Mining Problem Research Articles

Dense subgraphs in temporal social networks

Interactions among entities are usually modeled using graphs. In many real scenarios, these relations may change over time, and different kinds exist among entities that need to be integrated. We introduce a new network model called temporal dual network, to deal with interactions which change over time and to integrate information coming from two different networks. In this new model, we consider a fundamental problem in graph mining, that is, finding the densest subgraphs. To deal with this problem, we propose an approach that, given two temporal graphs, (1) produces a dual temporal graph via alignment and (2) asks for identifying the densest subgraphs in this resulting graph. For this latter problem, we present a polynomial-time dynamic programming algorithm and a faster heuristic based on constraining the dynamic programming to consider only bounded temporal graphs and a local search procedure. We show that our method can output solutions not far from the optimal ones, even for temporal graphs having 10000 vertices and 10000 timestamps. Finally, we present a case study on a real dual temporal network.

Engineering method for predicting the displacement of the earth’s surface under the influence of stope workings of coal mines

The development of a sufficiently reliable forecast of the values of trough parameters on the earth’s surface and the displacement of undermined rocks using mathematical modeling methods is an urgent problem for mining. Not only the protection of objects on the earth’s surface depends on its successful solution, but also the choice of the location of mine workings and rational ways to protect them from the influence of rock pressure, the forecast of gas release from undermined sources, the occurrence of gas-dynamic phenomena, the rationale for rational ventilation schemes for excavation areas, the calculation of the bearing capacity supports and many other engineering tasks. The study is based on the use of experimental data obtained during the operation of a separate extraction area, respectively, at the first stage when the working face is removed from the split furnace and at the second stage above the working face when it is sufficiently removed from the open working. This makes it possible to obtain all the necessary experimental data for constructing a general scheme for the development of clearing operations and the formation of a trough for the displacement of the earth’s surface in two stages. On the basis of the studies carried out, it was found that the maximum subsidence of the earth’s surface above the cut working with a sufficient distance from the production face is equal to the subsidence above the production face at the stage of attenuation of the processes of displacement of undermined rocks and the earth’s surface. This gives grounds to make an assumption about the closeness of the displacement parameters of the stationary and dynamic semitroughs.

Mining High Utility Itemsets Using Prefix Trees and Utility Vectors

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> items generally results in a huge search space, composed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n}$</tex-math></inline-formula> itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

Ecological-economic aspects of mining thin coal seams in the Western Donbas

&#x0D; &#x0D; &#x0D; The paper draws attention to the ecological-economic aspects of mining thin coal seams in the strategic coal-mining region of the Western Donbas and studies their causes and consequences in detail. It has been analyzed and determined that as a result of coal mining with complete caving of rocks, the processes of the earth’s surface subsidence above the mine fields occur. Due to the proximity of groundwater to the surface, waterlogging of territories and a change in the landscape occur. Using analytical calculations, it has been determined that the total surface subsidence value above the mine field of one of the Western Donbas mines is 1.2 m, which, according to the monitoring of environmental authorities, correlates well with the range of subsidence values in the region of 0.7-1.4 m. The places and parameters of rock dumps accumulating on the earth’s surface in coal mines are identified and analyzed, and the sources of waste rock formations in the technological system of coal mines are also studied. It has been revealed that the largest ingress of all waste rocks to the surface is caused by the stope operations conducted with undercutting of bottom rocks – 39% and underground mining operations to prepare new coal reserves for extraction – 33%. To date, it has been found that the volumes of waste rocks generated in mines are 1.5 times higher than the volumes of their utilization, and the prospect of disturbed land reclamation is limited in time, which, with today’s mining technology, will lead to an increase in their accumulation again. Based on a comprehen- sive study of the mined coal seam structure with a complex texture, using the example of one of the mines, it has been revealed that in the formation of operational ash content, the friable roof caving amounts to 13.0%, the seam rock interlayers – 19.2%, the coal seam natural ash content – 12.8%, and the seam bottom rock undercutting – 55.0%. A close linear relationship has been determined between the value of bottom rock undercutting with a shearer based on collected mine data and the indicator of mined coal operational ash content, which confirms the greatest importance of this factor. It has been proved that the most important factor influencing the formation of the mined coal operational ash content is the undercutting of the seam bottom rocks by a shearer, and with a 2-fold decrease in the ash content of coal, its energy potential increases by 1.25 times. Possible scenarios for the coal mining development in the Western Donbas are considered and a rational direction for solving the leading ecological-economic problems of underground mining of thin coal seams is proposed.&#x0D; &#x0D; &#x0D;

