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Health-related SMEs in Indonesia have significant opportunities to enter international markets, but many businesses still face limitations in digital capabilities, innovation, and readiness for external markets. This study aims to examine the influence of digital transformation on the success of health-related SMEs in going international, both directly and through business innovation as a mediator. The study employs a quantitative approach using a cross-sectional survey design involving 200 owners or key managers of SMEs in East Java, selected via purposive sampling. Data were analysed using Structural Equation Modelling-Partial Least Squares (SEM-PLS). The results indicate that digital transformation has a positive influence on business innovation and the success of going international. Business innovation also has a positive effect on internationalisation success and mediates the relationship between digital transformation and external market success. These findings confirm that digitalisation should not merely be understood as an operational tool, but as a strategic capability that must be integrated with product, process, marketing, and business model innovation. The research implications emphasise the importance of strengthening digital capabilities and business innovation to enhance the global competitiveness of Indonesian health-related SMEs

This study aims to identify domestic visitor segments in Bromo Tengger Semeru National Park (TNBTS), Indonesia, based on travel characteristics and consumption patterns to support the development of quality tourism in protected areas. Using snowball sampling, 283 domestic visitors was analysed by Two-Step Cluster Analysis in SPSS by integrating length of stay, activity preferences, and expenditure patterns. The results reveal a two-cluster solution as the most optimal segmentation, supported by the highest ratio of distance measures, with cluster quality rated as fair (silhouette = 0.20). Cluster 1 (39.2%) represents short-stay, lower-spending visitors who primarily seek iconic experiences (“Sunrise Seekers”), while Cluster 2 (60.8%) reflects longer-stay, higher-spending visitors who prefer village tourism activities (“Village Experience Seekers”). The strongest differentiating variables across segments are length of stay, activity preference, expenditure style, and age, whereas gender, education level, origin, and travel companions have limited role in segment separation. This study contributes empirical evidence of data-driven visitor segmentation in a conservation-based ecotourism destination within a volcanic national park, extending prior expenditure-focused profiling by integrating length of stay and activity preferences to capture visitor heterogeneity more comprehensively.

Digital transformation in the sales function has become a critical strategic element in facing the challenges of the digital era. This study aims to explore how companies’ managerial practices in managing digital sales transformation compare to existing prescriptive frameworks. Using a survey approach of 540 sales managers from the United States, United Kingdom, Germany, and Italy, and a cluster analysis based on five key dimensions—strategic clarity, sales force replacement, sales force empowerment, implementation actions, and key performance indicators—this study identifies four typologies of companies: Digital Sales Transformation Leaders, Laggards, Enablers, and Replacers. The results show that the success of digital transformation is influenced by a combination of strategic clarity, targeted use of technology, and a focus on customer value creation. This study provides a practical contribution in the form of a taxonomy that can be used as an evaluative and strategic tool for companies in designing and implementing effective digital sales transformation.

Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .

Improving the quality of Human Resources (HR) is a major challenge in facing global competition. Education as the main means of improving the quality of HR in Indonesia is still faced with the problem of inequality of access and quality between regions. This inequality causes disparities in educational development between urban and remote areas. This study focuses on grouping provinces in Indonesia based on aspects of educational development in 2023, using cluster analysis. Secondary data from the Central Statistics Agency (BPS) is used as the basis for analysis, including variables of average length of schooling, Gross Participation Rate (APK), Pure Participation Rate (APM), number of senior high schools, and community literacy development index. This study uses hierarchical and non-hierarchical cluster analysis methods to group provinces in Indonesia. The results of the hierarchical cluster analysis using the average linkage method show the most optimal cluster with the formation of three clusters. The first cluster consists of 31 provinces, the second cluster consists of 2 provinces, and the third cluster consists of 1 province. Data characteristics show large variations in the number of senior high schools and relative homogeneity in the average length of schooling between provinces.

Poverty in Aceh Province is a significant challenge with variation between districts/cities due to differences in access to education, health, job opportunities, and infrastructure. This study aims to group districts/cities in Aceh based on poverty indicators in 2021 in order to produce a more targeted policy basis. The research data consists of 23 poverty indicators obtained from secondary sources. Cluster analysis is applied using hierarchical (average linkage) and non-hierarchical (K-Means) methods to identify poverty patterns between regions. The results of the hierarchical cluster show that there are two main groups, namely the first cluster has low poverty rates, higher education, strong purchasing power, and low unemployment, while the second cluster has the opposite characteristics. The non-hierarchical analysis (K-Means) produced five clusters with significant differences in poverty levels, labor force participation, education, and economy. These findings provide a basis for the Aceh government to design poverty alleviation policies that focus on the specific needs of each cluster to accelerate the improvement of welfare in all districts/cities in Aceh Province.

