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Measuring coal volume in the Stock ROM area played an important role in production control and mining evaluation. This research was conducted in the Stock ROM area at PT. Victor Dua Tiga Mega, where volume measurements were generally carried out using Total Station (TS) as the main method, howefer the use of Unmanned Aerial Vehicles (UAV) has begun to be implemented as a more efficient alternative. This study aims to compare the results of Stock ROM coal volume calculations using the Total Station and UAV methods. The research method was carried out by collecting data in the field, processing the digital Elevation Model (DEM), and calculating the volume using the Cut and Fill method. The results of the study the difference in volume between the two methods, where the UAV measurement results tend to be greater than those of the Total Station. The difference in Fine Coal volume was 724,15 m3 or 16,74% and Raw Coal volume of 9.335,98 m3 or 8,03%. Based on a comparison with weighing data, measurements using the Total Station provided results that were closer to the actual conditions in the field.

This study aims to analyze the effect of transformational leadership and compensation on job satisfaction with employee performance as an intervening variable at PT Cipta Kridatama Site KIM Jambi. This research employed a quantitative approach using a survey method. The population of this study consisted of all employee of PT Cipta Kridatama Site KIM Jambi totaling 862 employees. The sample was determined using the slovin formula with a 10 percent error tolerance, resulting in 90 respondents. The sampling technique used was proportionate stratified random sampling. Data were collected through structural questionnaires and analyzed using Structural Equation Modeling based on Partial Least Squares (PLS-SEM) With The Assistance Of Smartpls 3 Software.  The result of  the study indicate that transformational leadership and compensation have a positive and significant effect on employee performance. Transformational leadership and compensation also have a positive and significant effect on job satisfaction. Furthermore, employee performance has a positive adn significant effect an job satisfaction.  The mediation test result show that employee performance is able to mediate the effect of transformational leadership and compensation on job satisfaction.These findings suggest that improving job satisfaction cannot be achieved solely through leadership and compensation policies, but also requires efforts to enhance employee performance. Therefore, organizations are encouraged to implement effective transformational leadership practices and fair compensation systems to improve employee performance and job satisfaction, particularly in high demand industries such as mining services.

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

The stability of disposal slopes is a critical aspect of open-pit mining operations because it directly affects operational safety and the continuity of overburden dumping activities. Disposal areas composed of overburden materials generally exhibit heterogeneous characteristics, particularly when soft materials such as mud are present, which can significantly reduce slope stability. Therefore, a comprehensive slope stability evaluation is required prior to further disposal development. This study aims to assess the stability condition of a disposal slope under initial conditions, evaluate the influence of material conditions, and analyze the effectiveness of counterweight application in improving both the safety factor and disposal capacity. The research methodology involved the collection of primary and secondary data, including slope geometry, lithological conditions, and the physical and mechanical properties of disposal materials obtained from laboratory testing and company technical data. Slope stability analysis was performed using the limit equilibrium method with the assistance of geotechnical software, taking into account groundwater conditions and operational loading. The analysis results indicate that the initial disposal condition yielded a safety factor of 0.718, indicating an unstable slope condition. After simulating the removal of mud material, the safety factor increased to 0.907 but remained below acceptable stability criteria. The application of a counterweight significantly improved slope stability, resulting in a safety factor of 1.498. Further optimization through slope geometry redesign produced a final safety factor of 1.101, which satisfies the requirements stipulated in KEPMEN ESDM No. 1827 K/30/MEM/2018. Additionally, the redesign increased the disposal capacity from 119,507,864.23 LCM to 119,682,378.22 LCM, representing an increase of 174,513.99 LCM. These results demonstrate that counterweight application combined with geometric optimization is effective in enhancing both slope stability and disposal capacity.

Regional economic development requires a comprehensive understanding of the structure, potential, and dynamics of economic sectors so that formulated policies can be targeted and sustainable. Bojonegoro Regency as one of the regions in East Java Province has unique economic characteristics with the dominance of certain sectors, so it is necessary to conduct an in-depth analysis of the economic sectors that play a role in driving regional growth. This study aims to identify basic and non-basic sectors, analyze the dynamics of changes in economic sectors, and assess the sectoral competitiveness of Bojonegoro Regency compared to East Java Province. This study uses a quantitative approach with secondary data in the form of Gross Regional Domestic Product at constant prices by business field obtained from the Central Statistics Agency. The analytical methods used include Location Quotient, Dynamic Location Quotient, and Shift Share. The results show that the mining and quarrying sector remains the sector with the most dominant relative advantage in the economic structure of Bojonegoro Regency. However, the analysis of dynamics and competitiveness indicates that several non-extractive sectors are starting to show faster development and growth potential. This finding suggests an opportunity for transformation of the regional economic structure towards a more diverse pattern. The implications of this research emphasize the importance of regional economic development strategies that do not only rely on traditional leading sectors, but also encourage the development of more sustainable potential sectors.

