spring models are commonly used to capture nonlinear shear deformation in shear-critical reinforced concrete columns, determining appropriate parameters for these models remains challenging and often relies on expert judgment. This study proposes predictive equations for shear spring parameters that can be integrated into fiber-section elements to enhance the accuracy of modeling reinforced concrete columns. Specifically, empirical equations are developed for the nonlinear modeling parameters of the Pinching4 model implemented in OpenSees. Experimental data of flexural–shear and shear-critical reinforced concrete columns are used to calibrate the modeling parameters for predicting strength, stiffness, pinching, and cyclic degradation behavior. The predictive equations, derived through regression analysis, are validated against experimental data and demonstrated relatively high accuracy in capturing the shear behavior. To evaluate the impact of the shear behavior of reinforced concrete columns on the seismic fragility of non-conforming reinforced concrete frames, two types of numerical models are established with and without the Pinching4 material using the proposed predictive equations. Analytical results indicate that neglecting the appropriate shear behavior of reinforced concrete columns could lead to an overestimation of the seismic performance of non-conforming reinforced concrete frames.

ОЦІНКА ЕФЕКТИВНОСТІ ТЕХНОЛОГІЇ ФОРМУВАННЯ ТЕХНІКИ ПЕРЕМІЩЕННЯ СПОРТСМЕНІВ ПІД ЧАС ПРАКТИЧНОЇ СТРІЛЬБИ З ПІСТОЛЕТУ В РУСІ З ВИКОРИСТАННЯМ ЕКСПЕРТНОГО АНАЛІЗУ

Relevance of the research topic. Currently, science in the field of physical culture and sports includes theoretical and empirical levels and aspects of knowledge and research that closely interact within the framework of most studies that simultaneously include the construction of theoretical models, conceptualization and operationalization of concepts and hypotheses, primary data collection and analysis. The purpose of the article is to determine the effectiveness of the author's technology for the formation of movement techniques during practical shooting of athletes with a pistol in motion. Participants of the study. The study involved 8 qualified athletes specialising in practical pistol shooting. The effectiveness of the author's technology was assessed by reducing the number of errors in the experimental and control groups during the implementation of the technology. Provided that the equivalence of the experimental and control groups is achieved at the initial stage of the experiment, the main hypothesis ‘the use of the author's technology will help to reduce the number of errors during movement among athletes specialising in practical pistol shooting in motion’ should be tested. Research methods. We conducted an experimental study in the laboratory using expert analysis. The results obtained in the course of the study were processed using the methods of mathematical statistics. Results and key conclusions. The research has a clearly expressed quasi-metric character, the evaluation of the movement technique of athletes during shooting is based on expert judgments, and not on accurate objective measurements. Athletes' errors are recorded by experts on the basis of observations, which is a classic example of using a semi-quantitative approach, where each expert assesses the presence or absence of specific errors, and the data is recorded in the form of binary scores (yes/no error). However, this approach allows subjective assessments to be structured for further analysis, even if they are not absolutely accurate. Reliability is ensured by a sufficient number of qualified experts and the consistency of their assessments of the study, namely the participation of five experts who assessed the technique of athletes before and after the experiment. The Kendall's coefficient W=0.357, at a significance level of p<0.05, indicates a moderate but statistically significant consistency of expert assessments. This means that the experts demonstrated a sufficient level of consistency in their judgements, which allows us to consider their assessments reliable for further analysis of the results of the experiment.

Can ChatGPT AI Replace or Contribute to Experts’ Diagnosis for Renovation Measures Identification?

Building energy renovations demand expertise from professionals to guide processes, including diagnostics, project planning, interventions, and maintenance. The emergence of open-access AI, like ChatGPT in November 2022, offers new possibilities for improving these processes by assisting or potentially replacing human experts. This study explores the effectiveness of ChatGPT in diagnosing energy renovation measures. Initial assessments involve basic queries to the AI, followed by the inclusion of additional data and secondary questions to gauge its full diagnostic potential. An existing building case from the literature is given to the AI to define the best energy renovation measures. Expert evaluations and comparisons with research-backed solutions assess the AI’s performance using different degrees of questioning details over 60 repetitions. The results indicate that ChatGPT can provide valuable insights and generate comprehensive lists of feasible measures and preliminary cost calculations and payback, but, in general, it lacks depth and quality without specialized input and preparation. A significant quality improvement was found between the tests with 2023 and 2024 AI versions. Open-access AI proves capable of enhancing renovation diagnostics but remains a complement rather than a replacement for building renovation expert judgment. This research underscores the potential of mainstream AI to democratize access to knowledge, albeit with limitations tied to its dependence on quality inputs and contextual expertise.