Intelligent Classification and Diagnosis of Diabetes and Impaired Glucose Tolerance Using Deep Neural Networks

Time series classification is a challenging and exciting problem in data mining. Some diseases are classified and diagnosed based on time series. Such is the case for diabetes mellitus, which can be analyzed based on data from the oral glucose tolerance test (OGTT). Prompt diagnosis of diabetes mellitus is essential for disease management. Diabetes mellitus does not appear suddenly; instead, the patient presents symptoms of impaired glucose tolerance that can also be diagnosed via glucose tolerance testing. This work presents a classification and diagnosis scheme for diseases, specifically diabetes mellitus and poor glucose tolerance, using deep neural networks based on time series data. In addition, data from virtual patients were obtained through the Dalla Man and UVA/Padova models; the validation was carried out with data from actual patients. The results show that deep neural networks have an accuracy of 96%. This indicates that DNNs is a helpful tool that can improve the diagnosis and classification of diseases in early detection.

ARTEMIS ACCORDS – A NEW ERA OF SPACE LAW OR AN INITIATIVE OF THE WORLD’S SUPERPOWER?

The article below addresses the Artemis Accords. It is a non-binding act by which its signatories adopted a set of principles for space exploration. Even though it is not an international treaty, it is a controversial issue. The author presents the Artemis Accords against the background of international agreements, soft-law and domestic regulations, aiming to answer the question of their revolutionary character and of them being an agreement between states that strive for a common goal. Firstly, the author presents the problem of space mining and the related possibilities. There are numerous benefits from this new branch of economy, but there is also a risk of global conflict on the ground of a race for space resources. After this, the author presents the state of affairs of international law and tries to draw crucial conclusions on the subject of space mining. The last part of the paper is devoted to an analysis of the Artemis Accords as a non-binding agreement between states and as a new platform which allows the launch of a new era of conquest of outer space. At the end the author presents his opinion on the Artemis Accords as an instrument of a new space policy of world’s superpower and its allies.

A dynamic model of coalbed methane emission from boreholes in front of excavation working face: numerical model and its application.

China's current energy consumption is primarily fueled by coal, increasing coal mining with growing energy demand. Coal and gas outburst accidents are common problems in coal mining, and prediction methods are fundamental for preventing such accidents. The gas emission characteristics of boreholes are a combination of comprehensive coal properties and coal seam gas occurrence status; thus, the accurate prediction of gas emissions from boreholes is crucial for preventing such hazards. This paper presents a method for measuring the gas flow rate in continuous boreholes, which is used to predict outburst danger in front of the working face. The model was compared with field measurement data and found suitable for research. The effects of different initial gas pressures, different borehole radius, and different burial depths on gas emissions from boreholes were studied. The results showed that (1) initial gas pressure is the main influencing factor of gas gushing. The amount of gas emission during drilling and the attenuation of gas pressure are more sensitive to pressure. An increase in gas pressure considerably increases the amount of gas gushing out of drilling holes. (2) The increase in the drilling radius increases the generation of coal cuttings, the area of the drilling hole wall, and the degree of damage to the drilling hole wall. Consequently, the amount of gas gushing out of the drilling hole increases. (3) In situ stress occurs mainly because of the increase in gas pressure with an increase in burial depth and the increase in gas desorption caused by the increase in damage to the borehole wall. This study provides a new outburst prediction method, which involves identifying outburst hazards through the gas gushing out of the borehole. The results are expected to aid the control of underground coal and gas outbursts and ensure the safe production of coal mines.

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment.

Features of Сohen’s kappa coefficient as a measure of expert opinion agreement

Modern metrics for evaluating agreement coefficients between the experimental results and expert opinion are compared, and the possibility of using these metrics in experimental research in automatic text processing by machine learning methods is assessed. The choice of Cohen’s kappa coefficient as a measure of expert opinion agreement in the NLP and Text Mining problems is justified. An example of using Cohen’s kappa coefficient for evaluating the level of agreement between the opinion of an expert and the results of ML classification and the measure of agreement of expert opinions in the alignment of sentences of the Kazakh-Russian parallel corpus is given. Based on this analysis, it is proved that Cohen’s kappa coefficient is one of the best statistical methods for determining the level of agreement in experimental studies due to its ease of use, computing simplicity, and high accuracy of the results.

