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The main objective of the Citizenship Education (PKn) course in higher education is to develop students into individuals with nationalist, participatory, and critical characters towards national dynamics. Conventional learning approaches that are still dominant in higher education, such as one-way lectures and memorization of materials, are considered less able to encourage active participation and the development of critical thinking patterns of students in the Citizenship Education (PKn) course. This study aims to identify the effectiveness of the application of innovative learning methods in improving students' activeness and critical thinking skills. Using a descriptive qualitative approach, data were collected through classroom observations, interviews with lecturers and students, and analysis of lecture documents from three study programs at Merdeka University of Malang. The results of the study showed that the application of learning strategies such as Project Based Learning, role playing, utilization of interactive multimedia, collaborative discussions, and nationality-based simulations were able to significantly increase students' participation and critical understanding. This method is relevant to the needs of learning in the era of globalization that demands digital literacy, cross-disciplinary collaboration, and contextual problem solving. Based on these findings, this study recommends the integration of innovative methods into the Civics curriculum in higher education, pedagogical training for lecturers, and the provision of technological infrastructure that supports the implementation of competency-based learning in the era of globalization.

Background: The rapid development of blockchain technology and smart contracts has fundamentally transformed contractual relationships by shifting the role of human interpretation and enforcement toward automated, code-based, and decentralized systems. This transformation generates complex legal implications, particularly regarding the evolution of contractual liability, which is increasingly distributed and no longer centered on a single legal subject. Objective: This study aims to analyze the evolution of contractual liability in smart agreements and examine how such transformation affects the fundamental principles of traditional contract law within modern legal systems. Methods: This research employs a normative and conceptual legal approach, supported by an analysis of blockchain regulations across multiple jurisdictions, case studies of smart contract implementation, and a comparative legal analysis between civil law and common law systems, complemented by a multidisciplinary literature review. Results: The findings indicate that contractual liability in smart agreements has evolved from a centralized fault-based liability model to an algorithmic, distributed, and code-dependent liability structure within blockchain ecosystems. This evolution creates new legal challenges concerning the attribution of liability, legal certainty, and the limitation of judicial intervention in automated contractual arrangements. Furthermore, the study identifies a tension between technological efficiency and substantive legal justice, highlighting the need for adaptive legal frameworks capable of accommodating decentralized technologies while ensuring the protection of legal rights and accountability of involved parties.

This research extends the previous work of Tsaqila, Winiarti, and Widaningrum (2024), who applied the Complex Proportional Assessment (COPRAS) method within a decision support system for supermarket branch location selection. Unlike the prior study, which focused on Ponorogo through a web-based framework, this study expands the implementation of COPRAS to the Medan Area, Medan Kota, Medan Polonia, dan Medan Maimun districts, adapting it to local urban, social, and economic characteristics. The main objective is to identify the most strategic site for a new supermarket by analyzing multiple criteria, including land cost, population density, accessibility, safety, cleanliness, and disaster risk. Data were collected from both field surveys and official government publications. The findings reveal that the COPRAS method provides reliable and objective assessments among the evaluated alternatives, with Medan Area emerging as the most suitable location for supermarket development. Overall, this study broadens the practical scope of the COPRAS method in a different regional context and reinforces its reliability and adaptability as a multi-criteria decision-making tool in the modern retail industry.

Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The assessment criteria are based on the 2025 FLS3N Technical Guidelines, consisting of voice quality, vocal technique, expression, and performance. The TOPSIS method is applied to generate alternative rankings based on the highest preference value. The system is developed using a web-based software development approach and tested using participant data from both male and female categories. The results indicate that the system can provide objective and consistent ranking recommendations, thereby assisting schools in selecting the best students to represent them in the FLS3N competition.

Reading comprehension, especially in personal letter texts, is challenging for many Indonesian high school students due to limited vocabulary and low motivation. Blooket, a game-based learning platform, offers potential to improve engagement and learning outcomes.Objective: This research aims to examine the effectiveness of Blooket learning media in improving students’ reading comprehension of personal letters at Grade XI of SMA PGRI Pekanbaru. A quantitative approach with a quasi-experimental non-equivalent control group design was used. The sample consisted of 39 students, divided into an experimental group taught with Blooket and a control group taught conventionally. Pre-tests and post-tests (25 multiple-choice items) were administered, and data were analyzed using normality, homogeneity, The experimental group’s mean score increased from 55.21 to 84.96, while the control group improved from 51.53 to 72.00. The paired sample t-test yielded p = 0.000 (<0.05), indicating a significant effect of Blooket on reading comprehension. Blooket’s interactive and competitive features effectively enhanced students’ reading comprehension of personal letters, motivation, and participation, making it a valuable alternative for teaching short functional texts in EFL classrooms.

