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The processing and utilization of natural resources aims to meet all human needs in each country, both in the agriculture, fisheries, and mining sectors. Natural resource law focuses more on exploitation and utilization, while environmental law focuses on its preservation, indeed these two laws seem to contradict and there is a difference. But this is always related to each other and cannot be separated.  Downstream which refers to the development or transformation of the added value of a product or commodity from raw materials upstream to semi-finished products or finished products downstream. In other words, through the downstream program, Indonesia has the opportunity to increase revenue by managing the production process from upstream to downstream. Downstream is the main focus of the government because downstream has significant added value potential for the country's economy. The government said the key to Indonesia's economic growth reaching 6-7% lies in downstream. One of the downstream industry programs is predicted to be an important step to support the momentum of golden Indonesia 2045.

This study aims to analyze purchasing patterns in online transactions using the Apriori algorithm to support sales strategy optimization. The research was conducted on transactional data from an online store selling household and daily-use products. The Apriori method was applied to identify associations between items based on minimum support and confidence thresholds. Four experimental scenarios were tested to compare the reliability of generated rules and determine the strongest item relationships. Data preprocessing included item grouping, transaction coding, and elimination of non-frequent items. The results show several strong association rules with lift ratio values above 1, indicating meaningful item relationships. The strongest rule identified was the association between forks and spoons, forming a highly relevant combination for product bundling strategies. The findings demonstrate that the Apriori algorithm can assist online stores in planning stock, designing product bundling, and improving marketing effectiveness. The research contributes practical insights for business decision-making and highlights the significance of data mining in e-commerce environments.

Kalimantan Mandiri Sukses is a service company engaged in the distribution of mining products, especially coal and heavy mining equipment transportation services which has been operating for more than ten years in Banjarmasin. This research aims to analyze work motivation strategies in an effort to improve work performance. Apart from that, another aim is to provide insight into the factors that influence employee work motivation and provide recommendations for increasing employee work motivation and improving their work performance. The method used in this research is descriptive qualitative with data collection techniques through interviews and observation. The data were analyzed using content analysis techniques, with the respondents in this study being employees of PT. Kalimantan Mandiri Sukses was selected using purposive sampling. The research results show that there are several work motivation factors that influence employee work performance, such as incentives, recognition, social support, work environment, and career development. Incentives provided by companies in the form of salaries and bonuses are the main factors in motivating employees. Apart from that, recognition and appreciation from management, social support from colleagues and the work environment, as well as clear career development are also factors that are no less important in motivating employees to improve their work performance at PT. Kalimantan Independent Success.

This research relates to predictions of laptop sales in computer shops in Central Aceh, with a focus on laptop brands Acer, Asus, HP and Lenovo. Over the last three years, sales of these laptops have reached 1,629 units, with a monthly average of between 108 and 150 units. Business owners today prefer brands with the highest percentage of sales, but this can lead to dead stock problems. Therefore, the author proposes using data mining techniques, especially the K-Nearest Neighbor (K-NN) method, to make recommendations for the number of products to be purchased by business owners based on past sales data. The K-NN method requires complete, structured and continuous sales data. It is important to choose an appropriate K value, and other factors such as weather, seasons, promotions, and special events also affect laptop sales. K-NN models may need to be combined with other data to improve prediction accuracy. It is hoped that this research will provide academic benefits in expanding knowledge about the use of the K-NN method in sales prediction, as well as practical benefits for business owners in planning their sales strategies. The research conclusions highlight the importance of good data collection, choosing the right K value, and considering external factors in the laptop sales prediction process.      

This study aims to determine the effect of Earning per Share (EPS), Return on Assets (ROA), Return on Equity (ROE), Current Ratio (CR), Deb to Equity Ratio (DER), Price to Book Value (PBV) on Returns. Share. The object of this research is the Indonesian Stock Exchange, mining companies in the coal industry sector for the 2019-2021 period. The research data used is secondary data with the sampling method using non-probability sampling. And the data analysis method is by calculating financial performance, determining stock returns using a sample of 19 companies. The results of this research have been tested on classical assumptions in the form of normality tests, multicollinearity tests, heteroscedasticity tests and autocorrelation tests as well as hypothesis tests in the form of t tests, f tests , determination test, and multiple linear regression test. The results of this study show that ESP partially has a negative effect on Stock Return, ROA partially  has a positive effect on Stock Return, ROE partially has a negative effect on Stock Return, CR partially has a positive effect on Stock Return, DER partially has a positive effect on Stock Return, and PBV partially has a negative effect on Stock Return.    

Fatal occupational accidents in the mineral mining industry are a crucial issue that requires special attention. This research aims to analyze in-depth the factors causing fatal occupational accidents in mineral mines using a case study approach. Through detailed investigation of a specific fatal occupational accident incident, this research explores the roles of human, technical, and environmental factors that contribute to the occurrence of the accident. By uncovering the complexity of interactions among these factors, this research is expected to provide valuable insights for the mineral mining industry in efforts to improve occupational safety and health, as well as to prevent the recurrence of similar tragedies in the future.