Barriers to product return in a circular supply chain: a case from a retailing industry

Abstract The circular supply chain (CSC) plays a pivotal role in delivering sustainable products to end users, profoundly affecting organizational performance and societal welfare. In this ecosystem, effective Product Return Management (PRM) is crucial, as consumers play a pivotal role in returning used products for reuse or recycling. Despite its significance in waste reduction and attaining environmental and economic goals, scant research has explored product return barriers within CSC. This study bridges this gap by investigating and prioritising these barriers to enhance the circular economy. Employing a systematic literature review (SLR), we extracted and screened 13 product return barriers in CSC using the Grey Delphi method. Subsequently, we introduced an enhanced version of the Group Grey-Best–Worst Method (GG-BWM), a novel approach that harmonises expert judgments by addressing uncertainties through Grey Theory and incorporating both individual-level and group-level inconsistency ratios. The findings highlight lack of motivation, lack of consumer awareness, and lack of proper infrastructure as the most critical barriers. Furthermore, sensitivity analysis conducted across 11 scenarios emphasises the significance of our results. This study contributes to the group multiple criteria decision making (MCDM) methods and CSC literature, while offering actionable insights practitioners seeking to improve the effectiveness of their CSC strategies.

DEVELOPMENT OF A PJBL-BASED STATIONERY CIVILIZATION BOOK FOR SELF-CONTROL CHARACTER EDUCATION IN CHILDREN

The focus of this study is on children who have trouble managing their own behavior. This study seeks to establish the improvement of children's self-discipline following the implementation of the Project Based Learning approach through the stationery civilization project. The research methodology utilized is the ADDIE model, which falls under the category of Research and Development (R&D) approaches. This study included ten licensed teachers from different areas to meet analysis requirements. It also included ten validators from expert judgment and five teachers as expert judges. Eight fifth grade students at an elementary school in Nanggulan, Kulon Progo were the participants in this study. Testing techniques, as well as non-testing techniques, were used for the sampling process. The study's findings demonstrate that utilizing the PjBL approach can enhance self-discipline in children, showing a notable distinction (p < 0.05) with a substantial influence level (r = 0.983) equivalent to 96%. The study's effectiveness level falls within the high category N-Gain score 74.246%.

ANALISIS KOMPARATIF BAHASA PEMROGRAMAN UNTUK PEMULA IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN DENGAN METODE SIMPLE ADDITIVE WEIGHTING

Selecting the right programming language for beginners is a significant challenge in software development. This research aims to analyze a comparison of programming languages Python, JavaScript, Java, C++, and PHP using the Simple Additive Weighting (SAW) method to provide an objective recommendation for beginners. The evaluation is based on six key criteria: ease of learning, job opportunities, execution speed, versatility, community & ecosystem, and maintenance cost, with weights assigned according to the importance of each criterion. Data were obtained through literature review, user surveys, and expert judgment analysis. The results show that Python received the highest preference score (1.13), followed by JavaScript (1.08), Java (1.18), C++ (1.27), and PHP (1.34). Python excelled in terms of ease of learning and versatility, while JavaScript led in community and ecosystem aspects. The study concludes that Python is the most suitable programming language for beginners, especially for foundational learning and general application development. These findings have implications for the development of programming curricula and recruitment strategies in the information technology industry.

