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Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.

The Indonesia Pintar Program (PIP) is an educational assistance program for students from underprivileged families, but determining the eligibility of recipients still faces obstacles in the form of subjectivity and data imbalance. This study aims to classify the eligibility of high school students receiving PIP in Jambi City using data mining methods. The SMOTE technique was applied to overcome class imbalance, and Gain Ratio feature selection was used to determine important attributes. The dataset used consisted of 19,596 student data with a training data distribution of 70% and testing data of 30%. The classification process used the Naïve Bayes, Decision Tree (J48), and Random Forest algorithms with the Use Training Set, 5-Fold, and 10-Fold Cross Validation testing schemes. The results show that SMOTE improves model performance, but feature selection in some cases reduces accuracy. Overall, Random Forest without feature selection provides the best results with an accuracy of 93.33% and is recommended as the most effective model for objectively determining PIP recipient eligibility.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.

Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.

This study aims to analyze the effect of sustainability report disclosure, audit committee meeting frequency, liquidity, leverage, and total asset turnover on profitability in mining companies listed on the Indonesia Stock Exchange (IDX) during the 2021–2023 period. Profitability is measured using Return on Equity (ROE). This research adopts a quantitative approach using secondary data obtained from annual financial statements and sustainability reports. The sample was selected using purposive sampling, yielding 34 mining companies with 102 observations in total. Multiple linear regression analysis was employed after fulfilling classical assumption tests. The results indicate that sustainability report disclosure, audit committee meetings, liquidity, leverage, and total asset turnover simultaneously have a significant effect on profitability. However, partially, total asset turnover has a positive and significant impact on profitability. Meanwhile, sustainability report disclosure, audit committee meeting frequency, liquidity, and leverage do not significantly affect profitability. These findings suggest that asset utilization efficiency plays a crucial role in improving profitability in the mining sector. This study is expected to provide insights for companies, investors, and regulators to understand the determinants of profitability better and to support improved corporate governance and financial decision-making in mining companies.

Mine disposal materials such as tailings, overburden, and waste rocks are critical components in mining operations that require comprehensive understanding of their geotechnical properties to ensure stability and safety of storage facilities. This literature review aims to analyze the role of particle gradation and mineralogical composition in determining shear strength and compressibility of mine disposal materials, with particular focus on nickel mining. A sistematic literature review method was employed by analyzing 30 scientific publications from 2019-2025 obtained from various academic databases. The review findings indicate that particle size distribution (gradation) has significant influence on shear strength and compressibility, where materials with coarser gradation and higher coefficient of uniformity (Cu) exhibit greater shear strength and lower compressibility. Mineralogy, particularly clay mineral content, increases cohesion and microporosity but also increases compressibility under loose conditions. Studies on nickel mine waste demonstrate that ferronickel slag possesses favorable drainage characteristics suitable for rockfill material, while tailings require strict gradation control. In conclusion, comprehensive characterization integrating gradation parameters (Cu, Cc, D50) with mineralogical analysis (XRD, XRF) is essential for predicting mechanical behavior of mine disposal materials and designing safe storage facilities.

Coal quality must be controlled from the pit area to the ROM stockpile to ensure compliance with market specifications. However, hauling and stockpiling processes often lead to changes in coal characteristics. This study aims to analyze variations in proximate parameters between coal from Pit B1 and ROM Stockpile Km4 at PT Trisensa Mineral Utama and to identify factors contributing to these changes. The methodology includes field sampling at both locations, sample preparation based on ASTM standards, and laboratory testing of inherent moisture, residual moisture, ash content, volatile matter, and fixed carbon. The results indicate that coal undergoes quality changes after being stored in the stockpile, marked by a decrease in inherent moisture of 2.54% (from 17.64% to 15.10%), a decrease in residual moisture of 1.42% (from 17.17% to 15.75%), a slight reduction in ash content of 0.16%, a decline in volatile matter of 0.28%, and a reduction in fixed carbon of 0.18%. These changes are influenced by field conditions, material contamination during mining, rainfall, coal porosity, and handling activities at the stockpile. The findings highlight the need for improved sampling management, better surface water control, and stricter material handling procedures to minimize coal quality degradation.

