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The rapid development of digital technology has transformed the tourism industry through the implementation of Augmented Reality (AR), which provides more interactive, educational, and immersive tourism experiences. In Indonesia, the adoption of AR has become increasingly important to address the unequal distribution of tourist visits, which remain heavily concentrated in a few destinations, particularly Bali. This study aims to identify the clusters of AR utilization in Indonesian tourism development and analyze their potential contributions to destination promotion, cultural heritage preservation, and tourism competitiveness. The study employed a Systematic Literature Review (SLR) approach based on the PRISMA guidelines. Data were collected from reputable national and international scientific publications discussing AR implementation in tourism, cultural heritage, and travel industries between 2014 and 2025. The findings reveal that AR utilization in Indonesia can be categorized into five major clusters: the Bali Tourism Corridor Cluster, the Jakarta Metropolitan Cluster, the West Java Cluster, the East Java Cluster, and the Eastern Indonesia Cluster. Various AR implementations in destinations such as the Bongal Historical Site, Tana Toraja, Langsa City, and Lawang Sewu demonstrate the technology’s ability to enhance visitor experiences, strengthen historical and cultural education, and support destination marketing strategies. Furthermore, technology readiness and human resource competence were identified as the most influential factors affecting successful AR adoption. The findings suggest that AR has significant strategic potential to expand destination exposure, reduce disparities in tourist distribution across regions, and support the sustainable digital transformation of Indonesia’s tourism sector.

The coffee shop industry in Indonesia is experiencing rapid growth that requires business owners to optimize data-driven strategies. This study aims to analyze customer preferences at Semanis Coffee and Resto using data mining methods  to support more effective business decision-making. The method used is Market Basket Analysis with the FP-Growth algorithm for association rule mining and the K-Means algorithm for customer segmentation. The research data consists of 672 sales transactions during the March-May 2025 period. The results of the association analysis with a minimum support of 0.004 and a minimum confidence of 0.2 resulted in five valid rules with a lift ratio above 1. The strongest rule is the combination of Americano→Milk Choco with a confidence of 42.9% and an elevator ratio of 5.229, indicating a strong linkage between products. The most popular products are Milk Choco (10.8%) and Americano (8.5%). Customer segmentation analysis identified three clusters: Cluster 0 (Loyal Customers) 80% with high frequency but low transaction value; Cluster 1 (Occasional Customers) 10% with low activity; and Cluster 2 (Large Buyers) 10% with high transaction value but low frequency. This study concludes that product bundling strategies, loyalty programs, reactivation campaigns, and premium services can be applied to increase the effectiveness of coffee shop businesses.

Smart cities are trending as an innovative approach to address urban problems. This study aims to analyse the trend of research publications on smart cities in Indonesia with a bibliometric analysis approach. The articles used in this study were obtained from Scopus data. From 131 articles found in the scopus database between 2013 and 2024. The selected articles were then managed using biblioshiny and Vosviewer software. The results showed that publications related to smart cities experienced fluctuations from the last 10 years. The article with the most citations is entitled Strengthening waste recycling industry in Malang (Indonesia): Lessons from waste management in the era of Industry 4.0 has the most citations of 85 citations. While seen from the highest affiliation shows that Gadjah Mada University is an institution with a total of 70 publications. Mapping articles based on the relationship between keywords (co-occurance) is formed into 12 clusters, each cluster describes topics that are often discussed in smart city-related literature, such as urban planning, social networking, e-government, public services, urban development, sustainable development, internet of things (IoT), urban growth, economic, artificial intelligence, and secondary datum.

The creative economy industry serves as a strategic pillar of the national economy, experiencing significant transformation in the digital era. This study aims to comprehensively analyze the pattern of human resource (HR) competency gaps within priority subsectors of Indonesia's creative economy and formulate effective, multi-stakeholder development strategies. Employing a Systematic Literature Review (SLR) methodology, this research rigorously analyzes 30 scientific journal articles, government reports, and publications from global institutions published between 2014 and 2024. The findings delineate three primary clusters of competency gaps: (1) The Digital-Technical Competency Gap, encompassing deficiencies in data analytics, specialized software mastery, and digital content creation tools; (2) The Digital-Business Competency Gap, which includes shortcomings in digital financial literacy, online business model development, and management of digital intellectual property rights; and (3) The Social-Cognitive Competency Gap, highlighting needs in adaptability, complex problem-solving, and effective virtual collaboration. In response, this paper proposes an integrative strategic framework grounded in a collaborative multi-stakeholder approach. Key recommendations include revitalizing educational curricula through industry-embedded learning and micro-credential integration, developing agile and accessible training ecosystems featuring bootcamps and digital platforms, and fostering supportive policies through fiscal incentives and the alignment of national qualification frameworks with digital skill standards. The successful implementation of this synergistic strategy is expected to significantly enhance the adaptability, innovation capacity, and global competitiveness of Indonesia's creative workforce, thereby ensuring the sustainable growth of the creative economy sector in the face of rapid digital disruption.

