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This literature review synthesizes findings from fifteen peer‑reviewed studies published between 2020 and 2026 to examine the determinants of sustainable competitive advantage in the furniture industry. The analysis reveals that green innovation, environmental performance, and market orientation interact as core drivers of competitiveness, supported by theoretical perspectives including the Resource‑Based View, Natural Resource‑Based View, Institutional Theory, Circular Economy, and Strategic Management frameworks. The review highlights how internal capabilities, regulatory compliance, and responsiveness to consumer preferences collectively shape the ability of micro, small, and medium enterprises to adapt to sustainability demands in global markets. Evidence shows that eco‑innovation practices, waste recovery strategies, and clustering models enhance efficiency and legitimacy, while indicators of green growth and brand performance provide practical tools for evaluating sustainability outcomes. The synthesis underscores that competitive advantage in this sector is not determined by isolated variables but by systemic integration across resources, operations, and market dynamics. This study contributes theoretically by consolidating fragmented insights into a coherent conceptual model and practically by offering guidance for enterprises and policymakers to foster green transformation. The findings emphasize the urgency of aligning industrial practices with ecological integrity and suggest that future research should examine cross‑country variations, longitudinal impacts, and the integration of digital technologies with sustainability strategies to strengthen the resilience of furniture enterprises in the global economy.

Flooding remains a recurrent hazard in the Special Capital Region of Jakarta (DKI Jakarta), causing substantial disruptions across multiple dimensions community life, including social well-being and economic activities. The availability of flood-related information through the Jakarta One Data Portal (Satu Data Jakarta) provides significant opportunities for broader data utilization; however, transforming such information into meaningful assessments regional vulnerability requires a systematic analytical approach to generate more comprehensive understanding of the conditions of individual urban villages (kelurahan). This study focuses on the classification areas based on the characteristics and impacts of flood events occurring throughout DKI Jakarta. The analysis utilizes flood event records from 2023 to 2025 and incorporates several indicators, including the number of flood occurrences, the number of affected neighborhood associations (RW), the number affected households, the number of affected residents, the number of evacuees, the number of evacuation sites, and floodwater depth. Data processing was conducted using the Knowledge Discovery in Databases (KDD) framework, encompassing data selection, cleaning and preprocessing, transformation, pattern exploration, result evaluation, and knowledge extraction. The findings demonstrate that three-cluster solution effectively captures variations in flood vulnerability levels, corresponding to low-, moderate-, and high-risk categories. A total of 96 urban villages were classified as low-risk, 27 as moderate-risk, and 46 as high-risk areas. The resulting clustering patterns provide a clearer spatial representation of flood-risk distribution across urban villages, thereby offering valuable insights for the development more targeted mitigation strategies, the prioritization of flood management interventions, and the enhancement of evidence-based decision-making processes in DKI Jakarta.

 Education in vocational high schools (SMKs) requires effective data management to improve students’ academic achievement and discipline. At SMK Islam Secang, students’ academic scores and attendance data have so far functioned merely as administrative archives, making it difficult to identify patterns of student performance. This study aims to classify students based on academic achievement and discipline by applying the K-Means Clustering algorithm using RapidMiner. The data used in this study consist of scores from six subjects and attendance records of 35 students from the Light Vehicle Engineering (TKR) department over two semesters. The data were obtained from original school records, compiled using Microsoft Excel, and processed in RapidMiner. The clustering process employed four clusters for academic achievement and two clusters for discipline, with Euclidean Distance used as the similarity measure. The results show that in the first semester, students were grouped into four academic achievement clusters: high achievement (6 students), moderate achievement (7 students), potentially problematic (14 students), and problematic (8 students). In the second semester, the distribution changed to high achievement (19 students), moderate achievement (14 students), potentially problematic (4 students), and problematic (1 student). Meanwhile, student discipline was divided into two clusters: disciplined (31 students) and undisciplined (4 students). These results demonstrate that K-Means Clustering is effective in mapping student conditions, revealing patterns in academic performance and attendance, and supporting educational evaluation, learning planning, and early detection of students who require academic or disciplinary intervention. Keywords: Data Mining, K-Means Clustering, Academic Achievement, Discipline, RapidMiner, Vocational High School (SMK)

The development of information technology and the internet has increased the use of online marketplaces as sources of transaction and information, including in the used vehicle sector. OLX provides vehicle information such as price, brand, year, fuel type, mileage, and seller location, but the data is presented individually, making overall market analysis difficult. This study aims to design a used car market analysis dashboard based on OLX advertisement data in the Greater Jakarta area using a Business Intelligence (BI) approach. Data were collected through web scraping and processed through data cleaning to improve quality. Market segmentation was conducted using the K-Means Clustering method based on vehicle price, year, and mileage. A Star Schema analytical database was implemented to support data management and visualization. User information needs were identified through questionnaires to determine Measures and Key Performance Indicators (KPIs). The dashboard was developed using Microsoft Power BI and features interactive visualizations, including KPI Cards, Bar Charts, Line Charts, and Pie Charts. Results show that the dashboard presents market information in a structured and informative manner, including vehicle counts, average prices, market trends, brand and location distributions, and segmentation results. Black Box Testing confirmed that all dashboard functions operated as expected, supporting faster and more effective data-driven decision making.

