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Toddlers are a vulnerable age group to various types of diseases due to their immune systems that are still developing. Limited utilization of medical record data and the lack of structured information regarding disease patterns in toddlers based on age and causative factors have resulted in suboptimal prevention and treatment efforts. Therefore, an approach is needed to systematically classify toddler disease data. This study aims to apply data mining techniques using the clustering method with the K-Means algorithm to group types of diseases in toddlers based on age and causative factors. The variables used in this study include toddler age, type of disease, and causative factors. The data were obtained from RSUD Dr. R. M. Djoelham Binjai and processed using MATLAB software with three clusters. The results show that the K-Means algorithm successfully groups toddler disease data into three clusters with different characteristics. The first cluster is dominated by toddlers aged 0–11 months with appendicitis caused by genetic factors. The second cluster is dominated by toddlers aged 1–3 years with diarrhea caused by environmental factors and has the largest number of members. Meanwhile, the third cluster is dominated by toddlers aged 0–11 months with sore throat caused by environmental factors. The clustering results indicate a relationship between toddler age, disease type, and causative factors, which can be used as supporting information for decision-making in the prevention and treatment of toddler diseases.

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.

Goals – Goals from studies This is For explore approach strategic in development System Information Management (SIM) as integral part in support digital transformation of modern organizations. Study This emphasize importance integration technology information , effective data management as well as improvement digital competence resources Power man in operation system. Design/ methodology / approach – Conceptual article This use method review library with analyze various work relevant academic and technical manuals , in particular related implementation of SIM in the sector public and private . Study This referring to the works Jatnika et al. (2022–2024), including utilization Microsoft Office applications as skills supporting basis​ organizational digital literacy . Findings – Findings studies This show that SIM development is not just effort technical , but rather need strategic in support digital transformation . Key strategies covers design modular systems , data mining integration , training programs based users , and evaluation system in a way periodic . Components This allows organization build responsive and adaptive SIM ecosystem . Implications practical – Organizations that want to do digital transformation is necessary invest in development digital capabilities of sources Power the human as well as ensure effectiveness use developed SIM system in a way strategic can become driving force main in increase efficiency , accuracy , and capability taking decision across work units . Originality / value – Study This offers a conceptual model structured about development of SIM in context digital transformation , based on literature applications and needs organizations in the real world . This article give outlook practical for taker policy , IT managers , and HR developers .  

Slope stability is a key concern in open-pit mining due to its impact on safety and operational efficiency. Mine X, located in Kalimantan Island, faces landslide risks on its high wall slopes. This study aims to model slope stability and determine safe and economical slope geometry. The analysis was performed using the Limit Equilibrium Method (Bishop Simplified), both analytically and numerically, through Slide 6.0 software by Rockscience Inc. Input data were obtained from five geotechnical investigation points provided by PT.X, including cohesion, internal friction angle, and saturated unit weight. Two lithologies were analyzed—claystone and sandstone—with slope height variations (5 m, 10 m, 15 m) and angles (26°, 45°, 51°, 59°), under dry and saturated conditions. Simulation results show that the factor of safety (SF) decreases with increasing slope height and angle, especially under saturated conditions. Sandstone demonstrates better stability than claystone. The recommended optimal slope geometry is 10 meters in height with a 59° angle, yielding a SF ≥ 1.25 and aligning with the PC-400 excavator's cutting capability. This study provides a technical reference for designing safe slopes that support mining productivity.

The study aims to investigate how corporate governance impacts sustainability report disclosure in mining companies that are listed between 2021 and 2024 on the Indonesia Stock Exchange (IDX). The proportion of independent board members, the number of audit committee meetings held, and the level of managerial ownership are used to evaluate corporate governance. Using secondary data from the companies' official websites, a quantitative research approach is used. Purposive sampling was applied to select the sample from an initial population of 198 firms, based on two criteria: (1) being in the mining industry and listed on the IDX during the designated timeframe, and (2) regularly publishing sustainability and annual reports. By applying these criteria, a sample of 47 businesses was obtained, producing 188 observations in total.  Multiple linear regression was used to analyze the data using SPSS version 25. The results of the partial test show that while the percentage of independent board commissioners has no discernible effect on sustainability report disclosure, the frequency of audit committee meetings and managerial ownership have a significant and positive impact. These findings demonstrate how important internal ownership and an active audit function are to raising the standard of sustainability accountability and transparency.

