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Regional development faces dynamic challenges amid rapid economic growth driven by natural resource extraction. This study aims to identify leading economic sectors, analyze structural economic transformation, and evaluate the role of these sectors in regional development. The research employs a quantitative method with a descriptive approach. Secondary data consist of Gross Regional Domestic Product (GRDP) at constant prices over the past five years. The analytical techniques applied include Location Quotient analysis to identify base sectors, Shift-Share analysis to assess structural changes as well as comparative and competitive advantages, and Klassen Typology to classify sectoral growth patterns. The results reveal a structural shift from primary sectors, such as agriculture and fisheries, toward secondary sectors, including mining and manufacturing. Despite challenges related to development equity, these leading sectors serve as key drivers of regional economic growth. To maximize the contribution of leading sectors to broader regional development, this study recommends that government policies prioritize the strengthening of intersectoral linkages.

This study is entitled “The Ecofeminist Movement of Mollo Indigenous Women in Rejecting Marble Mining (study: Rejection of Marble Mining in Fatumnasi Village, South Central Timor Regency)” which aims to understand and analyze the ecofeminist movement carried out by Mollo indigenous women in rejecting marble mining activities in Fatumnasi Village. The presence of marble mining in the Mollo indigenous area has posed a threat to Environmental sustainability, water sources, and cultural values that have long been the identity of the community. Through a descriptive qualitative approach, this study describes the role and form of resistance of Mollo indigenous women based on the ecological relationship between women and nature. Data were obtained through in-diepah interviews, field observations, and documentation of the head of Fatumnasi Village, traditional women’s figures, religious figures, community leaders, and youth leaders. The results of the study indicate that the movement to reject marble mining is not merely a form of protest against environmental damage, but also a form of ecofeminist awareness that emphasizes that women’s bodies and the body of nature are an inseparable whole. This movement is expressed through various acts of resistance such as traditional rituals, weaving, demonstrations, and customary deliberations, each carrying symbolic meaning about the harmony between humans and nature. Based on Françoise d’Eaubonne’s theory of ecofeminism, the Mollo women’s movement reflects critical awareness toward patriarchal and capitalist systems that exploit both women and the environment. Thus, it can be concluded that the ecofeminist movement of Mollo indigenous women in rejecting marble mining is a form of women’s struggle to maintain environmental sustainability and maintain cultural identity through loclah wisdom practices.Ecofeminism, Mollo Indigenous Women, Marble Mining, Fatumnasi Village, Environment

This study aims to examine the effect of green innovation on company value, with financial performance as a mediating variable, in the mining industry. This study uses a systematic literature review approach by examining various relevant previous studies. The results of the study indicate that green innovation plays a significant role in improving environmental performance and operational efficiency of companies, which in turn positively impacts financial performance. Good financial performance is a key factor in strengthening company value and stakeholder trust. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies. This study also found that companies that successfully implement green innovation tend to have a better image in the eyes of investors and the public, which contributes to increasing the company's market value. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies, strengthening their position in an industry that increasingly prioritizes sustainability and social responsibility.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

This research investigates the effect of fundamental factors, namely the current ratio, debt to equity ratio, and return on equity on stock returns of mining firms listed on the Indonesia Stock Exchange (IDX) during 2021–2023. The research highlights the utility of understanding a firm’s financial performance in guiding investment selection within the capital market. Although the mining industry contributes significantly to Indonesia’s economy, stock movements in this sector are often subject to uncertainty due to market fluctuations and commodity price volatility. This research utilizes secondary data from annual financial statements and stock price records of 51 IDX-listed mining companies over the study period. Panel data regression, combined with descriptive and quantitative statistical techniques, was employed using E-Views 12 software. The findings reveal that stock returns are significantly influenced by the current ratio, debt to equity ratio, and return on equity. These results provide useful insights for investors, financial analysts, and corporate management by emphasizing the function of fundamental indicators in assessing stock performance, particularly within the mining sector.

