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Per capita expenditure is an important indicator of household welfare because it reflects the economic capacity and consumption patterns of the community, as explained in Engel's Law. In regions with diverse geographical characteristics such as Papua Province, spatial analysis is needed to understand the variations in expenditure between districts/cities and the differences between urban and rural areas. This study aims to analyze the spatial distribution of per capita expenditure percentages for food and non-food items in nine districts/cities in Papua Province during the 2022–2024 period. The research data was sourced from the National Socioeconomic Survey (Susenas). The methods used included quantile-based choropleth mapping using QGIS, attribute data merging through table joins, and Pearson's correlation test to evaluate the consistency of spending patterns between years. The analysis results show that food and non-food spending patterns were relatively stable during the observation period with high correlation values (r = 0,85–0,93), although spatial variations between regions were still apparent. Mamberamo Raya Regency consistently had the highest proportion of food spending (>68%), while Jayapura City showed the lowest proportion. These findings indicate spatial disparities related to urbanization levels and economic access. Spatial visualization proved effective in revealing regional disparity patterns that were not fully apparent through conventional statistical tables and has the potential to support the formulation of more evidence-based regional development policies.  

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.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

This study aims to develop an interactive digital learning media based on Wordwall, named TANGKAS (Tantangan Asyik Ngulik Pecahan Kelas Empat Seru), as a practice tool for addition and subtraction of fractions in fourth-grade elementary students. The development process employed the ADDIE model, which includes the Analyze, Design, Develop, Implement, and Evaluate stages. The research subjects consisted of 29 fourth-grade students at SDN Ngemplak I Baureno. The needs analysis revealed that learning activities were still dominated by conventional methods with limited use of digital media, resulting in low student engagement. To address this issue, TANGKAS was developed using a maze chase game design to enhance motivation and support engaging fraction practice. The validation results indicated a material validity score of 94% (highly valid) and a media validity score of 80% (valid). The practicality aspect obtained 81.8% from students, both categorized as highly practical. The effectiveness test showed an improvement in learning outcomes with N-Gain scores of 0.6 for fraction addition and 0.54 for fraction subtraction, both classified as moderate. Therefore, TANGKAS is proven to be feasible, practical, and effective as an interactive game-based learning media to support students’ understanding of mathematics in elementary school.

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.

This quasi-experimental study examines the effectiveness of Artificial Intelligence (AI)-assisted learning in enhancing critical thinking skills among undergraduate history students. The study involved 60 students divided into experimental and control groups. The experimental group received AI-supported instruction integrating adaptive learning modules, scaffolded source-analysis prompts, and guided argumentative discussions facilitated by conversational AI tools, while the control group followed conventional lecture-based instruction. Data were collected using a validated critical thinking test, classroom observation protocols, and semi-structured interviews. Quantitative data were analyzed using paired and independent sample t-tests, while qualitative data were examined through Miles and Huberman’s interactive analysis model. Results indicate statistically significant improvements in critical thinking scores in the experimental group compared to the control group. Thematic findings reveal enhanced sourcing, contextualization, corroboration, and evidence-based argumentation skills. However, minor risks of over-reliance on AI highlight the need for instructional scaffolding and ethical guidance. The findings suggest that AI can function as a cognitive scaffold that strengthens historical thinking and metacognitive awareness when implemented within a structured pedagogical framework.

Empowering agricultural human resources is a strategic approach to support sustainable agricultural development and the achievement of the Sustainable Development Goals (SDGs), particularly SDG 2 (Zero Hunger), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth). This community service article aims to describe the implementation of an agricultural instructor empowerment training program conducted through collaboration between Universitas Negeri Surabaya (Unesa) and the Ngudi Luhur Self-Reliant Agricultural and Rural Training Center (P4S) in Blitar Regency. The activity involved approximately 50 participants consisting of agricultural instructors and local agricultural practitioners. The methods included Focus Group Discussions (FGDs) and field visits to superior corn cultivation areas managed by P4S. The results indicate an increase in participants’ understanding of the importance of agricultural innovation and technology utilization to enhance productivity, supported by experiential learning through direct observation of high-yield corn fields compared to conventional practices. This program strengthens the role of agricultural instructors as innovation dissemination agents and contributes to the achievement of sustainable development goals in the agricultural sector.