Regional economic development requires understanding the structure and performance of economic sectors to create effective policies. PadangiCity, the capital of West SumatraiProvince, plays a strategic role in the regional economy. However, differences in sector contributions and growth indicate structural imbalances that need attention. This studyiaims to identify leading and potential economic sectors in Padang City to support sustainable development planning. The study uses Location Quotient (LQ), iDynamic Location Quotient (DLQ), and the Growth Ratio Model (GRM) to analyze secondary data on GrossiRegional Domestic Product (GRDP) at constant 2010 prices from 2020 to 2024, sourced from the CentraliBureau of Statistics of Padang City and West Sumatra Province. LQ results show that most sectors in Padang City are base sectors, especially business services, transportation and warehousing, ifinancial and insurance services, real estate, and wholesale and retail trade. DLQ analysis indicates that mining and quarrying, trade, transportation and warehousing, iinformation and communication, and health and social services have higher growth prospects than the reference region. GRM results show that trade, information andicommunication, real estate, health services, andiother services are leading sectors with good performance and growth potential. In contrast, agriculture, manufacturing, and construction are still lagging sectors. These findings highlight a structural shift in Padang City’s economy toward service-sector dominance and underline the need for sustainable, inclusive, and adaptive development policies to support long-term economic growth.

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

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

Football in Indonesia is not merely a sport but a social phenomenon that triggers massive public emotional engagement. Every result of the Indonesian National Team, particularly in prestigious events such as the FIFA World Cup Qualifiers, often generates waves of opinion in digital spaces filled with criticism, support, and disappointment. The failure of the Indonesian National Team in the 2026 FIFA World Cup Qualifiers has become an important momentum to systematically understand public perception. The purpose of this study is to identify the distribution of public sentiment and to reveal the main issues most frequently discussed through keyword visualization, thereby providing an overview of societal reactions. This research utilizes 971 comments from the TikTok platform as the primary dataset, collected through a crawling process and processed using text mining stages before being classified with a sentiment analysis method. The findings indicate a sentiment distribution dominated by Neutral at 63.67%, Negative at 32.36%, and Positive at 3.97%. The word cloud visualization highlights dominant keywords such as “Pecat” (Dismiss), “Evaluasi” (Evaluation), and “PSSI” (Indonesian Football Association), reflecting public criticism of managerial aspects, although positive words such as “Semangat” (Spirit) and “Dukung” (Support) also appear, emphasizing supporter loyalty. These results contribute to an empirical understanding of the issues most highlighted by the public and the distribution of collective emotions, which can serve as a basis for PSSI and stakeholders in formulating communication strategies, policy evaluations, and improvements to the national football management system.

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

Tax Management refers to a company’s efforts to manage its tax obligations efficiently and legally in order to optimize net income. This study aims to examine the effect of Fixed Asset Intensity and Leverage on Tax Management, with Profitability as a moderating variable, in mining companies listed on the Indonesia Stock Exchange (IDX) for the 2021–2024 period. This research is conducted because tax management practices are considered to potentially influence corporate profitability and financial performance. The study is grounded in Agency Theory and employs a quantitative approach. The sample was selected using purposive sampling, resulting in 28 companies observed over four years, with a total of 112 secondary data observations obtained from annual reports or financial statements. Data analysis was performed using EViews 13 with a Moderated Regression Analysis (MRA) approach. The findings indicate that: (1) Fixed Asset Intensity has no significant effect on Tax Management; (2) Leverage has a significant negative effect on Tax Management; (3) Profitability does not moderate the relationship between Fixed Asset Intensity and Tax Management; and (4) Profitability strengthens the effect of Leverage on Tax Management.