Micro-Factors-Aware Scheduling of Multiple Autonomous Trucks in Open-Pit Mining via Enhanced Metaheuristics

Traditional open-pit mineral transportation systems are typically subject to manual command, frequently leading to vehicular delays and traffic congestion. With the advancement of automation and electrification technologies, this study proposes a highly accurate scheduling method for multiple autonomous trucks in an open-pit mine. This model considers micro-level temporal and spatial factors to tackle the task of scheduling autonomous trucks within open-pit mines. The cost function of the concerned scheduling problem is a comprehensive evaluation of energy consumption, time, and output. Beyond the loading and unloading activities, the model also factors in the charging requirements of autonomous trucks in mining regions. The scheduling model integrates a Voronoi diagram search and optimal spatial path time matching, aiming to provide superior mission planning and decision-making solutions for autonomous trucks in mining regions. For an efficient solution to the scheduling problem, we propose an improved-evolution artificial bee colony (IE-ABC) algorithm. This algorithm improves the global search and re-initialization processes and conducts algorithm ablation experiments to closely examine their impact on optimization. Simulation results across various algorithms, cost function definition strategy, and encoding strategy show that our method can improve scheduling performance in energy consumption and time. Experimental results demonstrate that the proposed model and algorithm can effectively solve the scheduling decision-making problem in an unmanned open-pit mine.

Data Analytic for Healthcare Cyber Physical System

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data.

Analysis and prospects of common problems in clinical data mining of traditional Chinese medicine prescriptions

Mining data from traditional Chinese medicine(TCM) prescriptions is one of the important methods for inheriting the experience of famous doctors and developing new drugs. However, current research work has problems such as to be optimized research plans and non-standard statistics. The main problems and corresponding solutions summarized by the research mainly include four aspects.(1)The research plan design needs to consider the efficacy and quality of individual cases.(2)The significance of the difference in confidence order of association rules needs to be further considered, and the lift should not be ignored.(3)The clustering analysis steps are complex. The selection of clustering variables should comprehensively consider factors such as the frequency of TCM, network topology parameters, and practical application significance. The selection of distance calculation and clustering methods should be improved based on the characteristics of TCM clinical data. Jaccard distance and its improvement plan should be given attention in the future. A single, unexplained clustering result should not be presented, but the final clustering plan should be selected based on a comprehensive consideration of TCM clinical characteristics and objective evaluation indicators for clustering.(4)When calculating correlation coefficients, algorithms that are only suitable for continuous variables should not be applied to binary variables. This article explained the connotations of the above problems based on the characteristics of TCM clinical research and statistical principles and proposed corresponding suggestions to provide important references for future data mining research work.

Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields

Traffic flow prediction is a fundamental problem in spatiotemporal data mining. Most of the existing studies focuses on designing statistical models to fit historical traffic data, which are purely data-driven approaches and fail to reveal the underlying mechanisms of urban traffic. To address this issue, we propose the spatiotemporal potential energy field model (ST-PEF+), which applies the field theory for human mobility to interpret the underlying mechanisms of urban traffic, and introduces the theory into data-driven deep learning models. ST-PEF+ consists of a PEF extraction module and a data-driven module. Inspired by the field theory for human mobility, the PEF extraction module adopts an algorithm to decompose the grid-based traffic flow graph into several polytree-based potential energy fields (PEFs), where traffic flows from high potential locations to low potential locations, just as water is driven by the gravity field. We also provide a theoretical analysis to ensure that the polytree decomposition algorithm can decompose any traffic flow graph. In the data-driven module, ST-PEF+ learns a spatiotemporal deep learning model to predict the dynamics of PEFs. The model adopts correlation-adaptive neural network structures, which consists of a temporal component for temporal correlations and a spatial component for spatial correlations. The temporal component employs a GRU and DCN combined structure to capture both short-term autocorrelation and long-term repeating patterns of PEFs. The spatial component extends the GAT using weighted directed attention to model the asymmetric spatial structure in PEFs. The prediction results of traffic flow are finally derived from PEFs that are predicted by the spatiotemporal deep learning model. We conduct extensive evaluations on three real-world traffic datasets. The results show that our model outperforms the state-of-the-art baselines. In addition, case studies confirm that the PEFs learned in our framework can reveal the underlying mechanisms of urban traffic, thus improving the model interpretability.