Mining materials as natural resources that cannot be renewed and have limited quantities of course have very high economic value. The existence of this high economic value is a factor in the mining business becoming an industry, both by the government and the private sector. The application of Environmental Law to mineral and coal mining is very important, so as not to cause environmental problems. This research aims to see how environmental law is applied to mineral and coal mining. This research includes library research because the data used is mostly secondary data in the form of legal documents as well as reviewing empirical data in the field. The analysis used uses a qualitative approach. The results of this research are that mineral and coal mining needs to implement environmental law properly, to avoid wider environmental problems. The stages in implementing environmental law, starting from completing environmental impact analysis, issuing IUP, preparing mining road infrastructure and reclamation after mining.

Penelitian ini bertujuan menganalisis faktor fundamental terhadap harga saham pada perusahaan sektor pertambangan yang terdaftar di BEI tahun 2018-2022. Variabel yang digunakan dalam penelitian ini yaitu Price Earning Ratio (PER), Earning per Share (EPS), Return on Asset (ROA), Current Ratio (CR) dan Debt to Equity Ratio (DER). Jumlah populasi yang diolah sebanyak 55 perusahaan dan sampel yang digunakan dengan pengambilan teknik sampling jenuh yaitu seluruh jumlah populasi. Data yang digunakan berupa data sekunder yang diperoleh dari laporan keuangan perusahaan sektor pertambangan yang dipulibkasi di Bursa Efek Indonesia tahun 2018-2022. Penelitian ini menggunakan kuantitatif deskriptif. Analisis data menggunakan metode klasifikasi dengan algoritma decision tree. Hasil penelitian menggunakan metode klasifikasi algoritma decision tree diperoleh nilai akurasi yang tinggi dengan nilai Kappa dan AUC yang tinggi yang menunjukkan kinerja sangat baik dalam membedakan antara kelas positif dan negatif serta kesesuaian antara label kelas dengan nilai sebenarnya. Nilai precision yang tinggi menunjukkan kemampuan model mengidentifikasi dengan benar dan recall yang tinggi menunjukkan kemampuan mendeteksi dataset yang baik

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

The re-legalization of sea sand mining activities is a controversial issue that significantly affects coastal communities. These mining activities can cause environmental damage and threaten the survival of coastal communities. This research aims to investigate the impact of re-legalized sea sand mining activities on coastal communities, with a focus on ecological, social and economic losses. The type of research used in this research is qualitative by analyzing findings from relevant research results. Research results show that sea sand mining activities have had a negative impact on ecosystems and communities around the coast. Sea sand mining causes marine habitat degradation, loss of fish resources, and economic uncertainty in coastal areas. Thus, absolute action is needed to protect coastal communities from the detrimental impacts caused by the sand mining industry

Industry is an economic sector that uses machines and labor to produce goods or services. Manufacturing, mining, construction, energy, and technology are some of the industry subsectors. Each subsector has an important role in economic growth and development of the country. Industry plays an important role in meeting society's needs, creating jobs, increasing national income, and encouraging innovation. Small and Medium Industries (IKM) consist of small and medium enterprises operating in various sectors. SMEs make an important contribution in creating jobs, local economic growth and national industrial development. The government has paid great attention to the development of MSMEs through various programs and policies, as well as support from the private sector and financial institutions.    

This study aims to analyze the effect of Corporate Social Responsibility (CSR) disclosure, Environmental Costs, and Company Size on the Financial Performance of mining sub-sector manufacturing companies listed on the Indonesia Stock Exchange. In this study using the type of quantitative data with the acquisition of secondary data derived from financial statements. Sampling using Purposive Sampling technique with 32 samples in this study. The method used in this study adopts the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to analyze the relationship between CSR disclosure, Environmental Costs and Company Size. The results of this study indicate that Corporate Social Responsibility (CSR) has a positive but insignificant effect on financial performance. While Environmental Costs and Company Size have a negative and significant effect on Financial Performance.

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.

Soil is an important component in the natural environment which has an important role in supporting human life and the ecosystem as a whole. One key aspect of soil properties is its erodibility, which determines how susceptible the soil is to erosion. This research aims to assess the level of soil erodibility in the disposal area, which is the location where overburden is deposited in coal mines. Soil erodibility is influenced by a number of factors, including texture, structure, organic matter content, and soil permeability. Laboratory analysis methods are used to measure soil erodibility from samples taken from the disposal area. The results of this research provide a deeper understanding of the potential for erosion in disposal areas and provide a basis for the development of effective erosion control strategies to minimize environmental and mine operational impacts. These findings are important for the coal mining industry in managing the environment sustainably and ensuring the continuity of their operations.    