Kedisiplinan kehadiran siswa merupakan aspek penting dalam proses pembelajaran karena mencerminkan partisipasi, tanggung jawab, dan kepatuhan terhadap aturan sekolah. Namun, data absensi sering kali hanya diperlakukan sebagai catatan administratif dan jarang dianalisis untuk mengidentifikasi pola keterlambatan yang berulang. Penelitian ini bertujuan untuk mengelompokkan pola keterlambatan siswa berdasarkan data absensi harian menggunakan algoritma K-Means guna memberikan gambaran yang lebih terstruktur dan objektif tentang disiplin kehadiran. Penelitian ini menggunakan pendekatan data mining yang meliputi pengumpulan data, preprocessing, transformasi data, normalisasi, dan clustering. Dataset yang digunakan terdiri atas 678 catatan absensi harian selama 47 minggu. Setelah direkapitulasi, data menghasilkan 54 kategori durasi keterlambatan dengan total 1.738 kejadian keterlambatan. Variabel utama yang dianalisis adalah frekuensi kejadian keterlambatan pada setiap kategori, dan StandardScaler digunakan sebelum proses clustering. Data kemudian dikelompokkan ke dalam tiga cluster, yaitu High, Medium, dan Low. Hasil penelitian menunjukkan bahwa keterlambatan singkat berdurasi 1-7 menit membentuk cluster High dengan total 1.007 kejadian, yang menunjukkan bahwa keterlambatan ringan yang berulang merupakan pola paling dominan. Cluster Medium mencakup keterlambatan 8-13 menit dengan 425 kejadian, sedangkan cluster Low mencakup keterlambatan 14-63 menit dengan 306 kejadian. Penelitian ini memberikan kontribusi berupa sudut pandang berbasis clustering dalam menafsirkan data absensi dan berimplikasi bahwa sekolah perlu memberi perhatian tidak hanya pada keterlambatan berat, tetapi juga pada keterlambatan singkat yang sering berulang melalui pemantauan lebih dini dan intervensi disiplin yang lebih tepat sasaran.

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.

Accurate classification of rainfall intensity patterns is important for early warning systems, hydrometeorological risk assessment, and water resource management. Surface rain gauges have limited spatial coverage, so this study uses NOAA NEXRAD Level II radar data from the KTLX station in 2023. K-Means clustering was applied to identify rainfall intensity patterns from 30 randomly selected days, with scans stratified into four daily time intervals. Seven features were extracted from each radar sweep, including reflectivity statistics, convective and stratiform ratios, and rainfall coverage. The data were normalized and balanced before clustering. The optimal cluster count was determined through a combined evaluation of the Elbow Method, Silhouette Score, and Davies-Bouldin Index, yielding K=5 as the most representative configuration. Evaluation results demonstrated a Silhouette Score of 0.3871 and a Davies-Bouldin Index of 0.8599, indicating moderate cluster cohesion that reflects the inherent overlapping nature of rainfall intensity transitions in radar reflectivity data. The clusters represent rainfall regimes from non-precipitating conditions to intense convective events. These results support the use of K-Means for automated rainfall pattern recognition and flood forecasting applications. 

This study aims to examine user opinion tendencies toward Gojek services by integrating Random Forest and K-Means Clustering approaches. The dataset consists of 15,000 user reviews collected throughout 2025 using web scraping techniques. The initial stage focuses on data preprocessing, including text cleaning, case normalization, tokenization, removal of non-informative stop words, and lemmatization to restore words to their base forms. Subsequently, sentiment labels are assigned using a lexicon-based approach. The next phase involves classification modeling through Random Forest to identify sentiment tendencies, while K-Means Clustering is employed to uncover latent patterns within the opinion data. The findings indicate that the Random Forest model achieves an accuracy level of 0.878, demonstrating strong performance in distinguishing positive and negative sentiments, as reflected by f1-scores of 0.932 and 0.818, respectively. However, the model shows limitations in consistently identifying neutral sentiment. In contrast, the implementation of K-Means Clustering successfully categorizes the data into three primary clusters, providing a more structured representation of user opinion characteristics. Overall, these results offer empirical insights that can serve as a strategic reference for enhancing the quality of Gojek’s service delivery.