STATISTICAL ANALYSIS IN SELECTING THE BEST ELECTRODE BETWEEN ALUMINUM AND IRON IN TSS REMOVAL USING ELECTROCOAGULATION

Indonesia is a country that has considerable coal reserves with coal resources of 149.01 billion tons. One of the problems of coal mining is turbidity. The turbidity is caused by the excavation of the open pit mine so that rainwater contact with the main wall. So that mud is formed, which can increase turbidity levels. The concentration of Total Suspended Solids (TSS) due to this phenomenon reaches 5,400 mg/L. Aim: This study aims to set aside TSS by electrocoagulation processing method using a batch system with eight pairs of iron electrodes arranged monopolarly. Methodology and Results: Comparisons using statistically independent t-tests showed that there was no significant difference between the iron and aluminium electrodes in the pH parameter. Conclusion, significance, and impact study:There were significant differences between iron and aluminum electrodes in the parameters of TDS and TSS. In the measured TDS and TSS parameters, the electrocoagulation process using iron electrodes has a higher value than aluminum electrodes. In the TSS removal, there is no significant difference between the two electrodes (iron and aluminium).

Analysis of Conflict Control in The Development of UNESCO Global Geopark as Sustainable Tourism in Belitung Regency

This study discusses conflict control analyze in sustainable tourism development in Belitung. The tourism development analyze were obtained using a qualitative descriptive approach. Data were collected through direct interviews with relevant stakeholders. The results of the analysis revealed three main conflicts that underlie the issues in sustainable tourism development, namely land disputes, partnership miscommunication or lack of synchronization, and mining problems. These three points give rise to various sub-issues and biased policies perceived by the local community and tourism actors and managers in Belitung Regency. Based on the findings, the researcher attempted to outline steps or strategies, including in the aspect of people: (1) Having a mindset of change and maturity, as well as concern for the next generation; (2) Willingness to introspect based on received criticism, and (3) Openness to criticism and suggestions. In the aspect of the planet: (1) Conducting education and training for the local community to raise awareness of tourism in each tourism destination. In the aspect of prosperity: (1) Establishing standardized indicators for income, price regulations, and well-structured business permits, accompanied by intensive supervision; (2) Supporting sustainable essence of alternative MSME funds; (3) Establishing government institutions to determine and regulate prices to remain competitive. In the aspect of peace: (1) Firmness in regulating various policies and avoiding overlapping.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism

The mining of frequent itemsets and association rules is a core problem in data mining and an essential task in data analysis. In this paper, we present SufRec, a new algorithm for finding frequent itemsets and association rules. We give two versions. In both versions, the mining of the frequent itemsets is decomposed as a sequence of tasks. The first version (the SufRecDep algorithm) proceeds the tasks successively, each one using the results of the previous ones. The second version (the SufRecInd algorithm) performs the tasks independently of each other. Both versions are recursive with respect to the items and have thus the advantage to be particularly efficient for updating the mining process when new items are added to the database or when others are excluded. Moreover, the task-independent processing of SufRecInd makes it very easy to build parallel versions of SufRec. We present two parallel SufRec algorithms. With the first one, P-FIFO-SufRec, the processors perform the tasks according to the order in which the items appear in the database, preserving the recursive nature of SufRec. For second one, P-PAST-SufRec, the set of items is fixed and the tasks are pre-assigned to each processor. In order to evaluate the performance of SufRec, we carry out a consequent experimental study. In particular, we compare its running times with those of well-known parallel and non-parallel frequent itemsets mining algorithms.

PID and compliance control-based anti-sticking of drill rod in underground mining

PurposeDuring drilling in coal mines, sticking of drill rod (referred to as SDR in this work) is a potential threat to underground safety. However, no practical measures to deter SDR have been developed yet. The purpose of this study is to develop an anti-SDR strategy using proportional-integral-derivative (PID) and compliance control (PIDC). The proposed strategy is compatible with the drilling process currently used in underground coal mines using drill rigs. Therefore, this study aims to contribute to the PIDC strategy for solving SDR.Design/methodology/approachA hydraulic circuit to reduce SDR was built based on a load-independent flow distribution system, a PID controller was designed to control the inlet hydraulic pressure of the rotation motor and a typical compliance control approach was adopted to control the feed force and displacement. Moreover, the weight and optimal combination of the alternative admittance control parameters for the feed cylinder were obtained by adopting the orthogonal experiment approach. Furthermore, a fuzzy admittance control approach was proposed to control the feed displacement. Experiments were conducted to test the effectiveness of the proposed method.FindingsThe experimental results indicated that the PIDC strategy was appropriate and effective for controlling the rotation motor and feed cylinder; thus, the proposed method significantly reduces the SDR during drilling operations in underground coal mines.Research limitations/implicationsAs the PIDC strategy solves the SDR problem in underground coal mines, it greatly improves the safety of coal mine operation and decreases the power cost. Consequently, it brings the considerable benefits of coal mine production and vast application prospects in other corresponding fields. Actual drilling conditions are difficult to accurately simulate in a laboratory; thus, for future work, drilling experiments can be conducted in actual underground coal mines.Originality/valueThe PIDC-based anti-SDR strategy proposed in this study satisfactorily controls the rotation motor and feed cylinder and facilitates the feed and rotation movements. Furthermore, the tangible novelty of this study results is that it improves the frequency response of the entire drilling system. The drilling process with PIDC decreased the occurrence of SDR by 50%; therefore, the anti-SDR strategy can significantly improve the safety and efficiency of underground coal mining.