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.

This study investigates the strategic strengthening of the rambak cracker industry as an instrument for local economic development in Penanggulan Village, Pegandon District, Kendal Regency. Rural agro-processing enterprises have increasingly been recognized as territorially embedded production units capable of generating value-added outputs and absorbing surplus labor within localized economic systems. Field-based empirical observations reveal that rambak production in the village operates through household-managed processing systems characterized by traditional production techniques, informal managerial practices, and limited digital marketing adoption despite contributing significantly to community income generation. Data obtained from expert respondents were analyzed using the Analytical Hierarchy Process to identify strategic priority determinants influencing industrial competitiveness and sustainability. The results indicate that product innovation and quality improvement constitute the primary strategic priority, followed by digital marketing development and institutional partnership strengthening, while production capacity expansion remains comparatively less influential in enhancing market competitiveness. These findings suggest that adaptive innovation and digitally enabled commercialization pathways function as critical mechanisms for improving value-chain integration and expanding market accessibility among rural food-processing industries. Strengthening innovation ecosystems within the rambak sector may therefore contribute to employment creation, income diversification, and sustainable community-based economic transformation in rural production clusters.

The rapid development of digitalization and innovation has become a key driver in improving business processes and the competitiveness of organizations worldwide. This study is the first comprehensive bibliometric analysis examining the relationship between digitalization and innovation in business processes, to map the intellectual structure of this field, track the development of its themes, and identify remaining research gaps. This analysis, which utilizes data from Scopus processed using VOSviewer and Biblioshiny software, covers publications from 2010 to 2024 and employs co-occurrence, co-authorship, and thematic evolution techniques. The results show a rapid growth in publications since 2016, peaking at over 110 publications in 2024. Eight key thematic clusters stand out: Industry 4.0, artificial intelligence, robotic process automation, blockchain, drivers, and agile business process management. Despite the field's maturity, it still suffers from high fragmentation, strong geographic concentration, and a reliance on cross-sectoral research designs. As a result, longitudinal insights remain limited, and digital transformation failure rates remain high, reaching up to 70%. This research presents the first quantitative and visual roadmap of global knowledge flows in this domain and underscores the need for longitudinal, geographically inclusive, and people-centric research to move beyond single-point understandings to a sustainable, context-sensitive framework that enhances both the theoretical depth and practical success of digital-based business process innovation

The halal cosmetics industry in Indonesia has grown rapidly in recent years in line with the increasing awareness of Muslim consumers regarding product safety, cleanliness, and compliance with Islamic law. However, this industry still faces several challenges, particularly related to the availability of halal-certified raw materials, production process standardization, and coordination among key stakeholders. This study aims to analyze the synergy between the government, business actors, and halal certification institutions in the development of the halal cosmetics cluster in Indonesia. Using a qualitative approach and case study design, this research draws on literature analysis and applies the concepts of halal industry clusters and the triple helix model. The results indicate that collaboration among cosmetic manufacturers, government institutions, and certification bodies such as BPOM and LPPOM-MUI has strengthened consumer trust and legal assurance regarding halal products. Nevertheless, barriers remain, including limited knowledge among producers about halal standards and uneven support infrastructures across regions. Therefore, strengthening policy integration, capacity building for industry players, and institutional support is necessary to enhance the competitiveness and sustainability of the halal cosmetics industry in accordance with the principles of Maqashid Sharia.

