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Regional development faces dynamic challenges amid rapid economic growth driven by natural resource extraction. This study aims to identify leading economic sectors, analyze structural economic transformation, and evaluate the role of these sectors in regional development. The research employs a quantitative method with a descriptive approach. Secondary data consist of Gross Regional Domestic Product (GRDP) at constant prices over the past five years. The analytical techniques applied include Location Quotient analysis to identify base sectors, Shift-Share analysis to assess structural changes as well as comparative and competitive advantages, and Klassen Typology to classify sectoral growth patterns. The results reveal a structural shift from primary sectors, such as agriculture and fisheries, toward secondary sectors, including mining and manufacturing. Despite challenges related to development equity, these leading sectors serve as key drivers of regional economic growth. To maximize the contribution of leading sectors to broader regional development, this study recommends that government policies prioritize the strengthening of intersectoral linkages.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

This study analyzes the practice of tender rigging, which is a dominant form of irregularity in government procurement of goods and services. The study focuses on identifying and analyzing the modus operandi, which includes the use of borrowing flags, document manipulation, leaking of the Self-Estimated Price (HPS), and price agreements between participants (tender arisan). The research findings reveal that the collusion occurs in two patterns: vertical between providers and the committee, and horizontal between providers. Both patterns fulfill the elements of unlawfulness as stipulated in Articles 2 and 3 of the Corruption Eradication Law. This practice of tender rigging causes significant state financial losses and undermines fair business competition. Therefore, the effectiveness of law enforcement depends heavily on accurate proof of state losses and good coordination between law enforcement officials and the Business Competition Supervisory Commission to ensure fair resolution in accordance with applicable legal provisions.

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

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.  

This study developed the Weton-Based Leadership Model as a leadership framework that integrates Javanese cultural values from the weton system with modern leadership theories, such as transformational, servant, charismatic, and situational leadership. Using a postmodern paradigm with an exploratory qualitative approach, this study utilizes pattern matching and explanation building methods through in-depth interviews with cultural experts and human resource management practitioners, as well as analysis of Javanese cultural documents. The results of the study identify five key components in the model, namely self-awareness, value alignment, situational adaptability, team harmony, and risk governance. These five components interact with each other to form contextual leadership that is in harmony with personal identity, organizational culture, and environmental demands. The practical implications of this study include the use of weton as a reflective instrument in recruitment, personalized leadership development, and the strengthening of an inclusive organizational culture. Further research is recommended to test this model in various industrial contexts through quantitative methods and longitudinal approaches.

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.

The rapid advancement of digital technologies and Artificial Intelligence (AI) has fundamentally reshaped the management and development of the tourism industry. Digital transformation strategies offer substantial opportunities to enhance destination competitiveness while simultaneously supporting economic, social, and environmental sustainability. This study aims to systematically examine the role of digital transformation and AI in strengthening sustainable tourism competitiveness through a literature review approach. A total of 42 peer-reviewed journal articles published between 2019 and 2025 were analyzed, drawing from Scopus, Web of Science, and Google Scholar. The analysis employed thematic synthesis to identify dominant patterns, conceptual relationships, and emerging themes across the literature. The findings indicate that AI-driven digital transformation enhances operational efficiency, enables personalized tourist experiences, supports data-informed resource management, and facilitates the development of smart tourism destinations. Nevertheless, persistent challenges related to human resource readiness, digital inequality, data governance, and ethical considerations remain evident. This review provides an integrated conceptual perspective on digital transformation and AI in sustainable tourism competitiveness and offers insights for policymakers, practitioners, and future research.

Contemporary church ministry is often trapped in a seasonal pattern, with increased activity only during major events like Christmas and Easter, while stagnating at other times. This phenomenon indicates a fundamental problem in leadership patterns that are not poverty-oriented. This article analyzes the roots of seasonal church ministry through a qualitative-descriptive approach and theological reflection based on Osmer's framework. Literature reviews by Barna, Maxwell, Banks & Ledbetter, as well as the leadership principles of Jesus Christ, show that seasonality is eliminated by reactive leadership that lacks a long-term vision, minimal spiritual training, and a weak ministerial cadre system. As a solution, this article offers a sustainable leadership model that emphasizes a year-round discipleship vision, ministerial training and regeneration, systematic ministry evaluation, and the spiritual example of leaders. This model is believed to help churches experience stable growth, establish a consistent ministry rhythm, and present relevant evidence to the world. Thus, sustainable leadership is a strategic and theological approach to addressing seasonal ministry and strengthening the mission of the church today.