Shaping central banking for sustainability has become increasingly relevant as climate change and the pursuit of sustainable development challenge the conventional scope of monetary policy. Green monetary policy reflects efforts to align central banking with environmental and economic objectives, yet the scholarly literature on this issue remains fragmented. This study employs a bibliometric approach using R Studio (Bibliometrix) to analyze publications indexed in Scopus from 2015 to 2025. The dataset comprises more than 1,200 documents with an annual growth rate of nearly 12%, signaling the rapid expansion of research in this field. Bibliometric techniques, including citation mapping, co-authorship analysis, and keyword co-occurrence, are applied to identify influential authors, sources, and thematic clusters. The results indicate a steady increase in international collaboration and a consolidation of research themes, reflecting the growing importance of sustainability in central banking discourse. This study is expected to contribute by providing a structured overview of the intellectual landscape of green monetary policy, clarifying its links with sustainable development and climate change, and offering guidance for future research and policy innovation in sustainable central banking.

This study presents a high-accuracy real-time soft failure detection framework for large-scale fiber-to-the-home(FTTH) optical access network using a hybrid ensemble of Isolation Forest and One-Class Support Vector Machine (OCVSM). The proposed model was trainde and validated on a real-word multivariate performance dataset comprising more than 1.8 million samples collected at 5-minute intervals from 50 Optical Line Terminal (OLTs) and over 3,000 Optical Network Terminals (ONTs) across a five-month periode(June-October 2025). Ground-truth validation was performed using 111 confirmed network incidents in October 2025 affecting 12,990 customer. The hybrid ensemble achieved Precision 0.940, Recall 0.982, with an average detection delay of only 7.8 minutes-representing an 87.7% reduction compared to conventional manual response (63.5 minutes). The framework significantly outperforms traditional threesholding and recent ML-based methods while demonstrating practical deployability in live operational enviroments.

This research aims to design and implement a village MSME website as a centralized digital promotional medium to overcome the limitations of conventional marketing and expand the market reach of local products more effectively and sustainably. The system was developed using the waterfall method, encompassing requirements analysis, design, implementation, testing, and maintenance. The system was developed using the PHP programming language and the CodeIgniter framework based on the Model-View-Controller (MVC) architecture to ensure a structured, efficient, and maintainable development process. The implementation resulted in a responsive and user-friendly website equipped with key features such as an informative product catalog, village MSME profiles, and a content management system via an admin dashboard that allows MSMEs to update data independently and flexibly. Functional testing demonstrated that all features functioned well and reliably according to user needs. Therefore, this village MSME website can be concluded as an effective digital solution for increasing the visibility of local products, strengthening MSME competitiveness, and supporting village economic growth through sustainable and integrated online promotion.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

Micro, Small, and Medium Enterprises (MSMEs) are the backbone of regional economies, including in the city of Banjarmasin, as they play a crucial role in job creation and income generation for local communities. However, many MSMEs continue to face challenges in the marketing aspect, particularly due to their reliance on conventional marketing methods that have limited reach and require relatively high costs. In response to these issues, this community service program aims to promote the digitalization of MSME marketing methods in Banjarmasin through intensive training and mentoring in the utilization of social media marketing. The implementation methods include an initial needs assessment survey to identify partners’ levels of understanding, workshops on digital marketing strategies, and hands-on mentoring for direct implementation on social media platforms such as Instagram and Facebook. The results of the program indicate a significant improvement in MSME actors’ knowledge and skills, particularly in managing business accounts, developing content strategies, and creating visually appealing content that aligns with target market characteristics. Furthermore, MSME partners were able to expand their market reach more effectively and efficiently, which is expected to enhance business competitiveness and sustainability in the digital era.