Consumer market demand for certain specifications of coal continues to increase every year. However, each coal company has a variety of coal qualities in terms of calorific value, water content, ash content, and total sulfur and so on. PT. Alamjaya Bara Pratama is a coal mining company that has several types of coal products that vary in quality. By coal blending, the aim is to obtain coal product that meet the target with a homogeneous composition and according to consumer coal quality specifications. With the linear programming method for coal blending calculations using the Production and Operation Management - Quantitave Methods software has an influence in maximizing the use of the composition quantity for each type of product (MD and HS) as well as optimizing the quality of the coal blending results. Through POM-QM, it has the advantage of being able to determine the benchmark target value for one of the desired coal quality parameters. With subbitumious coal quality as for the research results are calculations of the final quality for each Mother Vessel, one of which is MV Eastern Camelia with the planning results is (AC 5,0% adb, TS 0,90% adb, GAR 4590 kcal/kg arb) with tonnage requirements for coal products (MD = 29.727,84 MT and HS = 22.772,16 MT). Through the use of linear programming (POM-QM) in calculating coal blending planning, the results are obtained and meet each coal quality parameter limit criterion based on consumer demand.

Economic growth serves as a crucial indicator in evaluating the well-being of a country. This study analyzes the economic growth of Indonesia and Malaysia from 2017 to 2023, taking into account the impact of inflation on the achievement of economic goals. The research employs a qualitative descriptive method with a literature review through literature analysis. The results reveal that the economic structures of both countries share similarities, particularly in the sectors of agriculture, forestry, and mining. Indonesia's economic growth tends to be higher than Malaysia's during this period, despite experiencing fluctuations. The sectoral contributions to Gross Domestic Product (GDP) indicate the dominance of the industrial sector in both economies. The significance of economic growth is also considered by examining the inflation rates. Indonesia consistently experiences higher inflation rates than Malaysia, potentially indicating challenges in maintaining macroeconomic stability. Higher inflation rates in Indonesia can impact the well-being of the population and create instability in the prices of goods and services. This research provides insights into the dynamics of economic growth and inflation in Indonesia and Malaysia, as well as their implications for achieving economic development goals. The success of a country in attaining sustainable economic growth requires special attention to factors such as economic structure, sectoral contributions, and inflation stability.

With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.

The increasing development of technology and increasing buying and selling activities require every business owner to adapt to technological developments, for business owners selling or processing sales data is very important as is done by PT.XYZ household furniture items such as tables, chairs, dressers, wardrobes, sofas and many more, where sales data is still done manually, such as a lack of reviewing what products consumers need and ineffective data storage. To overcome this problem the researcher tried to implement it using one of the methods available in data mining, namely the K-Means Algorithm. –certain data groups (clusters). So, by grouping this data, the company can find out which items are selling best and which are not selling well. So that the goods in the warehouse do not pile up. From this research, the resulting output is 5 of the best-selling items, and 5 of the least-selling items. With the data processing carried out, it is hoped that it can provide solutions to the company so that they can find out which items are the best-selling and best-selling items.

This research aims to determine financial performance, operational complexity, and good corporate governance mechanisms against audit delay. The sample for this research is 66 mining companies listed on the Indonesia Stock Exchange (BEI) from 2020 to 2022. The data in this research was obtained from secondary data originating from the company's financial reports and annual reports. The analytical method used in this research is multiple linear regression analysis. The results of this research test show that Profitability, Leverage, Operational Complexity, Audit Committee, and Institutional Ownership have no effect on Audit Delay. Meanwhile, the Board of Commissioners has a positive influence on Audit Delay.

Data from the Social Security Administering Agency (BPJS) for Employment in 2021 - 2022 recorded 499,604 cases of work accidents, then the Circular Letter of the Chief Mining Inspector number: B- 6234/MB.07/DBT.KP/2023 dated 9 October 2023 regarding Increasing Vigilance in Efforts Preventing Mining Accidents and Improving Mining Safety Performance, throughout 2023, 46.9% of accidents will result in deaths due to interaction activities on mine roads. Based on preliminary studies at PT. The true Saptaindra Site SERA in 2023 found that the number of property damage accidents (equipment damage) was 11 cases and near misses were 4 cases. The aim of this research is to identify risks that can occur both in terms of humans, the environment and operational processes, then be able to analyze the level of risk and provide recommendations for controlling dangerous risks to minimize the potential for work accidents at the PT Saptaindra Sejati Site SERA. This research uses HIRA (Hazzard Identification Risk Assessment) method with the main focus being able to identify potential hazards through calculating the level of opportunity (Likelihood) and level of severity (Saverity) in order to obtain the level of risk using a risk matrix. And then control is carried out according to the work safety hierarchy, namely elimination, substitution, engineering, administration and PPE (personal protective equipment). The results of this research show that after controlling jobs with a medium risk level, there are 3 jobs including Topsoil removal, Over burden removal and Coal getting and less than 9 according to table 2.3 Risk Matrix and jobs with a low risk level. risk), namely 3 jobs including Land clearing, Drill and blast and Mine water pumping work in the sump.