Tuberculosis (TB) is one of the infectious diseases that remains a public health problem in Indonesia, including in West Java Province, which has a large population and high mobility. This condition has the potential to increase the risk of transmission and cause variations in the distribution of cases between districts and cities. This study aims to map the distribution of TB cases across all districts and cities in West Java Province from 2022 to 2024 using a spatial analysis approach. This analysis was conducted to describe the geographical distribution of cases, identify patterns of spread, and determine areas with relatively high or low case rates. TB data was obtained from routine recording and reporting by health facilities in West Java, then integrated with population and administrative boundary data. The results of the analysis provide information on case distribution patterns between regions and trends in case changes from year to year. The findings of this study are expected to serve as a basis for local governments in formulating more targeted TB prevention and control strategies, through a focus on interventions in areas with a high case burden, as well as optimizing sustainable public health programs in West Java Province.  

This study examines the effectiveness of interactive digital platforms in improving student academic outcomes. The integration of digital platforms in education is becoming increasingly important, but challenges arise regarding educators' adaptation of technology. This study analyzes the effectiveness of platforms such as Kahoot and Padlet through a systematic literature review. The study identifies gaps in previous research that focused more on platform features than on learning outcomes and educator adaptation. Using a systematic literature review of 45 peer-reviewed articles (2019-2024), this study explores the relationship between platform interactivity, educator technology proficiency, and measurable academic improvement. Data analysis uses thematic coding to identify patterns in successful digital learning implementation. The results show that interactive platforms can improve academic performance by 15-25% if educators have adequate technological skills, but success rates decline without proper training. This study proposes a comprehensive framework that integrates platform effectiveness metrics with educator development strategies. Recommendations include structured technology training programs and standardized assessment protocols to measure the effectiveness of digital learning.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

Measurement learning is a fundamental and applicable mathematical topic in everyday life, but it often causes learning difficulties for students, especially in understanding the meaning of units, relationships between quantities, and the conceptual measurement process. These difficulties are not only caused by students' limited cognitive abilities, but also by learning designs that do not fully facilitate social interaction and student learning development. This study aims to reconstruct the contextual didactic design in measurement learning by reviewing the role of social interaction and the Zone of Proximal Development (ZPD) in the student learning process. This study uses a qualitative approach with the type of Didactic Design Research (DDR). The research stages include analysis of the initial didactic situation to identify student learning barriers, implementation of contextual didactic design in measurement learning, and retrospective analysis of student responses as a basis for reconstructing the didactic design. Data were collected through learning observations, analysis of student work results, interviews, and learning documentation. Data analysis was carried out qualitatively by examining social interaction patterns, forms of scaffolding, and student movements in the Zone of Proximal Development. The results of this study indicate that understanding of measurement concepts develops through social interactions between students and between students and teachers within a meaningful learning context. Social interactions and scaffolding provided gradually can encourage students to move from actual abilities to potential abilities within the Zone of Proximal Development. Retrospective analysis indicates that reconstruction of the didactic design is necessary to refine the learning context, activity sequence, and scaffolding strategies to better align with students' learning characteristics. The reconstruction of the didactic design can reduce learning barriers and improve the quality of students' conceptual understanding in measurement learning. This research provides theoretical contributions to the study of social constructivism-based mathematics education and provides practical implications for teachers in designing measurement learning that is more responsive to social interactions and student learning development.

Heritage language loss among immigrant and diaspora communities represents a critical challenge to cultural identity, intergenerational communication, and cognitive diversity. Traditional heritage language maintenance approaches face significant barriers including limited resources, lack of qualified instructors, geographic dispersion, and competing demands of dominant language acquisition. This study investigates the effectiveness of bilingual digital story applications as innovative tools for heritage language maintenance among children aged 4-12 years. Through a 24-month longitudinal mixed-methods study involving 1,843 families across eight language communities (Spanish, Mandarin, Arabic, Korean, Tagalog, Vietnamese, Hindi, and Polish), we examined language proficiency development, cultural identity formation, family engagement patterns, and app usage behaviors. Quantitative analysis of pre- and post-intervention language assessments revealed statistically significant improvements in heritage language vocabulary (effect size d=0.68), listening comprehension (d=0.54), and oral production (d=0.47) among children using bilingual story apps for at least 20 minutes daily. Qualitative findings from parent interviews and child focus groups highlighted the apps' role in making heritage language learning enjoyable, facilitating parent-child interaction, connecting children to cultural narratives, and normalizing bilingualism. However, effectiveness varied substantially based on app design features, with interactive elements, culturally authentic content, parent involvement scaffolds, and adaptive difficulty showing strongest associations with outcomes. This research demonstrates that thoughtfully designed bilingual story apps can serve as accessible, scalable tools for heritage language maintenance, though they function most effectively as complements to rather than substitutes for rich home language environments and community connections. The study contributes empirical evidence to inform app development, family language planning, and policies supporting linguistic diversity in increasingly globalized societies.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