Recommender systems play a critical role in shaping user decisions across digital platforms; however, the increasing complexity of recommendation algorithms has raised serious concerns regarding transparency, trust, and accountability. This study focuses on enhancing the transparency of recommender systems by integrating Explainable Artificial Intelligence (XAI) techniques within a MovieLens-based recommendation framework. The primary problem addressed is the opacity of conventional recommendation models, which limits user understanding of why certain items are recommended and may reduce trust, perceived fairness, and system acceptance. Accordingly, the main objective of this research is to design and evaluate a hybrid explainable recommender system that balances predictive accuracy with human-understandable explanations. The proposed approach combines Matrix Factorization, feature-importance-aware neural networks, and knowledge graph embeddings to construct a robust recommendation model. To enhance explainability, multiple XAI strategies are integrated, including model-agnostic methods (LIME, SHAP, and CLIME), argumentation-based explanations, and context-aware personalized explanations. A comprehensive evaluation framework is employed, incorporating algorithmic metrics (accuracy, fidelity, robustness, counterfactual consistency, and fairness) alongside human-centered evaluations measuring trust, transparency, cognitive load, and perceived usefulness. Experimental results demonstrate that the knowledge graph–enhanced hybrid model achieves superior recommendation accuracy compared to baseline approaches. Moreover, context-aware explanations consistently outperform other methods in terms of fidelity, robustness, and user-perceived transparency, while argumentation-based explanations are found to be the most persuasive. CLIME offers a strong balance between technical stability and interpretability. The findings indicate that no single explainability technique is universally optimal; instead, hybrid and adaptive explanation strategies are most effective. In conclusion, this study confirms that human-centered, context-adaptive XAI significantly improves transparency and user trust in recommender systems, highlighting explainability as a fundamental component rather than an optional enhancement.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

This study aims to examine the influence of shopping lifestyle, hedonic shopping value, and e-wallet on impulsive buying behavior among students of the Management Study Program at the Faculty of Economics, Universitas Kristen Indonesia Toraja. This research is quantitative in nature, using multiple linear regression analysis. The sample consisted of 80 respondents, determined using Slovin's formula and purposive sampling technique as the basis for selecting respondents deemed relevant to the research objectives. The results of the study indicate that, partially, shopping lifestyle significantly influences impulsive buying with a t-value of 3.608 and sig = 0.001, hedonic shopping value significantly influences impulsive buying with a t-value of 2.186 and sig = 0.032, and e-wallet significantly influences impulsive buying with a t-value of 3.043 and sig = 0.003. Simultaneously, all three independent variables significantly affect impulsive buying, with a significance value of 0.000 < 0.05. The coefficient of determination (R2) value of 0.223 indicates that the three independent variables explain 22.3% of the variation in impulsive buying behavior, while the remaining 77.7% is explained by other factors not investigated in this study. These findings suggest that shopping lifestyle, the value gained from shopping, and the convenience of digital transactions through e-wallets are triggers for unplanned buying behavior.

As a region with cultural, religious, and ethnic diversity, Bali functions as a social laboratory to understand the interaction between religious communities. The implementation of Islamic Religious Education (PAI) plays a strategic role in building harmony in a multicultural society. Therefore, comprehensive Islamic Religious Education (PAI) is needed to achieve the goal of a harmonious, peaceful, and secure community life even though they have different religions, tribes, nations and even languages. Based on the complexity of the problems above, the focus of the research is: 1) What is the strategy of educators in integrating Islamic values that are relevant to cultural diversity in Bali? 2) What are the challenges faced in implementing PAI in Bali's multicultural environment? 3) What is the PAI curriculum that supports the values of tolerance and diversity? The objectives of this study are: 1) To determine the strategy of educators in integrating Islamic values that are relevant to cultural diversity in Bali. 2) To determine the challenges faced in implementing PAI in Bali's multicultural environment. 3) To determine the PAI curriculum that supports the values of tolerance and diversity. This type of research is qualitative with a descriptive approach, informants are determined purposively. As sources of information (informants) are the Head of the Environment, Head of Customs, Muslim Community Leaders, Head of Foundation, Head of School, students and Parents. Data were collected through interviews, observations and documentation, then analyzed with the stages of data reduction, data presentation and drawing conclusions. 