This study maps the regulatory landscape of financial technology (fintech), focusing on cryptocurrency regulation at both global and Indonesian levels. Cryptocurrency, one of the fastest-growing fintech instruments, functions as a virtual currency secured by cryptography. Despite lacking physical form, it is widely used for investment, transactions, and speculation, with trust supported by blockchain’s transparency and immutability. However, regulatory frameworks remain fragmented across countries. The research applies a bibliometric approach, using Bibliometrix (R Studio) for descriptive analysis and VosViewer for keyword network visualization. Data were retrieved from Scopus with the keywords “cryptocurrency regulation” and “fintech regulation,” covering 2016–2025. Findings reveal 1,178 documents from 484 sources, contributed by 4,693 authors, with an average of 7.43 authors per document and an international collaboration rate of 24.79%. The annual growth rate reaches 44.31%, with an average of 14.01 citations per document. Keyword analysis identifies four main clusters: financial regulation, green finance and sustainability, decentralized finance (DeFi), and blockchain cybersecurity. This study provides a knowledge map of regulatory evolution from conventional finance to blockchain-based fintech, offering insights for academics, regulators, and industry to balance innovation, consumer protection, and financial stability.

Cilegon City, located in Banten Province, is recognized as a vital industrial hub contributing to Indonesia's economic growth. However, the city's investment competitiveness still requires substantial enhancement to attract both domestic and foreign investors. This study focuses on developing a strategy to strengthen the investment competitiveness of Cilegon City, primarily through optimizing its industrial sectors and innovating public services. The research adopts a descriptive qualitative approach, using literature reviews, interviews, and observations of local policies and conditions to gather data. The findings suggest that improving investment competitiveness can be achieved through the development of industrial clusters, providing necessary supporting infrastructure, and enhancing public service efficiency, particularly through digitalization and innovations in the licensing process. Additionally, fostering collaboration between local governments, businesses, and the community is essential for creating a competitive and sustainable investment environment. Public service innovation, especially in streamlining the licensing process, increases transparency and builds investor confidence. The study concludes that with an integrated strategy, Cilegon City can transform into a top industrial investment destination with the potential to compete effectively on both national and international levels. This research highlights the importance of strategic planning, innovation, and collaborative efforts in positioning Cilegon as a globally competitive industrial center, enhancing its attractiveness to investors and contributing to economic development.

Crime is a social issue that continues to evolve alongside increasing community activity and regional development. This study aims to Cluster crime data in Binjai City based on the location of incidents using the K-Means algorithm and the Cross Industry Standard Process for Data Mining (CRISP-DM) approach. The data were obtained from the Binjai Police Department, with attributes including the type of crime, time of occurrence, and location, categorized by district. A comprehensive data preprocessing stage was carried out, involving the extraction of information from raw data, normalization of crime type labels, and conversion of categorical data into numerical form using label encoding. The optimal number of Clusters was determined using the Silhouette score method, which yielded the best result at K = 10. The Clustering results were further evaluated using the Davies-Bouldin Index (DBI) to ensure Cluster quality. The analysis revealed that Binjai Utara District has the highest number of crimes, particularly aggravated theft (curat), which frequently occurs from early morning to late morning. This Clustering is expected to provide valuable insights for authorities in formulating more targeted and data-driven regional security strategies.

This study aims to map trends, patterns, and scientific developments related to sharia compliance in Islamic financial institutions through a bibliometric approach and network visualization using VOSviewer software. The data analyzed amounted to 409 scientific journal articles obtained from international databases during the 2019-2024 period. The analysis was conducted using quantitative methods for bibliometric data mapping and qualitative methods for literature review. The results showed a significant increase in the number of publications discussing sharia compliance, with five main clusters of interrelated topics, such as implementation, sharia governance, financial performance, the role of the Sharia Supervisory Board, and sharia principles. The findings show that compliance with sharia principles is an important aspect in maintaining the credibility and sustainability of Islamic financial institutions, although its implementation still faces various challenges, such as limited human resources and competitive pressures with conventional institutions. This study is expected to serve as a basis for further research and strategic input for regulators and industry players in strengthening the Islamic financial system.

This research aims to apply the K-Means Clustering method in grouping consumer interests regarding the use of services at the Binjai Post Office. The Post Office is part of a state-owned enterprise in North Sumatra Province with the main task of providing postal and logistics services. Postal services remain one of the most important means of communication, especially for sending packages, letters, and documents. However, with various services and diverse consumer needs, post offices can provide more effective and relevant services. The K-Means Clustering method is a classification technique based on machine learning algorithms used to identify patterns present in consumer interest data. The data used in this research includes various related variables, namely the type of delivery, total cost, and delivery time. The results of the clustering process conducted using 3 clusters indicate that there is a grouping of consumer data based on preferences for using delivery services. In group 1, there are (21 data points) with a centroid at coordinates (C1) 2; 4.3810; 3.5238. In group 2, there are (124 data points) with a centroid at coordinates (C2) 3; 2.0565; 3.1452. In group 3, there are (387 data points) with a centroid at coordinates (C3) 3.6925; 1.1370; 1.7209. This research shows that the application of K-Means Clustering can enhance the understanding of consumer interests and assist in the development of more targeted strategies to optimally meet needs.