This study examines the critical role of expert opinions from the Ministry of Energy and Mineral Resources (ESDM) during the investigation phase in uncovering gold mining without permit (PETI) crimes under Article 120 of the Indonesian Criminal Procedure Code (KUHAP) in West Kalimantan. The research employs a normative-empirical approach, analyzing legal provisions, government reports, and judicial practices to assess how ESDM experts contribute to establishing the material truth of PETI cases. Findings reveal that expert opinions are indispensable for verifying the absence of permits, assessing environmental damage, and quantifying state losses, thereby strengthening evidentiary frameworks for prosecutors and judges. However, challenges persist, including coordination gaps between law enforcement and ESDM, insufficient technical capacity among investigators, and potential threats to expert independence. The study concludes that optimizing the use of ESDM expertise is not merely procedural but strategic for effective, accountable, and just enforcement against PETI, which remains a significant threat to national resource sovereignty and environmental sustainability.

Diabetes Mellitus is a disease caused by the failure of the pancreas organ in producing the hormone insulin in excess causing increased blood sugar levels and resulting in a lack of insulin. This study discusses the application of the k-means clustering method to determine risk factors for diabetes mellitus. By using the clustering method, data will be grouped into several clusters or groups which in this study compare by applying several data mining tools such as RapidMiner, SPSS, WEKA, and Python. From the results of the comparison carried out resulted in 5 calculations, namely the manual calculation of cluster 1 with a ratio value of 73% being the first priority, calculations using RapidMiner resulting in cluster 3 with a ratio value of 58% being the first priority, calculations using SPSS cluster 2 with a ratio value of 34% being the first priority, and calculations using Python produce cluster 1 with a ratio value of 55% being the first priority.

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 seeks to evaluate the extent to which profitability, leverage, independent commissioners, and political links influence tax avoidance in Indonesian mining companies for the 2021–2024 timeframe. The mining sector was chosen because it contributes significantly to national income but is typically associated with the practice of tax avoidance. The novelty of this study lies in the addition of the political connections variable, which has rarely been studied in the context of Indonesian mining. The research data were obtained from annual reports and financial statements of companies obtained through purposive sampling, resulting in 77 observations. Multiple linear regression analysis under a quantitative method was applied, and the evidence suggests that profitability contributes positively to tax avoidance, as higher profits are associated with a stronger tendency for companies to minimize tax payments. Conversely, political connections have a negative effect, indicating that political and military experience shapes loyalty to the interests of the state, thereby encouraging tax compliance. Meanwhile, leverage and independent commissioners do not exert any influence on tax avoidance. The outcomes of this research may serve as a reference for regulators, scholars, and investors to better comprehend the determinants of tax avoidance and to contribute to enhancing governance structures and refining tax policy.

This study examines the impact of Market Value Added (MVA), Economic Value Added (EVA), and Financial Value Added (FVA) on stock returns in energy-sector mining companies listed on the Indonesia Stock Exchange (IDX) during 2018–2023. A quantitative approach with multiple linear regression was applied to 23 purposively selected firms based on data availability. Secondary data were obtained from annual reports and stock prices published on the IDX website. The findings show that EVA has a significant effect on stock returns (p = 0.048 < 0.05), while MVA (0.075) and FVA (0.080) are not significant individually. However, the three variables collectively influence stock returns (p = 0.031 < 0.05). The adjusted R² of 0.396 indicates that 39.6% of return variability is explained by the model, with the rest influenced by other factors. Overall, EVA emerges as the key indicator for investors in evaluating return potential, while market-based measures such as MVA are less decisive, and historical value indicators (FVA) are less statistically relevant as predictors of stock returns. From a managerial perspective, firms are encouraged to focus on capital efficiency and sustainable economic value creation to enhance their investment appeal.

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.

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.

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.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

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.

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.

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.

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.