This study aims to analyze the factors influencing the income levels of pineapple farmers in Siabal-Abal V Village, Sipahutar Subdistrict, North Tapanuli Regency. The research focuses on several key variables, namely land size, production quantity, production tools, and farming experience, which are presumed to be associated with farmers’ income. A quantitative research approach was employed, using multiple linear regression analysis to examine the simultaneous and partial effects of each variable on the income of pineapple farmers. The population of this study comprised all pineapple farmers in Siabal-Abal V Village, with a sample of 65 respondents selected through a simple random sampling technique. Data were collected through interviews and structured questionnaires designed in accordance with the research objectives. The results of the data analysis indicate that, simultaneously, land size, production quantity, production tools, and farming experience have a significant effect on the income of pineapple farmers. However, the partial test results reveal that only land size has a positive and significant effect on farmers’ income. Meanwhile, production quantity, production tools, and farming experience do not show a statistically significant influence. These findings indicate that land size is the dominant factor in determining the income level of pineapple farmers in the study area. Therefore, improving access to agricultural land or optimizing the utilization of existing land is an important strategy for increasing the income of pineapple farmers in Siabal-Abal V Village.

Games that were initially enjoyed individually and offline have now evolved into online games that allow interaction, competition, and friendship with other players in real-time. Online games are now increasingly popular, one of which is Mobile Legends. This game can be downloaded through the Play Store and App Store. In April 2024, Mobile Legends ranked the most popular game by downloads on the statista.com website. The research objective was to determine the influence of event marketing, content marketing, influencers, and the social environment on the decision to purchase Mobile Legends game skins in Kebumen Regency. The method used was non-probability sampling with a purposive sampling technique aimed at 100 users. Data collection techniques used questionnaires and literature studies. The data analysis technique used multiple linear regression, which was then processed with the SPSS application version 25.0 for Windows. The results showed that event marketing had a significant effect on purchasing decisions, content marketing had a significant effect on purchasing decisions, influencers had no significant effect on purchasing decisions, the social environment had no significant effect on purchasing decisions, and event marketing, content marketing, influencers, and the social environment simultaneously had a significant effect on purchasing decisions.

This study aims to investigate the empirical impact of population size and unemployment rates on economic growth in Central Kalimantan Province. The research focuses on the regional context of Central Kalimantan, utilizing a longitudinal dataset covering the eleven-year period from 2013 to 2024. To achieve the research objectives, the study employs quantitative analysis through the SPSS software package, utilizing multiple linear regression as the primary analytical tool to examine the relationship between the dependent variable (economic growth) and the independent variables (population and unemployment). The findings of the analysis reveal a divergent impact between the two independent variables. The results indicate that the population has a significant and positive influence on economic growth in Central Kalimantan during the 2013-2024 period, suggesting that demographic factors play a crucial role in regional expansion. Conversely, the unemployment rate was found to have no significant effect on economic growth within the same timeframe. Furthermore, the study conducted a comprehensive suite of classical assumption tests to ensure the validity and reliability of the statistical model. The results of these diagnostics confirm that the regression model adheres to the assumption of normality, shows no evidence of multicollinearity among the variables, and is free from symptoms of heteroscedasticity. Additionally, the analysis concludes that the model does not exhibit any issues related to autocorrelation. Consequently, the regression model is deemed statistically robust and appropriate for providing an accurate representation of the economic dynamics in Central Kalimantan.

This study aims to simulate the M/M/1 queueing system using Python through a Modeling and Simulation approach supported by the Discrete-Event Simulation (DES) method. The objective of the research is to analyze key performance indicators of queueing behavior, including arrival time, service time, waiting time, queue length, and server utilization. The methodology employs DES, which models system behavior based on discrete events such as customer arrivals, service initiation, and service completion. The simulation generates stochastic arrival and service times using Poisson and exponential distributions, respectively. The results indicate that the DES-based M/M/1 simulation accurately reflects theoretical queueing behavior, showing increases in waiting times and queue lengths when arrival rates approach service rates, while server utilization corresponds to system load intensity. The findings demonstrate that DES is an effective approach for analyzing queue performance and can be extended to more complex models such as multi-server systems, priority queues, and predictive simulations using artificial intelligence.

This study analyzes the maintenance process of the Roll Bending machine used in the wind tower production line at PT Kenertec Power System, Cilegon, Indonesia. The Roll Bending machine plays a crucial role in shaping steel plates into cylindrical shell components, which are later assembled into wind tower sections. The objective of this research is to identify maintenance patterns, types of failures, and improvement strategies to ensure machine reliability and operational efficiency. The research employed observation, interviews with maintenance personnel, and documentation review to collect relevant data. The findings show that the machine experienced multiple failures, mostly related to hydraulic system leaks, PLC programming errors, and component wear such as cylinders, seals, and gear pumps. A significant increase in corrective maintenance activities occurred between August 2023 and April 2024, particularly in February 2024, indicating the need for a more consistent predictive maintenance strategy. The implications of this study highlight that optimized maintenance scheduling and monitoring are essential to reduce downtime, avoid production delays, and maintain product quality. This research is expected to support maintenance decision-making and contribute to the improvement of industrial machine reliability in wind tower manufacturing operations.