The Rp 300 trillion tin trade mega-corruption case is one of the largest corruption scandals in the history of Indonesian natural resource management. This study aims to analyze violations of Pancasila principles and ethical governance in the tin trade mega-corruption case, and to identify threats to Indonesian natural resource management. The study uses a qualitative approach with descriptive methods. Data were collected through documentary studies of law enforcement reports, government documents, media reports, and related literature. Data analysis was conducted thematically within the theoretical framework of Pancasila, good governance, and public ethics. The findings indicate that this case violates all of Pancasila's tenets, particularly the second (Just and Civilized Humanity) and fifth (Social Justice for All Indonesian People). There were violations of governance principles including transparency, accountability, participation, the rule of law, and effectiveness. The modus operandi involved collusion between business actors, state officials, and law enforcement officers. The mega-allegations reflect a systemic failure in natural resource management that contradicts the constitutional mandate and Pancasila values. Structural reforms are needed in mining governance, enforcement of the integrity of the apparatus, and public participation in supervision.

Taxes are a primary source of government revenue and play a crucial role in economic development. However, tax avoidance practices are still widely practiced by companies, including in the mining sector, which has significant potential to generate state revenue. This study aims to examine the influence of financial distress, corporate governance (independent commissioners and audit committees), and institutional ownership on tax avoidance in mining companies listed on the Indonesia Stock Exchange for the 2020–2023 period. The study population consisted of 83 companies, and through purposive sampling, 61 companies were selected, with a total of 244 observations. The analysis used panel data regression with the help of Eviews 25. The results indicate that financial distress and institutional ownership have a positive effect on tax avoidance, while independent commissioners and audit committees have a negative effect on tax avoidance. These findings suggest that a company's financial condition and ownership structure play a significant role in determining tax avoidance policies.

Selecting a faculty that aligns with students’ interests and talents is a strategic step in determining the success of higher education and future career paths. However, most vocational high school (SMK) students still face difficulties in identifying the most suitable faculty due to the lack of data-driven analysis. This study implements the C4.5 classification algorithm within data mining techniques to build an automatic and measurable faculty recommendation system. The dataset consists of attributes such as SMK major, interest level, aptitude test results, academic grade average, and gender, with the output being the recommended faculty. The C4.5 algorithm was chosen for its ability to generate a transparent and interpretable decision tree, which helps both guidance counselors and students understand the rationale behind the recommendations. The experimental results show that the constructed classification model achieved an accuracy rate of 88%, based on cross-validation testing using data from 12th-grade students. The implementation of this system is expected to serve as an objective tool in the faculty selection process and to promote a data-driven decision-making approach in secondary education environments.

The limits of authority among the company’s organs in corporate management are expressly regulated under the UUPT, which assigns managerial and representative functions to the Board of Directors, supervisory and advisory functions to the Board of Commissioners, and control functions to the General Meeting of Shareholders. In practice, these authorities are often not implemented effectively, resulting in various violations. Such violations do not always arise from ultra vires acts but may also stem from negligence in exercising the granted authority. Improper management, administrative omissions, and passive supervision contribute to the risk of loss upon revocation of a mining business license. The absence of a valid license removes the company’s legal basis for operating and triggers potential liability for he organs that were negligent. This research employs a normative juridical approach with a descriptive-analytical specification. Data were obtained from primary, secondary, and tertiary legal materials through literature review and case study of Decision No. 3/Pdt.G/2023/PN.Mgg. Directors must distinguish between beheer and beschikking actions when determining the scope of corporate management. Meanwhile, the Board of Commissioners is obligated to conduct supervision and provide advice proactively, whether requested or not, as a manifestation of good faith.

Rock stability and service life in geotechnical and mining engineering are highly dependent on the rock's mechanical and physical parameters, where the variation in sandstone grain size is a crucial intrinsic factor. This study aims to comprehensively analyze the correlation between sandstone grain size with uniaxial compressive strength (UCS) and resistance to weathering (Slake Durability Index) in samples taken from the Balikpapan and Pulau Balang Formations in the Samarinda area, East Kalimantan. The research methodology involved a series of standard laboratory tests, including rock physical properties analysis, grain size distribution analysis, UCS testing, and slake durability testing through three cycles. The test results show a significant correlation: sandstone with finer grain sizes and higher density consistently demonstrates greater UCS values and a higher Durability Index, indicating superior mechanical and physical resistance. Specifically, the Pulau Balang Formation exhibits a more compact structure and finer grain size, resulting in better durability values compared to the Balikpapan Formation. These findings are important as a geomechanical data basis for slope design planning, rock mass stability analysis, and material selection in infrastructure projects or mining operations involving both formations.

