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This research aims to know and find the form of oppression experienced by female characters and nature as well as how women's struggle for nature and environmental preservation in the novel Teruslah Bodoh Jangan Pintar by Tere Liye. The method used in this research is descriptive qualitative. The results of this study are the forms of oppression against nature and women in the novel Teruslah Bodoh Jangan Pintar by Tere Liye can be seen from the many forms of oppression in the novel, based on De'Eaubonne's ecofeminism theory divided into two, namely the first exploitation of nature and women such as indiscriminate land conversion, illegal logging. The second is environmental and human degradation such as poor soil quality due to mining waste and degrading human dignity. The form of women's struggle against nature and women in the novel Teruslah Bodoh Jangan Pintar by Tere Liye can be in three aspects, namely the first liberation from the  patriarchal system. Second, the protection of the environment and natural resources. Third, criticism of exploitation and capitalism.

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

This research aims to analyze the effect of solvency ratios on profitability in mining companies in Indonesia, namely PT. Bukit Asam Tbk which is a state-owned company engaged in the mining industry. The solvency ratios used are debt to Equity Ratio (X1) and Debt to Asset Ratio (X2) while the profitability ratio used is return on assets (Y). The research method used is quantitative descriptive analysis with multiple linear regression as an analysis tool. The data used comes from the company's annual report for the period 2014 - 2023. The research results show a significant influence of the solvency ratio on the profitability ratio.

Trend analysis is an analysis that compares more than 2 periods of financial statements. PT Antam Tbk is one of the largest mining companies in Indonesia. The purpose of this study was to determine the results of the evaluation of the financial performance report of PT Antam Tbk using trend analysis in the period 2020 to 2023. The research method used is quantitative descriptive method, which involves processing the company's financial data that is already available in the form of financial statements. This study uses secondary data in collecting information. The results of this study concluded that PT Antam Tbk succeeded in increasing the value of assets and equity, and decreasing the value of liabilities. As for the income statement, the company managed to increase profit after income tax by 167.7% from the period 2020 to 2023.  

Grouping is a process or activity to develop a system that is more organized and easy to understand, making it easier to analyze, identify or manage data and can also be used to explore information so that it becomes new knowledge for anyone who wants to obtain it. and in this case the information we want to explore is about MSME data in Binjai City. Namely, it is difficult to know how to identify existing business development patterns, whether they are not yet developed, less developed, already developed, and very developed. Offline and online promotions have not been optimal in increasing the growth and change of a business from time to time. And most MSMEs still don't understand how to market their products and services effectively and efficiently. MSMEs are one of the most numerous community business groups in Binjai City. To obtain this information, one solution that can be implemented is by utilizing data mining using input data in the form of Binjai City MSME data. This data will be processed using the clustering method with the k-means algorithm using MSME business type variables, sales type variables and development pattern variables. .Based on the results of grouping Binjai City MSMEs using the K-Means Clustering Method from 20 grouped data, 3 clusters and 2 iterations were obtained where cluster 1 contained 4 data and was located in the MSME business type group, namely the businesses included in this cluster were businesses in the field of Fashion, for the sales type group, uses online and offline types, and for business development patterns, it has a development pattern that has developed. cluster 2 has 11 and is located in the MSME business type group, namely the businesses included in this cluster are businesses in the culinary sector, for the sales type group the offline type is used, and for the business development pattern it has a development pattern that has developed. and cluster 3 has 5 data and is located in the MSME business type group, namely the businesses included in this cluster are businesses in the culinary sector, for the sales type group it is using the offline type, and for the business development pattern it has a less developed development pattern. so it can be concluded that the pattern of business development of Binjai City MSMEs produces relevant data so as to produce designs that can be used for this research.

Dairi Diancam Tambang, a documentary film produced by Yayasan Diakonia Pelangi Kasih together with Legal Aid and Advocacy of the People of North Sumatra (Bakumsu). The purpose of this writing is to study the life of the people in the villages of Bongkaras and Longkotan whose people professed as farmers after the existence of mining which affected the destruction of agricultural land and river lanes source of citizens' irrigation.  This study uses library studies and methods of descriptive analysis as well as the conflict theory of Karl Marx that the struggle of the proletarians in the defense of their rights seized by the bourgeoisie. The results of the research showed that there was a conflict between the public and PT DPM regarding land management permits in the protected forest area that became the site of the underground mines, as well as public concerns about their safety due to the presence of the mining company PT D PM that is in vulnerable areas of natural disasters such as earthquakes and floods, and the expectation of the village people that the government is more attentive to how the social conditions of the people to keep the earth awake.