This study analyzes the effect of Bitcoin prices, the LQ45 Index, mutual fund net asset value (NAV), and the net profit margin (NPM) of gold mining companies on the price of gold as a safe haven asset within the context of the Indonesian financial market. Gold is often seen as a safe haven asset that is the primary choice of investors when economic uncertainty increases, but the relationship between gold and various other investment instruments still requires further study. This study uses a multiple linear regression method with a robust standard errors approach to analyze 420 monthly and quarterly data observations during the 2018-2022 period. The results of the study found that the price of Bitcoin and the NPM of gold mining companies had a significant positive influence on the price of gold, while the LQ45 Index had a significant influence effect. Meanwhile, the NAV of mutual funds showed a significant positive influence that was not in line with the initial hypothesis. These findings indicate that gold does not always function absolutely as a safe haven asset, as its role is contextual and still influenced by the dynamics of other investment instruments such as digital assets, stock markets, and mutual funds. The study's results make an important contribution to financial literature by proving that the safe haven characteristics of gold are complex and dynamic, so investors need to consider various factors and market conditions before allocating investments to gold as a hedging strategy in their portfolios.

Blasting and crushing are essential stages in the limestone mining process; however, both stages may contribute to material volume loss due to technical factors and geological conditions. This study aims to analyze the blasted volume, crushed volume, and the amount of volume loss occurring throughout these processes. The research utilizes primary data including blasting geometry, blasting patterns, crushing production, and secondary data such as regional geology and equipment specifications. Based on 15 blasting activities conducted from October to December 2024, the total blasted volume reached 71,691 tons with an average powder factor of 0.23 kg/m³. Meanwhile, the total volume produced from secondary crushing was 71,575 tons. The comparison indicates volume loss influenced by suboptimal fragmentation, rock characteristics, work efficiency of the crushing unit, and operational constraints in the field. The results of this study are expected to serve as a reference for optimizing blasting design and crushing operations to minimize volume loss and improve overall mining productivity.

TJSL/CSR in Indonesia is regulated as a legal obligation (mandatory) for companies engaged in the natural resources sector. Its success depends on the principle of accountability, which requires transparency and responsibility. This normative-juridical study evaluates the application of accountability principles in the mining sector. Normatively, PP 47/2012 requires CSR to be listed as an expense and focused on sustainable development (PPM). However, this regulation is not robust because it does not set a minimum fund allocation or clear program boundaries, allowing for multiple interpretations. Empirically (Sekotong case study), accountability is implemented in a formalistic manner, consisting only of one-way administrative reports without meaningful participation from the affected communities. A significant weakness is apparent when dealing with the increase in illegal gold mining (PETI) in legal concession areas. This situation results in a vacuum of responsibility. Companies can claim environmental damage caused by PETI, so that responsibility does not successfully ensnare corporate negligence in prevention efforts. The CSR accountability structure in Indonesia is weak because it only emphasizes activities that are carried out, not negligence that is overlooked. Regulatory reform is needed so that accountability includes passive responsibility to ensure that TJSL functions as a significant instrument of sustainable development.

The stability of slopes in open-pit coal mining in Indonesia is significantly influenced by geological faults, which are a major factor causing slope failures. This study aims to examine the impact of faults on slope stability by conducting a systematic literature review of 25 scientific publications from 2018 to 2025. The results indicate that faults and fault zones consistently reduce rock mass integrity through several mechanisms, including stress concentration in weak zones, the formation of preferential sliding surfaces, amplification of hydro-mechanical effects from groundwater and rainfall, and the reduction of rock strength parameters. Case studies in Kalimantan and Sumatra confirm these mechanisms with slope failures aligning with fault orientations. Kinematic and numerical analyses using the Limit Equilibrium Method (LEM), Finite Element Method (FEM), and Distinct Element Method (DEM) show a reduction in the safety factor (SF) by up to 36% on slopes affected by faults. Practical recommendations include continuous monitoring using Slope Stability Radar (SSR), optimization of slope geometry with angles < 18° in fault zones, groundwater control, reinforcement with anchors and bolting, and UAV-based discontinuity mapping for hazard zoning. This study concludes that managing slopes in fault zones requires an integrated approach combining detailed geological investigation, multi-method numerical analysis, real-time monitoring, and specific mitigation design.