This study aims to explore the meaning of service excellence from the perspective of hotel employees at Kinasih Resort Depok, Indonesia. Most existing research on service excellence has focused on guest satisfaction, leaving the subjective experiences of employees as service providers underexplored. This study employs a qualitative approach with a descriptive phenomenological design. Data were collected through in-depth interviews, participatory observation, and documentation from five purposively selected informants across the F&B Service, Housekeeping, Front Office, Human Resources, and management departments. Data analysis followed Moustakas's phenomenological procedure encompassing epoché, horizonalization, theme clustering, and essence description. Findings reveal that employees construct layered meanings of service excellence according to their hierarchical positions: frontline workers emphasize friendliness and responsiveness, supervisors emphasize speed and problem resolution, while management frames it as a holistic service ethos encompassing internal relationships. Emotional labor emerged as an inevitable dimension managed through collaboration, prioritization, and de-escalation strategies. Organizational factors including training systems, communicative leadership, guest feedback-based evaluation, and managerial attention to employee well-being demonstrably shape how employees internalize service excellence values. This study contributes to employee-centered literature on service excellence and offers practical implications for human resource development in resort contexts.

Stock market volatility represents a key indicator of financial market uncertainty, particularly in emerging economies where market structures are still evolving and are highly sensitive to global shocks. This study aims to analyze and compare the volatility dynamics of stock markets in four Asian emerging economies: Indonesia, India, Malaysia, and Thailand. The research employs a quantitative approach using daily stock index data from January 2011 to January 2026 obtained from Yahoo Finance. Stock returns are calculated using logarithmic transformation and analyzed using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH(1,1)) model. Prior to model estimation, stationarity and ARCH effect tests are conducted to ensure the validity of volatility modeling. The empirical findings indicate that all return series exhibit non-normal distribution, strong volatility clustering, and significant ARCH effects. The estimation results show that both ARCH and GARCH parameters are statistically significant, with persistence levels close to unity across all markets, implying that volatility shocks tend to persist over a long period. These findings suggest that emerging stock markets in Asia are highly sensitive to external shocks and exhibit long-memory volatility behavior. The results provide important implications for investors and policymakers in designing effective risk management and market stabilization strategies.

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.

Dengue fever is a disease commonly found in tropical and subtropical regions. This disease can cause severe symptoms, such as very high fever, accompanied by nausea, vomiting, headache, abdominal pain, and leukopenia (decrease in white blood cells). This infectious disease, known as dengue hemorrhagic fever (DHF), is a viral infection transmitted by the Aedes Aegyppti mosquito. This study aims to classify dengue-prone areas using the K-Means Algorithm, and to classify the factors that cause dengue in Purwodadi District, Grobogan Regency. The clustering results using the K-Means algorithm with Rapidminer tool from 266 data produced 3 clusters: cluster 0 (blue) with 138 patients dominated by Kuripan, Purwodadi, Ngambak villages, cluster 1 (green) with 31 patients in Ngraji, Nambuhan, Cingkrong villages, and cluster 2 (orange) with 97 patients in Danyang, Kalongan, Pulorejo villages. This study is expected to provide additional information for stakeholders in controlling dengue cases and increase awareness of the importance of environmental cleanliness as a preventive measure.

Background: Malaria transmission in Indonesia exhibits substantial spatial and temporal heterogeneity, particularly between stable endemic areas and remote outbreak-prone areas. Although routine surveillance is crucial for malaria control and elimination efforts, its limitations can obscure early warning signals, particularly in geographically isolated areas. Objective: This study aimed to analyze malaria epidemiology by integrating routine surveillance data from endemic primary health care settings with results from high-mortality outbreak investigations in remote highland districts in Indonesia, focusing on temporal trends, spatial clustering, Plasmodium species patterns, diagnostic gaps, and mortality. Methods: A mixed epidemiology approach was used. A retrospective longitudinal analysis of routine malaria surveillance data was conducted for the period 2023–2025 in endemic settings, while a cross-sectoral outbreak investigation was conducted in remote highland districts. Descriptive analyses were conducted to assess trends, demographic characteristics, species distribution, spatial heterogeneity, case detection methods, and outbreak-related mortality. Results: Routine surveillance data revealed fluctuations in malaria transmission, with a significant decline in cases in 2024 followed by a sharp increase in 2025. Conversely, outbreak investigations documented high case fatality rates, particularly affecting children and older adults, caused by delayed diagnosis, limited diagnostic capacity, and limited access to timely treatment. Conclusions: These results highlight the dynamic and context-dependent nature of malaria epidemiology in Indonesia. Integrating routine surveillance with outbreak investigations provides a comprehensive understanding of endemic trends and systemic vulnerabilities. Strengthening adaptive surveillance, improving diagnostic capacity, and implementing spatially targeted interventions are crucial for preventing outbreaks and reducing malaria-related mortality, particularly in remote and high-risk areas.