This research examines the mapping of creative culinary industry clusters typical of Central Kalimantan in Palangka Raya City using Hierarchical Cluster Analysis. The main objective of this research is to identify clustering patterns of the local creative culinary industry which can be the basis for formulating local culinary optimization policies. By applying Hierarchical Cluster Analysis, this research succeeded in uncovering the characteristics and relationships between creative culinary industry players in Palangka Raya, as well as development potential that can be improved through appropriate policies. It is hoped that the results of this research can provide strategic recommendations for local governments and other stakeholders in designing and implementing policies that support the growth of the creative culinary industry, while promoting the richness of Central Kalimantan's unique culinary delights.

Rapid technological developments encourage the banking sector to continue to innovate so as not to be left behind. Tight competition in this industry is caused by customers' freedom to choose products and services that are considered more profitable. This phenomenon is known as Customer Churn, which is a condition where customers choose not to continue subscribing to a particular company. The method applied uses a machine learning approach and customer segmentation approach. The churn analysis results show that the machine learning model, especially the random forest model, has the highest level of accuracy with an F1-Score of 91%. This model has the potential to reduce churn rates from 20.4% to 5.61%, illustrating its positive impact. Apart from that, for the clustering results, the K-Prototype model was obtained for the clustering model with the highest Silhouette Score number of 0.1557 and 4 clusters were obtained.    

This study discusses the analysis of the power structure of leading industry clusters in Sidoarjo Regency. Sidoarjo Regency is one of the districts in East Java Province that has the potential and success in developing the local economy through the formation of industrial clusters. This study uses descriptive qualitative research methods by collecting secondary data through literature analysis. The results of the analysis show that factors such as superior natural resources, infrastructure availability, labor skills, research and innovation facilities, and government support policies affect the successful formation of leading industrial clusters in Sidoarjo Regency. Industrial clusters increase productivity, efficiency, innovation and commercialization. This study provides an understanding of the characteristics and dynamics of leading industry clusters in Sidoarjo Regency and the factors that influence efficiency. The results of this study can be the basis for further development to optimize the economic potential of the region with the help of industrial clusters.

Industry clusters, which consist of a group of companies related geographically and sectorally, have become the focus of attention in regional economic and business strategy studies. This study uses an industrial structure analysis approach to understand the strength of competition within an industry cluster. Some of the factors analyzed include the level of industry concentration, new entries, bargaining power of buyers and suppliers, as well as product or service substitution. The data used in this study includes information about the companies that are members of the industrial cluster studied. The research method used includes secondary data collection, such as company financial reports, industry data, and related publications. In addition, interviews with industry stakeholders were also conducted to gain deeper insights into the structure of competitive forces and the factors that influence industrial clusters. The results of the analysis show that the structure of competitive forces in industry clusters can vary depending on the industrial sector studied. This research contributes to our understanding of how the structure of competitive forces and industry clusters can shape a competitive business environment, as well as providing useful insights for the development of effective business strategies within industry clusters.    

The garment industry faces challenges in grouping diverse goods based on their characteristics, which can affect the efficiency of the production process and inventory management. This study aims to apply the K-Means Clustering algorithm in garment goods classification to improve business process management and optimization. The K-Means algorithm, as one of the popular clustering methods, is used to group garment goods data based on features such as size, color, fabric type, and product model. This method begins with the selection of relevant features from the dataset obtained from the garment industry. Furthermore, the K-Means algorithm is implemented to determine the optimal number of clusters using the elbow score and silhouette methods. The clustering results are analyzed to evaluate the extent to which the algorithm can form homogeneous and business-relevant groups of goods. The results of this study indicate that the K-Means Clustering algorithm is effective in grouping garment goods into several categories that are consistent with business patterns and needs. The application of this method results in a better understanding of goods grouping that can improve production efficiency and facilitate inventory management. This study contributes to the best practices in the use of the K-Means algorithm in the convection sector and shows the potential of this method in supporting data-driven decision making.   Keywords:,