This study aims to analyze the effect of Environmental, Social, and Governance (ESG) on the financial performance of mining companies listed in the Jakarta Islamic Index (JII70) for the 2020–2024 period, with the Debt to Equity Ratio (DER) as a control variable. The findings show that, partially, the Environmental variable has a positive but insignificant effect on ROA, indicating that efforts in energy efficiency, waste management, and emission reduction have not yet been fully reflected in short-term profitability. In contrast, the Social variable has a significant effect on ROA, emphasizing that companies’ engagement in building stakeholder relationships, protecting employee rights, and implementing social responsibility programs contribute substantially to financial performance. The Governance variable also has a significant effect on ROA, highlighting the importance of good governance practices, transparency, and accountability in enhancing profitability. Meanwhile, the control variable DER shows no significant effect on ROA. Simultaneously, ESG performance has a significant effect on ROA, proving that integrated ESG implementation supports the profitability of mining companies. These findings confirm that ESG is not only a compliance measure with sustainability principles but also a long-term business strategy that strengthens companies’ competitiveness and serves as a crucial consideration for investors in making investment decisions.

This study aims to analyze the spatial patterns of ambulance transport demand in Semarang City based on patients’ origin subdistricts, origin villages, and destination healthcare facilities. The analysis employed the K-Means Clustering algorithm as a data mining method to group areas according to similarities in the volume of ambulance requests. The dataset consisted of ambulance transport service records from January 2024 to September 2025, obtained from the Semarang City Health Office. The analytical procedures included data cleaning, normalization, determination of the optimal number of clusters using the Elbow Method, and cluster formation using K-Means. The results show two main clusters for subdistricts and destination healthcare facilities. High-demand subdistricts were generally densely populated areas such as Banyumanik and Pedurungan, with an average of 1,256 requests, while RSUP Dr. Kariadi emerged as the dominant referral facility with 3,893 requests. Meanwhile, village-level origins formed three clusters, with average demands of 549 (high), 190 (medium), and 36 (low). These findings are expected to support strategic planning for equitable ambulance fleet distribution and improved efficiency of patient transportation services in Semarang City.

This study was conducted to evaluate the level of conformity between actual mining activities in March 2025 and the monthly sequence design plan at PT Tama Raya's P3000BK14 pit. The evaluation focused on geometric deviations, volume achievement, and changes in field conditions that influenced these discrepancies. The data used included the sequence design, actual topography from the TLS (Low-Scale Land Survey), and weekly production realizations. The results showed significant discrepancies between the planned and actual operations in weeks 2 and 4, primarily in the form of overcuts, undercuts, non-designed contour changes, thinning of the coal seam, and hydrological obstacles such as ponding and unstable drainage. The R2 sequence redesign in week 4 proved to improve the overburden excavation flow but did not fully restore coal access due to persistent geological and water conditions. These findings emphasize the need for more rapid topographic updates, more intensive water handling, and interdepartmental coordination to ensure better synchronization of plans and operations.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered

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 literature study evaluates the accuracy of the Slope Mass Rating (SMR) method for coal mine slope stability in Indonesia through a systematic descriptive synthesis of 25 empirical studies from 2020 to 2025. The objectives of the study were to identify the level of SMR prediction accuracy, factors affecting the method's performance, and modifications required for local Indonesian conditions. The research method involved a systematic search with inclusion criteria for empirical studies reporting SMR and/or Safety Factor (SF) values ​​for coal mines and associated slopes in Indonesia. Quantitative analysis showed a range of reported SMR values ​​between 41 and 96 with a median of 72, while SF values ​​ranged from 1.137 to 4.09 for normal operational conditions. The synthesis results indicated that SMR provides a consistent stability classification for initial slope design and failure mode identification (planar, wedge, toppling), with historical validation showing a correlation of up to 91.23% between SMR-based hazard zoning and actual field events in some cases. Key limitations include dependence on discontinuity data quality, sensitivity to groundwater conditions and tropical weathering, and variation in the interpretation of adjustment factors F1-F4. Modifications such as NAAF23 and integration with numerical modeling have been shown to improve prediction reliability. It is recommended that coal mining practitioners combine SMR with kinematic analysis and limit equilibrium modeling as standard operating procedures, and develop adjustment factors specific to Indonesian geological conditions. Further research should focus on standardizing parameter reporting and cross-site quantitative validation to enable more robust statistical meta-analyses.  

Damage to rotable axle components on CAT KT4 Loaders in the mining industry is often the main cause of operational downtime. This study aims to analyze the frequency of damage to axle components and evaluate the effectiveness of preventive maintenance to reduce downtime duration and repair costs. Damage data collected from January to August 2024 were quantitatively analyzed to identify the most frequent damage patterns, especially seal leaks and brake modules. The results show that the implementation of preventive maintenance, such as regular seal replacement and hydraulic pressure checking, can significantly reduce the frequency of breakdowns and improve operational efficiency. The research also provides recommendations on maintenance strategies that can be implemented to extend component life and reduce overall downtime.

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.