Socio-Emotional Competencies in Primary School Through A Social Constructivist Approach

Theme: The social constructivist approach allows to establish processes of transformation in the educational field, guiding the motivated participation of the teacher and the concentration of efforts to promote socio-emotional competencies. Objectives: This article, derived from the doctoral research process, had as its general objective: To interpret the different pedagogical conceptions on the development of socio-emotional competencies guided through teaching under a social constructivist approach applicable in basic primary education. Therefore, the specific objectives were: 1. To identify the different pedagogical conceptions of the teacher on teaching with a social constructivist approach in basic primary education of the public educational institution. 2. To describe the pedagogical experiences on the development of socio-emotional competencies in basic primary education in public educational institutions. Method: maintains a qualitative approach, under the socio-critical paradigm, using hermeneutical phenomenology as a method based on the case study, in which six moments or phases were followed. The participants were ten teachers linked to the Luis Carlos Galán Educational Institution in the municipality of Villa Garzón de Putumayo, to whom inclusion and exclusion criteria were applied, as well as informed consent. The collection instruments were the focus group and semi-structured interview scripts, validated through expert judgment. Conclusion: Social constructivism as a support approach for the development of socio-emotional competencies implies highlighting the importance of social contact and the relevance of the learning context, showing the need to address the processes of social interaction.

Design of Automatic Security System Based Internet of Things at the Museum

This research aims to determine the procedures for developing an Internet of Things (IoT)-based Automatic Security System as a security device for museum objects and to test its feasibility based on user feedback in museums. The Design Thinking model used consists of five stages: Empathize (understanding the user’s perspective), Define (gathering information on the encountered problems), Ideate (generating innovative ideas), Prototype (creating a prototype of the obtained ideas, ensuring the functionality of each device feature, and validating the product through expert judgment for feedback and product revision), and Testing (evaluating the system’s feasibility using Usability standards based on ISO/IEC 9241-11, focusing on Effectiveness, Efficiency, and Satisfaction with user involvement). The research results indicate that the IoT-based Automatic Security System has met optimal outcomes based on Usability aspects, namely: the Effectiveness analysis showed a ratio above 100% with a Very Effective level of achievement; the Efficiency aspect analysis received user feedback with a device usage rate of 1.35 goals per second; and the Satisfaction analysis, using the System Usability Scale (SUS) questionnaire, scored 78.25, indicating an Excellent level, suggesting that the system is worthy of use.

Comprehensive benefits evaluation of low impact development using scenario analysis and fuzzy decision approach

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making. The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. The comprehensive benefits of 26 LID configurations were conducted in Chenglong Road Subdistrict under five rainfall return period scenarios of 5, 10, 20, 50, and 100 years. The results show that LID measures, particularly combinations of sunken green spaces and permeable paving, offer significant reductions in runoff and peak flow, along with improved flood mitigation across multiple return periods. Additionally, this study identifies practical LID implementation priorities for local decision-makers. The relative closeness is influenced by the indicators and non-calibrated parameters. However, it overall does not affect the main trends and key insights derived. The robustness of the proposed approach is reinforced by four key aspects: the impact of the Thiessen polygon method in ArcGIS, the influence of composite runoff coefficient and iterative optimization in SWMM, the effect of hesitant fuzzy linguistic sets and TOPSIS on weight calculation, and the contribution of simulations under different return periods to stability analysis.

Expert Judgment Supporting a Bayesian Network to Model the Survival of Pancreatic Cancer Patients.

Purpose: Pancreatic cancer is known for its poor prognosis. The most effective treatment combines surgery with peri-operative chemotherapy. Current prognostic tools are designed to predict patient outcomes and inform treatment decisions based on collected data. Bayesian networks (BNs) can integrate objective data with subjective clinical insights, such as expert opinions, or they can be independently based on either element. This pilot study is one of the first efforts to incorporate expert opinions into a prognostic model using a Bayesian framework. Methods: A clinical hybrid BN was selected to model the long-term overall survival of pancreatic cancer patients. The SHELF expert judgment method was employed to enhance the BN's effectiveness. This approach involved a two-phase protocol: an initial single-center pilot phase followed by a definitive international phase. Results: Experts generally agreed on the distribution shape among the 12 clinically relevant predictive variables identified for the BN. However, discrepancies were noted in the tumor size, age, and ASA score nodes. With regard to expert concordance for each node, tumor size, and ASA score exhibited absolute concordance, indicating a strong consensus among experts. Ca19.9 values and resectability status showed high concordance, reflecting a solid agreement among the experts. The remaining nodes showed acceptable concordance. Conclusions: This project introduces a novel clinical hybrid Bayesian network (BN) that incorporates expert elicitation and clinical variables present at diagnosis to model the survival of pancreatic cancer patients. This model aims to provide research-based evidence for more reliable prognosis predictions and improved decision-making, addressing the limitations of existing survival prediction models. A validation process will be essential to evaluate the model's performance and clinical applicability.