Rapid adoption of cashless payments has reshaped everyday spending, especially among young consumers. While e-wallets deliver speed and convenience, constant exposure to discounts, cashbacks, and one-click checkouts may also foster more frequent discretionary purchases. This study examines the relationship between e-wallet usage intensity and consumptive lifestyle, focusing on how convenience and promotional stimuli relate to spending tendencies. Using a cross-sectional online survey of students and early-career workers, we collected self-reports on payment habits and consumption patterns with validated Likert-type instruments. Data were screened and analyzed with correlation and linear regression after basic assumption checks. The results indicate a positive and statistically meaningful association between e-wallet usage and consumptive lifestyle; respondents who transact more often via e-wallets tend to report stronger preferences for instant gratification, hedonic purchases, and impulse buying. Convenience features (e.g., stored cards, fast checkout) and promotional exposure (e.g., limited-time deals) emerged as salient correlates of the relationship. The findings add contextual evidence from Indonesia’s digital economy and suggest practical implications for users, platforms, and educators. Financial-wellbeing interventions such as digital budgeting tips, in-app nudges, spend limits, or post-purchase reflections may help align seamless payments with healthier consumption decisions. Future work can test causal mechanisms and evaluate design features that encourage prudent, goal-consistent spending without diminishing user experience.

Agrarian conflicts in Lampung illustrate that land disputes are not only about ownership but also about how language shapes public perceptions of power and justice. This study aims to explain the changing patterns of media reporting on agrarian conflicts and the social meanings emerging from these changes. The research uses a descriptive qualitative approach with Fairclough’s critical discourse analysis and Lederach’s conflict transformation theory. Data were obtained from online news reports on agrarian conflicts in Lampung published between 2010 and 2025. The results show that earlier reports emphasized control and security, while later coverage shifted toward issues of justice, land rights, and community recognition. This change indicates that language in media reporting plays a crucial role in transforming public perspectives from a logic of authority toward a consciousness of justice and humanity. The study highlights the importance of fair and empathetic public communication as a foundation for peaceful and sustainable conflict resolution.

This study aims to describe the forms and social functions of code-switching and code-mixing in the film Hujan Bulan Juni and to explain how language choice represents identity and social differences among the characters. The film was chosen because it portrays interactions between characters from different cultural backgrounds Javanese and Manadonese which allows for complex patterns of language contact. The study employed a descriptive qualitative method using observation and note-taking techniques on dialogues containing Indonesian, Javanese, and Manadonese linguistic elements. The data were analyzed using the sociolinguistic frameworks of Gumperz (1982) and Wardhaugh (2015) to uncover the social functions and identity meanings reflected in the speakers’ language choices.The findings reveal that code-switching from Indonesian to Manadonese serves to reinforce ethnic solidarity, emotional closeness, and cultural pride, while switching from Indonesian to Javanese reflects politeness, intimacy, and social hierarchy. Meanwhile, code-mixing between Indonesian and Manadonese marks distinctive speech styles and expresses regional identity, whereas code-mixing between Indonesian and Javanese functions to soften utterances and portray the speakers’ courteous demeanor. Overall, language choice in Hujan Bulan Juni is not merely a communicative tool but also a representation of identity and social differentiation within Indonesia’s multilingual society.

Climate risk finance has emerged as an increasingly important field of research along with the growing urgency to address climate change and its impacts on the global financial system. Climate change poses real risks to the stability of the international economy and financial systems. Climate risk finance represents an approach that encompasses various financial instruments in supporting climate change mitigation and adaptation. Although the term climate risk finance has not been widely used explicitly as a single keyword, the concept that integrates climate risk and financing is reflected in related keywords such as climate risk, climate finance, and climate change. This study employs a bibliometric analysis method using the Scopus database, supported by analytical tools such as VOSviewer and R Studio, to explore the development of research on climate risk finance. The study identifies publication patterns, international collaborations, and emerging themes within the related literature. The findings show that the publication rate on climate risk finance is relatively moderate each year, but has experienced growth in the last decade. The evolving understanding in this field is expected to strengthen the resilience of financial systems and support sustainable strategies to address long-term climate risks.