Electricity Usage Control (P2TL) is a strategic program of PT PLN to reduce losses due to illegal electricity usage. This study aims to analyze the use of Geographic Information Systems (GIS) based on QGIS in supporting P2TL monitoring in Nabire Regency. The data used includes 54,939 customer coordinate points, collected through field surveys using GPS with a high level of accuracy. The method applied is descriptive spatial analysis with overlay techniques and spatial clustering. The results of the study show an uneven distribution of customers between districts, with the highest concentration in Nabire District (18,247 customers or 33.2%). The dominant tariff type is R1T (60.3%) with the largest power capacity of 1300 VA (49.5%). Spatial analysis identified 12 hotspots and 8 priority monitoring zones. The application of GIS increases the effectiveness of identifying vulnerable areas by 78% and reduces the duration of field inspections by 45%. The resulting thematic map visualization provides significant support in location-based decision making.

Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.

This study presents ReflectAI, a web-based system designed to automate the creation of teaching materials tailored to students' learning styles using behavior data from a Learning Management System (LMS). Student digital activity data—such as logins, material access, forum participation, assignment submission, and quiz results—are extracted and processed using a Hierarchical Clustering algorithm to categorize students into three learning styles: visual, auditory, and kinesthetic. Based on the clustering results, the system automatically generates personalized learning modules using generative AI (ChatGPT API), aligned with each student's learning preferences. Employing a data-driven system development approach, the system was tested with data from 230 students in a mathematics course. The results show diverse learning style distributions and relevant, tailored content generation. ReflectAI is designed to reduce teachers’ administrative workload and enhance personalized and adaptive learning. This system contributes to educational transformation through deep, data-driven technology integration.

A suitable and targeted marketing plan is required because of the intense competition in the retail drinking water sector. Customer segmentation using RFM (Recency, Frequency, and Monetary) analysis is one of the techniques employed. Additionally, K-Means clustering, a clustering technique based on machine learning, is employed. This study's goal is to present the findings in the form of graphs that can be used to examine consumer trends according to their attributes. With a value of 10286, the Calinski Harabaz index is a suitable metric to move on to the segmentation step in this study, which also tests three metrics using the clustering method. An ideal cluster is created for every cluster evaluation by dividing the Calinski Harabaz index into three more manageable clusters. This contrasts with other evaluation metrics that only yield two clusters. For instance, when XYZ drinking water sales transaction data was distributed, it was discovered that, out of the total drinking water sales, woodsale had 422 customers, diamond had 1061 customers, and star diamond had 2005 customers. The management of the XYZ drinking water company and other marketing fields are expected to encounter more intense competition as a result of the study's findings.

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

Diabetes Mellitus is one of the chronic diseases with high morbidity and mortality rates, making data-driven analysis necessary to understand patient mortality patterns. This study aims to analyze the mortality rate of Diabetes Mellitus patients based on age and length of hospitalization using a data mining approach with the K-Means Clustering method. The study employs a quantitative approach using secondary data obtained from the medical records of Diabetes Mellitus patients at Ibnu Sina Regional General Hospital, Gresik Regency, in December 2022. The dataset consists of 266 patient records with variables including age, length of stay, and final patient status. Data analysis was conducted through preprocessing stages, including data cleaning, transformation, and normalization, followed by the clustering process using the K-Means algorithm with the assistance of the RapidMiner application. The results show that patient data are divided into three clusters based on age ranges: 0–40 years, 41–55 years, and 56–90 years. The cluster with the age range of 56–90 years has the highest number of patient deaths compared to the other clusters. Meanwhile, the length of hospitalization does not show a significant effect on patient mortality. This study is expected to serve as a consideration for hospitals and health institutions in efforts to prevent and manage Diabetes Mellitus, particularly among the elderly population.

Savings and Loan Cooperatives (KSP) play a vital role in expanding community access to capital, especially within the informal sector. Nevertheless, non-performing loans remain a persistent challenge that can threaten liquidity and long-term institutional sustainability. KSP CU Mera Ndi Ate faces similar issues, which are assumed to stem not only from administrative weaknesses but also from members’ perceptions and behavioral factors. This research aims to examine the potential causes of non-performing loans through text-based sentiment analysis using an unsupervised learning approach. A quantitative method with a data mining framework was applied. Data were gathered through interviews, observations, documentation, and 200 customer opinion texts processed using the Orange Data Mining application. The analytical stages included preprocessing, corpus development, feature extraction, sentiment clustering, and visualization. Because the dataset lacked predefined labels, unsupervised learning was used to identify naturally emerging sentiment patterns. Findings reveal a predominance of critical sentiments related to credit assessment procedures and service quality. The highest sentiment score (75) concerned insufficient creditworthiness evaluation, followed by concerns about service efficiency (66.6667). These insights suggest that improving assessment accuracy and service quality may help reduce non-performing loans.