Prioritization of Preventive Measures: A Multi-Criteria Approach to Risk Mitigation in Road Infrastructure Projects

Risk management in construction projects is a critical process aimed at identifying, evaluating, and mitigating potential risks that could impact project performance. Preventive measures play a central role in this process, serving as proactive strategies to minimize the likelihood and impact of risks on project outcomes. This study involved 37 experts from multidisciplinary fields related to road infrastructure, ensuring a diverse and comprehensive perspective on risk evaluation and prevention. The DELPHI method was employed to systematically define key risks and their corresponding preventive measures, providing a structured foundation for further analysis. The experts evaluated 302 preventive measures across 56 risks using 4 predefined criteria: implementation costs, time required for implementation, implementation complexity, and probability of success. A multi-criteria decision making (MCDM) approach was then applied to analyze these evaluations, enabling the prioritization of preventive measures and the allocation of resources toward the most effective strategies. Additionally, fuzzy logic was employed to analyze and validate the results, providing a complementary approach to the MCDM methodology. The results of this research provide a robust framework for risk management, offering practical guidance for decision makers in the construction industry. By integrating expert judgment, systematic evaluation, and advanced analytical methods, this study delivers actionable insights and establishes a reliable methodology for enhancing the effectiveness of risk mitigation in road infrastructure projects.

Integrating Pancasila Values in Listening-Speaking Materials for Junior High Students

This study aims to address the need to promote the integration of Pancasila student profile values—specifically independence, critical thinking, and creativity—into listening-speaking supplementary materials for grade VII students at a state junior high school in Kolaka Utara. By understanding the target needs and learning needs, this research seeks to develop listening-speaking supplementary materials that are appropriate for promoting both English language proficiency and Pancasila values. This research employs a research and development (R&D) approach in education, utilizing the well-known ADDIE development model. Data collection methods included qualitative interviews and document analysis, as well as quantitative questionnaires. The needs analysis results indicated that students required materials for learning descriptive text in oral form but faced challenges with word pronunciation, grammar, vocabulary expansion, and understanding spoken information. Consequently, students desired materials that improved their vocabulary and pronunciation with an emphasis on multimedia elements. The listening-speaking supplementary materials integrated the values in implicitly and explicitly. These materials consisted of six units following a genre-based approach and used authentic materials with technology integration. The appropriateness of these materials was demonstrated through positive feedback from students and teachers, who found them engaging, well-aligned with learning objectives, relevant, and sufficient in exercises and tasks. Expert judgment rated the materials with a mean score of 3.9, categorized as “very appropriate” within the range of 3.26 < x ≤ 4.00. Furthermore, this research contributed to developing educational resources, and it is expected that students can engage with good value in their future lives.

Developing Reading Material Based on Local Culture at SMP Negeri 32 Medan

Integrating local culture into the process of learning a foreign language has only recently begun to draw attention. Therefore, local culture-based English reading materials are a solution to attract students' interest in reading English. The objectives of this research are to develop the students’ English reading materials based on local culture at SMPN 32 Medan, to find out the validation of English reading material based on local culture at SMPN 32 Medan, and to explain the effectiveness of English reading material based on local culture at SMPN 32 Medan. This research adapted the R&D model proposed by Sugiyono (2013) with some modifications. The data was taken from students and teachers of SMPN 32 Medan and also expert judgment from Universitas Muslim Nusantara Al Washliyah Medan. The instruments of this research are questionnaire, documentation and test. The result of validation from the material expert judgment is 91.66% and the material expert judgment is 85.71%. It means that the product in very high category. The result of validation from the teacher is also high category, 88.63% . In addition, the product was also tested on students and the results showed a significant increase in scores after the students learned using the local culture-based reading materials. Unit 1 increased up to 10.36%, Unit 2 increased up to 12.15%, Unit 3 increased up to 14.06% and Unit 4 increased up to 15.26%. The average score of students, which was previously the highest at 78, increased to 89. The conclusion based on the research results shows that these English reading materials can be adopted as teaching materials as additional learning for English lessons that are interesting, contextual, and adapted to the Merdeka curriculum.