Coal mining activities often result in acid mine drainage (AMD), which can cause environmental pollution if not properly managed. This study aims to evaluate the potential use of fly ash from the Stream Power Plant (PLTU) Tenggarong to mitigate the impacts of AMD, specifically targeting iron (Fe), manganese (Mn), and pH parameters. Acid Mine drainage is formed when sulfide minerals oxidize, producing acidic compounds that can harm the environment. This research focuses on analyzing the ability of fly ash to adsorb iron and manganese from AMD solutions, as well its capability to increase solution pH. Based on the conducted research, the optimum pH value was achieved when using 10 grams and 15 grams of fly ash in the adsorption process. The optimum concentration of iron (Fe) was attained using 10 grams to 15 grams of fly ash, while for manganese (Mn), it was achieved with 20 grams to 25 grams of fly ash. The adsorption process using 25 grams of fly ash showed the highest efficiency in reducing iron (Fe) concentration by 93.78 % and manganese (Mn) concentration by 75.47 %.

Accurate rainfall prediction is needed to improve the performance of land that always uses rainfall data. Data mining or often called knowledge discovery in databases (KDD) is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data. In predicting rainfall, there are several conditions that can be observed as reference data to predict rainfall, namely wind speed, temperature, and air humidity. In this research, a backpropagation artificial neural network prediction method is developed that can be used in predicting future rainfall. The backpropogation artificial neural network method that was built produced an accuracy value of 95.36%, a precision value of 90.50%, a recall value of 97.50% and an f-measure value of 92.00%

The insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.

PT Astra International BMW Semarang operates in the automotive sector, focusing on sales, aftersales, and spare parts for BMW cars. The availability of spare parts is crucial for customer satisfaction, as stock shortages can lead to disappointment. Using data from 52,162 spare parts sales transactions from January 2019 to June 2023, the study applies data mining techniques with the a priori and eclat algorithms to identify consumer purchasing patterns and prevent stock shortages. The research aims to provide recommendations for prioritizing spare parts stock. Utilizing the CRISP-DM methodology and R programming, the study found that the highest confidence in purchasing patterns occurs with a combination of three itemsets: if a customer buys an oil filter set (B11.42.8.593.186) and washer cleaner (B83.12.5.A1A.683), they will also buy BMW engine oil (Z99000000333) with 100% confidence. These findings can help PT Astra International BMW Semarang manage spare parts stock more effectively.

All PT Astra International BMW Semarang transactions are recorded in the database but the problem is that the stock management is  efficientless so  the part stock that buyers are interested is not available. This research aims to conduct a comparative mining results using the association rule with apriori algorithm for year 2021, 2022 and 2023 sales transaction dataset with total of 43.694 records using the Rstudio. Data mining process in each year uses the same parameters for each itemset combination. The best association pattern occurs in 2023 with support value 0.05913841 and confidence value 100%. This can be concluded that the rules formed from each year could be different eventhough using same parameters. The item that always appears in the association rule from 2021 – 2023is Z99000000333 (BMW Engine OIL) which is often purchased with items named “Set fil-oil” so it can be a recommendation for  item stocking  in the warehouse.

The increasingly rapid development of the times has opened people's views to be more open to various kinds of world and national problems. Technology, which is one of the tools for thinking, has created innovative patterns of thinking in society that are mature in determining diverse perspectives and actions, as well as every outcome of views and attitudes that is permitted as long as it does not violate the rules and norms that have been regulated in state life. Customary forests are one of the categories of social forestry which are located in the living areas of local indigenous communities and can usually be found in inland areas. Customary forests are also regulated in Law Number 41 of 1999 concerning Forestry as stated in Article 1 number 6 "Customary forests are state forests that are within the territory of customary law communities." Conflicts between company interests and the rights of indigenous communities sometimes arise due to a lack of legal protection and unequal power, this is based on expansion which can be said to be irregular because quite a few mining companies expand mining areas outside the mine itself in order to exploit very natural resources. abundant in it.

Illegal mining in Indonesia isn't it things we just heard, In fact, illegal mining has become widespread in almost every region potentially rich in minerals. Illegal mining is the most common found is mining gold. Mining gold illegal or often in short for PETI (Unlicensed Gold Mining) is a mine amount pollution material the mine most tall. In Indonesia, gold mining without a permit is considered an unlawful activity, especially because miners do not have a mining business permit as a business entity. They do not pay taxes and royalties which are sources of state income from mining activities. Their activities also often cause social unrest and environmental damage. One that has gold mines is Jambi Province, precisely in the Merangin Regency area. Gold mines in this district are not only found on land but also in river basins (DAS). Merangin Police have carried out many prevention and law enforcement efforts to deal with mining activities without permits.