This study aims to determine the effect of using number puzzle media on the ability to recognize number symbols in early childhood at Kihajar Dewantoro VI Kindergarten. The background of this study is based on the importance of concrete and interesting media in supporting early mathematics learning, especially the introduction of number symbols in children aged 4–5 years. The study used a quantitative method with a quasi-experimental design through a comparison between conventional learning and learning using number puzzle media. Data were collected through observations of aspects of the ability to recognize number symbols and match numbers. The results showed that the average ability of children in conventional learning only reached a score of 6, while learning using number puzzle media increased significantly to a score of 13.1. These findings indicate that number puzzles can make children more focused, enthusiastic, and easier to understand number symbols in a concrete and meaningful way. Thus, number puzzle media is proven to be effective in improving the ability to recognize number symbols in early childhood and can be used as an alternative mathematics learning media in PAUD.

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.

Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.

This study aims to explain the application of various differentiated learning techniques based on Problem-Based Learning (PBL) to improve analytical and writing skills related to procedural texts among junior high school students. This research is based on students' lack of ability to understand and compose procedural texts methodically and in accordance with language conventions, which is caused by the prevalence of conventional teacher-centered learning. This study used a descriptive qualitative methodology involving seventh-grade students from a junior high school that has adopted the PBL model in Indonesian language subjects. Data were collected through observation, interviews, and documentation, then analyzed qualitatively. The results showed that the application of PBL along with differentiated learning and TPACK increased student engagement, accommodated diverse learning styles, and fostered critical thinking, analytical abilities, and collaborative skills. Learning became more meaningful and relevant, enabling students to compose procedural texts more effectively. This study recommends the application of the PBL model with differentiation as an innovative strategy to improve the quality of Indonesian language education in junior high schools.

The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

This study examines the comparative volatility of gold and Bitcoin over the period January 2020 to August 2025, using monthly data and employing descriptive statistics, the Augmented Dickey-Fuller (ADF) test, GARCH (1,1), and the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model estimated with EViews 13. The results show that Bitcoin is characterized by extreme and persistent volatility, reflecting its speculative nature, whereas gold remains stable and functions as a conventional safe-haven asset. Correlation analysis indicates that the relationship between gold and Bitcoin is generally weak but dynamic, as the strength and direction of their co-movements change across different market conditions. These findings highlight the potential role of gold as a hedge and Bitcoin as a speculative diversifier, offering insights for portfolio diversification and risk management. These results also suggest that investors should carefully consider their risk tolerance and investment horizon when allocating assets between traditional and digital commodities.

The rapid development of information technology encourages the use of digital media as an educational tool in the health sector, particularly for pregnant women. One of the problems faced by Klinik Bidan Rima Pondok Meja is the limited use of conventional educational media, such as books and posters, which are considered less attractive and difficult to understand. This study aims to design and develop an Android-based Augmented Reality (AR) application as an educational medium for nutrition and fetal development for pregnant women. The application presents three-dimensional (3D) visualizations of fetal development from week to week, along with information on nutritional needs during pregnancy. The system development method used in this research is the Prototype model, while the Augmented Reality technology applies marker-based tracking. The development tools used include Unity, and Blender 3D. The result of this study is an Android-based AR application prototype that provides interactive and easily understandable information about fetal development and maternal nutrition. This application is expected to increase learning interest and understanding of pregnant women in maintaining a healthy pregnancy at Klinik Bidan Rima Pondok Meja.

Conventional management of sports services often leads to inefficiency and limited public access to experts and facilities. Reliance on manual systems poses a high risk of scheduling conflicts or human error. This study aims to develop the User Interface (UI) and User Experience (UX) design for the Movement and Athletic Talent Hub (MATCH) application as an integrative digital solution. The approach employed is the Design Thinking method, encompassing the stages of empathize, define, ideate, prototype, and testing. The design process resulted in an interactive prototype featuring key functions such as facility booking, trainer search, and a digital payment system. Evaluation was conducted using the System Usability Scale (SUS) method involving target users. The test results yielded an average score of 79.5, categorizing the MATCH application within the Good rating and Acceptable status. These findings indicate that the design is effective in meeting user needs and is viable for further development as a digital sports ecosystem.