Mining companies are increasingly required to demonstrate environmental, social, and governance (ESG) accountability through sustainability reporting (SR). However, empirical evidence regarding the impact of SR on firm value in Indonesia’s mining sector remains inconsistent. This study aims to systematically examine the relationship between sustainability reporting and firm value using legitimacy theory as the conceptual framework. A Systematic Literature Review was conducted following the PRISMA 2020 protocol, employing narrative and thematic synthesis. Peer-reviewed articles published between 2018 and 2025 were retrieved from Google Scholar, Garuda Portal, and SINTA databases using relevant keywords. From 4,260 initial records, 11 studies met the inclusion criteria after screening, deduplication, and quality appraisal using an adapted CASP checklist. The findings reveal three dominant patterns: most studies report a positive effect of SR on firm value through improved transparency, corporate reputation, and investor confidence; several studies find no significant relationship due to short-term investor orientation; while a minority report negative effects associated with low disclosure quality and greenwashing concerns. Furthermore, the effectiveness of SR is influenced by disclosure quality, corporate governance, profitability, and leverage. This study implies that sustainability reporting can enhance firm value when disclosures are credible, consistent, and material, supporting legitimacy theory and encouraging alignment with the GRI 14: Mining Sector 2024 standard.

Pembangunan Ibu Kota Nusantara (IKN) merupakan salah satu proyek strategis nasional yang banyak mendapatkan perhatian publik karena dampaknya yang luas pada aspek sosial, ekonomi, dan politik di Indonesia. Media sosial, khususnya TikTok, memainkan peran penting dalam membentuk dan merefleksikan opini masyarakat, sehingga menjadi sumber data yang relevan untuk analisis sentimen secara real-time. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap progres pembangunan IKN menggunakan algoritma Naive Bayes. Komentar-komentar TikTok yang terkait dengan IKN dikumpulkan dan melalui beberapa tahapan preprocessing, seperti case-folding, cleansing, tokenization, stopword removal, dan stemming. Data yang telah diproses kemudian diberi label sentimen secara manual (positif, negatif, dan netral) sebelum digunakan untuk melatih model klasifikasi. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu memberikan performa yang efektif dalam mengklasifikasikan sentimen publik terkait IKN, yang ditunjukkan melalui metrik evaluasi seperti accuracy, precision, recall, dan F1-score. Distribusi sentimen mengungkapkan bahwa persepsi publik cenderung beragam, dengan sebagian besar komentar bernada skeptis terhadap kesiapan pemerintah dan kelayakan proyek, sementara sebagian lain menunjukkan optimisme terhadap manfaat ekonomi jangka panjang. Penelitian ini memberikan kontribusi dalam memahami respons publik terhadap proyek infrastruktur nasional di era digital serta membantu pemangku kepentingan dalam meningkatkan strategi komunikasi publik.

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.

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 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.

Gaya hidup sehat (GHS) memiliki peran penting dalam menjaga kesejahteraan fisik dan mental, khususnya pada kalangan mahasiswa dan pekerja. Namun, tren gaya hidup modern yang semakin bersifat sedentari telah secara signifikan meningkatkan risiko munculnya berbagai masalah kesehatan seperti stres, penurunan konsentrasi, obesitas, dan penyakit kronis. Penelitian ini bertujuan untuk mengidentifikasi dan mengelompokkan individu berdasarkan profil gaya hidup dan kualitas tidur mereka menggunakan metode K-Means Clustering. Variabel yang dianalisis mencakup durasi tidur, tingkat stres, aktivitas fisik, indeks massa tubuh (BMI), serta pola gangguan tidur. Analisis dilakukan dengan memanfaatkan dataset Sleep Health and Lifestyle untuk mengungkap pola tersembunyi dalam kebiasaan perilaku dan kesehatan responden. Proses pengelompokan menghasilkan tiga kelompok utama, yaitu: (1) individu dengan gaya hidup sehat optimal yang ditandai dengan tidur yang cukup, pola makan seimbang, dan aktivitas fisik teratur; (2) individu dengan risiko sedang yang memiliki kebiasaan hidup tidak teratur dan tingkat stres menengah; serta (3) individu berisiko tinggi yang dicirikan oleh kualitas tidur yang buruk, stres tinggi, dan kebiasaan hidup yang kurang sehat. Temuan ini menunjukkan bahwa algoritma K-Means efektif dalam mengklasifikasikan individu ke dalam kelompok gaya hidup yang bermakna, sehingga mampu memberikan representasi yang akurat terhadap profil kesehatan populasi. Hasil penelitian ini diharapkan dapat membantu lembaga pendidikan, organisasi kesehatan, dan tempat kerja dalam merancang program promosi kesehatan yang lebih terarah dengan menekankan pengelolaan tidur yang baik, nutrisi seimbang, serta aktivitas fisik rutin untuk meningkatkan kesejahteraan dan produktivitas secara keseluruhan.

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.