This study aims to analyze the effect of blasting geometry on drilling and blasting costs in mining operations at PT Unggul Dinamika Utama, Kutai Timur Regency, East Kalimantan Province. The research focuses on comparing two operational areas, namely PIT Tempudo 6 and PIT East, which apply different blasting geometries: a burden of 7 m and spacing of 8 m at PIT Tempudo 6, and a burden of 8 m and spacing of 9 m at PIT East. The research method involved collecting primary data from actual field drilling and blasting activities, as well as secondary data from the company. The parameters analyzed included blasting geometry, explosive consumption, and operational costs of drilling and blasting. The results show that the total drilling cost at PIT Tempudo 6 was Rp. 215,689,696, while at PIT East it was Rp. 162,177,899. The total blasting cost at PIT Tempudo 6 reached Rp. 3,023,066,977.60, while at PIT East it was Rp. 1,780,839,602.80. Thus, the total operational cost of blasting activities at PIT Tempudo 6 amounted to Rp. 3,238,756,673.60, and at PIT East amounted to Rp. 1,943,017,501.80. It can be concluded that differences in blasting geometry significantly affect operational cost efficiency. Larger burden and spacing values lead to more efficient costs by reducing the number of drill holes and explosive consumption per blasted rock volume.

Technological advancements in education have led to major transformations, particularly with the implementation of the Merdeka Curriculum, which emphasizes learning flexibility, student-centered approaches, and educator autonomy in developing innovative teaching methods. One of its essential aspects is the integration of technology for managing educational data, including student health records. At SMP IT Mutia Rahma, biannual student health monitoring has generated a growing volume of data, making it difficult to identify students experiencing psychological challenges. Adolescent mental health problems—such as learning stress, anxiety, and social pressure—can negatively affect academic performance if left unaddressed. This study aims to group students based on their mental health conditions to support more effective intervention strategies. The K-Means Algorithm, a data mining technique for clustering data by similarity, was employed to analyze student health data. The results show that in a three-cluster model, Cluster 2 represents students in a stable condition characterized by high resilience and low counseling needs, indicating good mental health and academic engagement. Meanwhile, Clusters 1 and 3 include students requiring further attention and support. This research demonstrates that the K-Means Algorithm can serve as an effective tool in identifying and categorizing student mental health conditions to improve school-based health management and early intervention programs.

This study analyzes the effect of Total Asset Turnover, Debt to Equity Ratio, and Return on Assets on earnings management in mining companies listed on the Indonesia Stock Exchange during 2020–2024. Using a quantitative and causal research design, the study examines 18 purposively selected companies over five years, resulting in 90 observations. Data were analyzed through panel data regression using SPSS 26. The results show that Total Asset Turnover does not significantly affect earnings management, while Debt to Equity Ratio and Return on Assets have a significant influence. These findings indicate that profitability and leverage play important roles in shaping earnings management practices in the mining sector

This activity aims to encourage the economic independence of Micro, Small, and Medium Enterprises (MSMEs) by optimizing local potential in Kepoh Village, Toboali District, South Bangka Regency. The outreach activities were conducted by collecting primary data through observation, interviews, and documentation regarding the condition of MSMEs and the village's superior potential, including the marine and fisheries, plantation, and mining sectors. The data obtained showed that the majority of MSMEs in Kepoh Village are engaged in the food trade and home industries, with varying income levels, ranging from Rp. 150,000 to Rp. 30,000,000 per month. In addition, the village's abundant potential, such as fisheries with an average production of 16.5 tons, plantations with various commodities, and tin mining resources, presents a great opportunity to support the development of local MSMEs. Through this outreach activity, MSMEs are encouraged to increase their business capacity, utilize local potential sustainably, and understand the importance of protecting Intellectual Property Rights (IPR) as a marketing strategy and to increase competitiveness. This counseling is carried out to provide a positive contribution in providing knowledge, motivation, and more innovative business management strategies starting from product marketing strategies both in economic and legal aspects as well as the urgency of business legality, so as to strengthen the economic independence of MSME actors and support the inclusive economic development of Kepoh Village