This study aims to examine the application of religious moderation in overcoming religious fanaticism in Indonesia, a country with high religious and cultural diversity. The main objective of this study is to understand how religious moderation can reduce the potential for social conflict caused by religious fanaticism. Using a systematic literature review (SLR) methodology, the database used to search for literature is data contained in Google Scholar and the Covidence Application, 6 relevant studies were analyzed using the PICOC-PRISMA criteria. The results of the study indicate that religious moderation can be implemented through inclusive religious education, the role of mass media in disseminating values of tolerance, and government policies that support harmony between religious communities. In addition, factors that influence the emergence of religious fanaticism include narrow understanding of religion, social injustice, and the spread of radical ideology through social media. This study contributes to strengthening multiculturalism and harmony between religious communities in Indonesia.

This study was conducted by Muhammad Sangkot Pangidoan (Student ID: 20090033) under the title “The Effect of Product Quality on Business Development in the Culinary Sector at Rumah Makan Incor Laru, Tambangan District, Mandailing Natal Regency.” This quantitative research employed multiple linear regression analysis. Data were collected through questionnaires distributed to 96 respondents using a simple random sampling technique. The objective of this study was to determine and examine the effect of product quality on the business development of Rumah Makan Incor Laru. The research was carried out in Tambangan Tonga Village from December 2023 to August 2025. The independent variable in this study is product quality, while the dependent variable is business development. The results show that product quality has a positive and significant effect on business development, with a significance value of 0.000 < 0.05. The coefficient of determination (R²) is 0.522, indicating that 52.2% of changes in business development are explained by product quality, while the remaining 47.8% are influenced by other factors outside this study. The simple regression equation obtained is Y = 24.583 + 0.275X, with a t-count of 9.340 greater than the t-table value of 2.372, meaning the hypothesis is accepted.

Background: Mental health during pregnancy is influenced by multiple factors, including the social environment. Family support, peer interactions, and community engagement are critical determinants of psychological well-being, yet their impact local community health settings remains underexplored. Objective: This study aimed examine the influence the social environment the mental health pregnant women Tilongka Billa Community Health Center. Methods: A quantitative correlational study was conducted with 70 pregnant women selected through purposive sampling. Data were collected using structured questionnaires assessing demographic characteristics, social support (family, peer, and community), and mental health status (Perceived Stress Scale and Edinburgh Postnatal Depression Scale). Descriptive statistics summarized the participants’ characteristics, while Pearson Spearman correlation tests analyzed relationships between social environment factors mental health outcomes (p < 0.05). Results: The results indicated that family support had the highest mean score (4.1 ± 0.7), followed by peer support (3.8 ± 0.8) and community involvement (3.5 ± 0.9). Mental health assessments revealed moderate stress levels (18.2 ± 5.0) and mild depression risk (9.5 ± 4.3). Correlation analysis showed significant negative relationships between social support and mental health indicators, with family support exhibiting the strongest correlation with reduced stress (r = -0.48, p = 0.002) and depression (r = -0.52, p = 0.001). Conclusion: The study demonstrates that supportive social environment, particularly family and peer support, plays crucial role in reducing stress and depressive symptoms among pregnant women. Strengthening social support networks through counseling, peer groups, and community engagement programs may enhance maternal mental health and promote positive pregnancy outcomes.

This study aims to implement an information retrieval system for cryptocurrency data using an attribute-based approach integrated with the Vector Space Model (VSM). The primary objective is to develop a system capable of retrieving the most relevant digital asset information according to specific search attributes, including positive sentiment, price fluctuation, and prediction confidence level. The research adopts a descriptive qualitative method combined with an experimental approach to evaluate the retrieval performance of the cosine similarity algorithm on normalized numerical data. Data preprocessing and attribute weighting were conducted to ensure consistency and improve retrieval accuracy. The experiment demonstrates that the proposed system achieves a Precision@5 value of 1.0, which indicates that all top-five retrieved results are fully relevant to user queries. These findings validate the effectiveness of the attribute-based VSM in analyzing multidimensional cryptocurrency datasets. Overall, this research contributes to the advancement of information retrieval applications in the cryptocurrency domain, particularly for supporting data-driven decision-making and intelligent financial analysis.