Poverty means the inability to fulfill the basic needs of family members, both food and non-food.  In this study, we will analyze several indicators that are assumed to be factors that influence poverty in East Java in 2023, including East Java in 2023, including the percentage of poor people, life expectancy, average years of schooling, and unemployment rate. life expectancy, average years of schooling, and open unemployment rate using cluster analysis to group kabupatens. cluster analysis to group districts/cities into clusters based on the factors that influence poverty. factors that influence poverty. The data used is secondary data obtained through the Central Bureau of Statistics (BPS) website as much as 38 data. Then the data obtained were analyzed for data characteristics, multivariate normal distribution assumption test, independent assumption test, and cluster analysis. assumption test, multivariate normal distribution, independent assumption test, cluster analysis hierarchical, and non-hierarchical cluster analysis, and selection of the best method to determine the optimum cluster. optimum cluster. So that the results obtained data characteristics tend not to be equal, fulfill the multivariate normal distribution assumption test, dependent data. At Hierarchical clustering results obtained the grouping of districts/cities in East Java based on the factors that influence poverty into 5 based on factors that influence poverty into 5 clusters, with 7 districts/municipalities in cluster 1, 16 districts/municipalities in cluster 2, 10 districts/municipalities in cluster 3, 4 districts/municipalities in cluster 4. districts/municipalities in cluster 3, 4 districts/municipalities in cluster 4, and 1 district/municipality in cluster 5. Based on these results, differences in characteristics between clusters indicates that there are significant variations in poverty factors in each region. The results of the non-hierarchical clustering resulted in the grouping of districts/municipalities in East Java based on the factors affecting poverty into 2 clusters, with 13 clusters. factors that influence poverty as many as 2 clusters, with 13 cluster 1, 25 districts/cities in cluster 2. Also, the results of the ANOVA test results obtained the results of all variables of the factors that influencing poverty in districts/municipalities in East Java Province significantly on poverty.

The main objectives are to identify emotion patterns hidden in K-Pop music based on audio features extracted from the Spotify API and to build an emotion classification model that can predict the emotions of K-Pop songs.In this approach, the K-Means algorithm is used to cluster K-Pop songs based on audio features such as energy, valence, tempo, danceability, and speechiness. The clustering results reveal several main groups that represent variations in musical characteristics and emotions. Next, the C4.5 algorithm was used to build an emotion classification model based on the clustering results. The C4.5 model showed high performance with accuracy reaching 99.48% on a 90:10 dataset split, 99.21% on an 80:20 split, and 98.95% on a 70:30 split.The Streamlit application was developed to visualize emotion predictions from K-Pop songs with a web-based user interface. In addition, Ngrok was used to provide remote access to this application, allowing users to test and use the application remotely.The results of this study show that the combination of K-Means and C4.5 can effectively cluster and classify emotions in K-Pop music, providing valuable insights into the musical characteristics that influence emotions. This application has the potential to be used in further analysis, development of intelligent features in music applications, and improvement of user experience in listening to K-Pop music.

This research aims to map the economic potential in the Central Kalimantan region using the integration of three analytical methods, namely Hierarchical Cluster Analysis, Shift-Share, and Dynamic Location Quotient. Through this approach, this research identifies economic sectors that have comparative and competitive advantages at the local level and have the potential for further development. Hierarchical Cluster Analysis is used to group regions based on similar economic characteristics, while Shift-Share Analysis helps in determining sectors that contribute to regional economic growth. Dynamic Location Quotient is applied to assess changes in the comparative advantage of economic sectors over time. It is hoped that the results of this research can become a reference for stakeholders in formulating appropriate regional economic policies to encourage sustainable economic growth in Central Kalimantan.

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.

Community welfare is a primary objective of national development, encompassing various aspects such as health, education, and decent employment, all of which play crucial roles in achieving national stability and progress. However, welfare is not solely dependent on economic factors but also on the overall quality of life. Unfortunately, disparities in welfare persist across different regions, influenced by local environmental factors, including access to education, which in turn affects job opportunities and income levels. Inequalities in employment opportunities can potentially slow down national development by reducing the number of individuals capable of contributing productively to key economic sectors. To enhance national development, further analysis of welfare indicators such as the open unemployment rate, human development index, labor force participation rate, and poverty levels is essential. Therefore, this study conducts cluster analysis on welfare indicators across districts/cities in Central Java for the year 2023. Both hierarchical and non-hierarchical (K-Means) clustering methods are employed to identify patterns of inequality by partitioning data into groups based on specific similarities. This approach facilitates a more effective review of policies to address welfare disparities across various regions. The findings indicate that the welfare indicators in Central Java are in a relatively poor condition, with low labor force participation rates, low human development indices, and high poverty rates. The hierarchical and non-hierarchical cluster analysis identified 5 optimal clusters, with all welfare variables having significant influence, requiring four iterations to reach the final centroids.

Warunk AIUEO is a place to eat that provides menus are usually served by food stalls. This restaurant usually crowded with young people, provides main menus such as noodles, bread, and various kinds of rice. Warunk AIUEO established since 2014, is able to increase fast food noodles but still at affordable prices. Therefore, this study aims to see what factors motivate consumers to visit this place based on the profile characteristics of consumers. This study used questionnaire as a tool for data collection, respondents are limited who live in Bandung. The number of respondents in this study amounted to 40 respondents. The variables in the questionnaire are 14 manifest variables which are processed using factor analysis to reduce the number of variables to 3 factors which represent the 14 initial variables. These three factors are reliability, assurance, and serviceability. The results of the factor analysis are then used to input cluster analysis data, which aims to group respondents based on their characteristics when deciding to visit Warunk AIUEO. Cluster analysis shows that 3 clusters are formed which are labeled modern, functional, and simple.