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This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

The primary focus of this research is to evaluate the influence of Good Corporate Governance (GCG), profitability levels, and entity scale on the market value of coal mining companies listed on the Indonesia Stock Exchange (IDX) between 2021 and 2023. This study adopts a quantitative design by utilizing secondary data from the official IDX website, where 8 companies were selected as samples from a total population of 34 coal sub-sector companies through purposive sampling techniques. Data processing was carried out through panel data regression analysis using Eviews 12 software. The research data indicates that, independently, the implementation of good corporate governance and the level of profit acquisition do not contribute significantly to determining the value of the entity. Conversely, company size is proven to have a significant negative impact. Simultaneous testing confirms that these three independent variables collectively have a significant effect on company value. These findings indicate the need for strategies that consider factors beyond good corporate governance and profitability in efforts to increase company value, such as operational efficiency and proper asset management.

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.

Mining of rocks, particularly andesite, in East OKU Regency provides significant economic contributions but generates environmental impacts that require rehabilitation through reclamation and post-mining management. This study aims to evaluate the compliance level of Production Operation Mining Business License (IUP OP) holders with these obligations and to identify challenges in their implementation. A descriptive quantitative and qualitative approach was employed, with primary data collected through interviews and field observations related to reclamation and post-mining plans, as well as the placement of guarantees. Secondary data included IUP licensing documents, legislation, and guidance letters from the Energy and Mineral Resources Office of South Sumatra Province. Quantitative analysis categorized compliance levels, while qualitative analysis examined challenges and guidance strategies. The results indicate variations in IUP OP holders’ compliance; some have prepared documentation and placed guarantees, but delays and lack of continuity were observed. Major challenges include profit-oriented approaches, limited technical and environmental understanding, and limited permit duration. Guidance strategies, supervision, guarantee management, provision of technical experts, and community empowerment proved essential. These findings have implications for enhancing compliance, sustainable post-mining planning, and responsible mining practices.

This study aims to determine and analyze the effect of Corporate Social Responsibility, Green Accounting, Intellectual Capital, and Firm Size on Financial Performance with Good Corporate Governance as a moderating variable. This study was conducted on mining companies listed on the Indonesia Stock Exchange (IDX) for a five-year period, namely 2020–2024. The study population consisted of 34 mining companies, with the sampling method using purposive sampling, resulting in 33 companies as research samples. The information used was derived from secondary sources, namely annual reports and sustainability reports.  Multiple linear regression and Moderated Regression Analysis (MRA) were used to analyze the data, with the assistance of EViews software. The results showed that Corporate Social Responsibility had a positive and significant effect on Financial Performance. Green Accounting and Intellectual Capital also had a positive and significant effect on Corporate Social Responsibility. Meanwhile, Firm Size had a positive but insignificant effect on Financial Performance. The results of the moderation test indicate that Good Corporate Governance is unable to moderate the influence of CSR, Green Accounting, Intellectual Capital, or Firm Size on Financial Performance. This finding suggests that increasing social responsibility, implementing green accounting, and managing intellectual capital can improve the financial performance of mining companies, but their effectiveness has not been strengthened by corporate governance mechanisms.

PT. Hamparan Mulya operates in the coal mining industry and applies an open-pit extraction system. In this mining method, managing surface water particularly rainwater is essential to ensure smooth operations. An effective drainage system is required to prevent runoff water from contaminating nearby rivers, lakes, and surrounding ecosystems. One practical approach used in mining operations is the construction of settling ponds, which function as treatment units for water collected in the sump before it is released into natural waterways. This study aims to identify the appropriate storage capacity for the settling pond and sump by analyzing rainfall data using the Log Pearson Type III method. The analysis produced a design rainfall value of 507.16 mm/day and a rainfall intensity of 56.94 mm/hour. With a catchment area of 14 km², the resulting runoff discharge reaches 30,782.16 m³/hour. Based on these parameters, the settling pond must be engineered to accommodate a total discharge of 30,782.16 m³/hour.

Guaranteeing the geotechnical stability of slopes is an absolute prerequisite for the sustainability of open pit mining operations, considering the potential for multidimensional losses due to slope failure. The specific geological conditions at PIT B1 PT. Pancaran Surya Abadi, which is composed of sedimentary rocks (coal, sandstone, and claystone), are susceptible to degradation and softening, especially due to high rainfall that causes an increase in pore water pressure and a decrease in rock shear strength. This study aims to analyze the stability of highwall slopes using the Morgenstern-Price Method to determine the Safety Factor (SFF) value according to Ministerial Decree number 1827 K/30/MEM/2018, and continued with a semi-quantitative risk analysis. The analysis results show that the initial slope has a static SFF of 0.77 (Not Safe). After redesign, the recommended optimal single slope geometry is: sandstone (Height 5 m, Angle 20°, Berm 5 m) and claystone (Height 10 m, Angle 60°, Berm 5 m). This redesign resulted in a FK of 1.34 (Safe). Sensitivity analysis to groundwater level rise (GTL) showed that the GTL value remained safe (GTL ≥1.30) up to a 30% increase in GTL. However, a 40% to 80% increase in GTL caused the GTL to decrease (1.28–1.21), classified as Medium Risk. A 100% increase in GTL drastically reduced the GTL to 1.05, classified as High Risk. This study emphasizes the need for close monitoring and additional drainage to maintain the long-term stability of slopes under the influence of rainfall.  

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

Mining sequence design is one of the important stages in open pit mining activities that aims to organize the excavation stages so that production activities run effectively, efficiently, and in accordance with the established targets. This research was conducted at PT Putra Perkasa Abadi Site SKS with the aim of designing a mining sequence in January and February 2025. The methods used include analysis of topographic data, geology, pit design, and monthly production targets. The data was processed using mining software to determine the excavation sequence based on elevation, overburden volume, and coal reserves. The design results show that the total planned overburden and coal volume is able to meet the company's production targets by considering slope stability and the efficiency of digging and loading equipment. In addition, the resulting sequence design also takes into account aspects of work safety and field operational conditions, such as mine road access and drainage systems. With a structured design, it is expected that mining activities during this period can run according to schedule, minimize operational obstacles, and support the achievement of production targets and cost efficiency at PT Putra Perkasa Abadi Site SKS.

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

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.

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 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 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

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.

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.

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.

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.

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 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.