Categorization of crowd-sensing streaming data for contextual characteristic detection

The growing reliance on large wireless sensor networks, potentially consisting of hundreds of nodes, to monitor real-world phenomena inevitably results in large, complex datasets that become increasingly difficult to process using traditional methods. The inadvertent inclusion of anomalies in the dataset, resulting from the inherent characteristics of these networks, makes it difficult to isolate interesting events from erroneous measurements. Simultaneously, improvements in data science methods, as well as increased accessibility to powerful computers, lead to these techniques becoming more applicable to everyday data mining problems. In addition to being able to process large amounts of complex streaming data, a wide array of specialized data science methods enables complex analysis not possible using traditional techniques. Using real-world streaming data gathered by a temperature sensor network consisting of approximately 600 nodes, various data science methods were analyzed for their ability to exploit implicit dependencies embedded in unlabelled data to solve the complex task to identify contextual characteristics. The methods identified during this analysis were included in the construction of a software pipeline. The constructed pipeline reduced the identification of characteristics in the dataset to a trivial task, the application of which led to the detection of various characteristics describing the context in which sensors are deployed.

Feature Selection based nature inspired Capuchin Search Algorithm for solving classification problems

Identification of the optimal subset of features for Feature Selection (FS) problems is a demanding problem in machine learning and data mining. A trustworthy optimization approach is required to cope with the concerns involved in such a problem. Here, a Binary version of the Capuchin Search Algorithm (CSA), referred to as BCSA, was developed to select the optimal feature combination. Owing to the imbalance of parameters and random nature of BCSA, it may sometimes fall into the trap of an issue called local maxima. To beat this problem, the BCSA could be further improved with the resettlement of its individuals by adopting some methods of repopulating the individuals during foraging. Lévy flight was applied to augment the exploitation and exploration abilities of BCSA, a method referred to as LBCSA. A Chaotic strategy is used to reinforce search behavior for both exploration and exploitation potentials of BCSA, which is referred to as CBCSA. Finally, Lévy flight and chaotic sequence are integrated with BCSA, referred to as LCBCSA, to increase solution diversity and boost the openings of finding the global optimal solutions. The proposed methods were assessed on twenty-six datasets collected from the UCI repository. The results of these methods were compared with those of other FS methods. Overall results show that the proposed methods render more precise solutions in terms of accuracy rates and fitness scores than other methods.

Laws Concerning Mineral and Coal Mining on Community Welfare from an Environmental Ethics Perspective

This research aims to (1) identify, explain how the legal politics of establishing the revision of Law Number 4 of 2009 to become Law Number 3 of 2020 (2) analyze and provide views on the juridical implications of Law Number 3 of 2020 on community welfare from an environmental ethics perspective. The research method used is based on qualitative research with a normative juridical type of research, by collecting data using library study type techniques, legal documentation, and additional interviews in Jatam and PT Selo Argodedali. As a result of the research and analysis that has been carried out, conclusions are obtained, namely; (1) differences of opinion between mining companies who believe that the revision of Law 3/2020 provides answers to mining problems. Meanwhile, the community believes that Law 3/2020 provides losses due to mining. The 2020 Mining and Coal Law is considered unfavorable to all parties, so legal politics in the formation of the law is a statement of the wishes of the state authorities regarding the law that will apply and the direction that will be developed. In fact, this law should provide welfare for the community in accordance with the mandate of Article 33 paragraph (3) of the 1945 Constitution. (2) The 2020 Minerba Law is considered not to contain articles that uphold aspects of environmental ethics because it prioritizes exploitation and depleting nature for the interests and needs of life. without thinking about nature.