Data mining is a technique for extracting new information from data warehouses, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as is the case with the Dimsum business at Dimsum Madani.toko. This is located on Jalan Lampu gg. Pelita 4, Brayan Bengkel, East Medan, the location is close to many Brayan Resident's Houses. This of course affects sales levels. Increasing daily sales activity results in an accumulation of sales transaction data that continues to increase, thereby burdening data storage. Unfortunately, this data is only stored without further processing. In fact, this data collection holds valuable information.This research uses Market Basket Analysis with the Apriori Algorithm to find association patterns based on consumer shopping behavior. The goal is to identify items that are often purchased together. The research results showed that the combination of Seaweed Dimsum with Tofu Skin Spring Rolls had the highest support value (50%) and the highest confidence (75%).

This study aims to evaluate the effectiveness of existing law enforcement related to environmental pollution in the Taluduyunu River. The method used in the study is normative and then analyzed in a descriptive qualitative. The results showed that the basis of law enforcement evaluation of the Taluduyunu River due to pollution of mining waste (Case Study Buntulia District, Pohuwato Regency). The results of this study revealed that law enforcement against river pollution due to mining waste is still not effective. Despite clear regulations regarding waste disposal, many mining companies have not fully complied with these regulations. Increased coordination between government agencies, the application of more stringent sanctions, as well as environmental awareness on the part of the industry are needed to thoroughly address this issue. To improve the effectiveness of law enforcement, there is a need for stricter policy reforms, increased capacity of law enforcement institutions, and closer collaboration between government, industry, and civil society in an effort to maintain environmental sustainability and public health affected by mining waste pollution.

Indonesia is one of the countries in Southeast Asia rich in nickel reserves, so this has become an attraction for foreign investors to compete to invest amid a drastic increase in market demand for lithium batteries for electric vehicles. Tesla, Inc. became one of the investors who showed interest in the potential for establishing a lithium-ion battery factory for electric vehicles. Indonesia is aware of Tesla's enthusiasm in trying to conduct intense negotiations and lobbying because these MNCs have several advantages in terms of advanced features and acceleration, as well as adhering to green principles. The author's aim in raising this issue is to reflect on Indonesia's long process of building government-to-business negotiations and lobbying with Tesla, where Indonesia sees this opportunity as a step to pursue national interests. The method used in this research is based on a literature study through secondary data collection. The findings from this research are that both Indonesia and Tesla use a rational approach and integrative strategy in negotiating investment cooperation. However, Indonesia's optimistic attitude is reflected in experiencing various challenges, including competition from competitors from other countries and unsustainable nickel mining problems.

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

This study aims to examine capital structure, profitability and dividend policy on firm value. This study used a sample of mining companies listed on the Indonesia Stock Exchange for the 2020-2022 period. This sampling method is using purposive sampling. Based on predetermined criteria, a sample of 17 companies was obtained. This research was conducted with a period of 3 years, bringing the total sample to 51 companies. The type of data used is secondary data taken from the company's financial statements. The analysis technique used in this study is multiple linear regression using the SPSS 26 application program. The results show that capital structure (DER) has no effect on firm value (PBV), while profitability (ROA) has a significant positive effect on firm value (PBV). And dividend policy (DPR) has a significant positive effect on firm value (PBV).    

Mining companies are one of the companies that have great prospects in the future. Mining companies are one of the corporate sectors that are targeted for investment, this causes investors to often calculate share returns for their investments in companies in this sector. Stock returns in companies tend to fluctuate or experience erratic increases and decreases in stock returns. The increase or decrease in stock returns is caused by internal and external factors, internal factors can be seen through financial reports and external factors can be seen from macroeconomic and other factors. This research is secondary data, namely it comes from financial reports of mining companies registered in JII70 for the 2018-2022 period. This research is included in the type of quantitative research, using Eviews as a statistical medium and the statistical methods used in this research are multiple linear regression analysis, panel data testing, classical assumption testing, and hypothesis testing. while the results of this research show that partially both Net Profit Margin and Price To Book Value have a positive and significant effect on Stock Returns as well as simultaneously Net Profit Margin and Price To Book Value together or simultaneously have a positive and significant effect on Stock Returns while inflation weaken the relationship Net Profit Margin and Price To Book Value on Share Returns in mining companies registered on JII70.

This research aims to determine the effect of foreign ownership tax and profitability on the implementation of transfer pricing. This research uses a quantitative approach with secondary data in the form of data obtained from mining sector companies registered on the sharia securities list for the period (2017-2022). The sampling method uses purposive sampling with a sample consisting of 7 companies. This research uses data analysis methods, namely multiple linear regression and panel data processed with the SPSS application. The results of this research show that the tax and profitability variables do not have a significant effect on the application of transfer pricing, while foreign ownership has a positive and significant effect on the application of transfer pricing.