This study aimed to examine the ecological and socio-economic aspects of mangrove utilization in Nasem Village, Merauke Regency, Papua Selatan. The ecological assessment included mangrove species composition, regeneration potential, health status, and Importance Value Index (INP). Socio-economic data were collected through questionnaires and focus group discussions with 30 respondents. The results showed that  Avicennia marina  had good regeneration potential and dominated the community with the highest INP (1.13), while  Avicennia alba  was categorized as new regeneration with a low INP (0.31). The health status of both species was classified as rare/damaged according to national standards, indicating the need for restoration. From a socio-economic perspective, 90% of respondents utilized mangroves for firewood and fisheries, and 95% stated that mangroves significantly contributed to household income, although 83% earned less than IDR 1,000,000 per month. Most respondents (85%) recognized the ecological functions of mangroves, but anthropogenic pressures such as sand mining remained major threats. These findings highlight the urgent need for community-based mangrove management that integrates ecological restoration with sustainable economic development. The study provides scientific evidence to support policy recommendations for sustainable coastal zone management in Merauke.

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.

This study aims to obtain empirical evidence regarding the effect of Environmental, Social, and Governance (ESG) disclosure on firm value. The research sample was obtained using purposive sampling on mining firms listed on the Indonesia Stock Exchange (IDX) during the 2020–2023 period, with a total of 102 observations. Data analysis was conducted using panel data regression to test the proposed hypotheses. The results show that environmental disclosure has a significant positive effect on firm value, while social and governance disclosure have a significant negative effect. The theoretical implication of this study refers to agency theory, which asserts that information transparency through ESG can reduce information asymmetry between management and shareholders. However, if disclosure is carried out merely as a formality or symbolic practice, it may instead generate agency costs that are detrimental to the firm. In addition, these findings are also relevant to signaling theory, in which environmental disclosure can serve as a positive signal of a firm’s commitment to sustainability practices, thereby enhancing investor trust and strengthening the firm’s reputation. Practically, this study contributes to providing a more comprehensive understanding for firms, management, investors, and other stakeholders, while also serving as a reference for future research on ESG and firm value.

Shallots are one of the most strategic horticultural commodities in Indonesia, with high demand and varying production levels across regions. Differences in productivity between areas often create challenges in managing distribution and formulating national food policies. This study aims to analyze shallot production data in Indonesia by applying the K-Means Clustering algorithm using Python. The production data were collected from official agricultural statistics publications, followed by preprocessing, normalization, and determination of the optimal number of clusters using the Elbow method and Silhouette Score. The clustering results show the formation of several groups representing regions with high, medium, and low production levels. Visualization of the clustering results reveals the distribution patterns of shallot production, which can serve as a basis for supporting policy formulation in the development of shallot production centers in Indonesia. Thus, the application of K-Means Clustering with Python proves to be an effective approach to provide clearer insights into regional production variations and can be utilized as an analytical tool to support decision-making in the agricultural sector.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.

Profitability is one of the most important indicators for assessing a company's financial performance, as reflects the extent to which management efficiently manages resources to generate profits for the company. The purpose of this study was to determine the effect of working capital turnover, cash turnover, accounts receivable turnover, and inventory turnover on the profitability of mining companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. The sample was selected using a purposive sampling technique with a non-probabilistic sampling approach based on specific criteria. As a result, 36 companies qualified for this study. Data were processed using multiple linear regression analysis with SPSS version 25. The results of this study indicate that working capital turnover has a positive effect on profitability, while cash turnover has no significant effect. Meanwhile, receivable turnover has a positive effect on profitability, and inventory turnover has a negative effect on profitability. These results indicate that effective current asset management in company can increase profits, while the low contribution of cash turnover indicates that liquidity don”t always correlate with profitability, the negative impact of inventory turnover indicates the potential for decreased profits if inventory management is suboptimal.. This study confirms that working capital management has diverse impact on profitability. Working capital and accounts receivable turnover are driving factors for improved financial performance, while cash turnover does not directly impact profits, inventory turnover can negatively impact profitability if not managed effectively.