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Mining roads are one of the most important facilities that greatly affect the smoothness of production and affect mining costs. Therefore, the construction of mining roads must be carried out carefully to meet technical and work safety standards. Road conditions, distance, road slope and road carrying capacity will greatly affect the production capacity of heavy equipment, especially the production capacity of transport equipment. The location of this research is at PT. Energi Cahaya Industritama, precisely in Palaran sub-district, Samarinda city, East Kalimantan province. After observations were made at the research location, it was found that the geometric conditions of the road did not comply with the standards based on the overburden transport equipment or the largest transport equipment, for example, the width of the road on bends and the slope of the road. After conducting the research, the results of the data from the geometry of the access road were obtained, namely, the actual condition of the geometry of the access road on the straight road width is divided into 6 segments with a width of 13.3 meters, 14.3 meters, 14.5 meters, 13.2 meters, 15 meters, 15.7 meters. Meanwhile, the width of the bend road is also divided into 2 segments with a width of 15.9 meters and 15.5 meters respectively. And on the slope of the road there are 2 segments with values ​​of 11.1% and 14.8% respectively. Based on the results of the simulation software, the productivity of the transport equipment was obtained at 92.42 BCM/hour, which shows that productivity has increased by 12.23 BCM/hour from the actual productivity value of 80.109 BCM/hour. It has an average cycle time of 10.33 minutes, which shows a reduction in time or 6.01 minutes faster than the actual average cycle time of 16.34 minutes.

This study analyzes the application of production theory in the economy of East Kutai, especially in the mining and oil palm plantation sectors. The main focus is on the basic principles of production theory such as the law of diminishing returns, economies of scale, and isoquant curves, and their relevance in improving production efficiency. The study also considers the perspective of Islamic economics which emphasizes sustainability and justice in production. The results of the study indicate that the application of appropriate production theory can reduce costs and increase output, as well as support sustainable and inclusive economic growth in East Kutai.

Clustering is one of the methods in data mining used in grouping data based on certain characteristics. This study aims to compare the performance of the K-Means Clustering and Hierarchical Clustering methods in clustering Senior High Schools (SMA) in Surakarta based on the parameters of the number of students, facilities, accreditation scores, and school achievements. In this study, a comparison was made between two popular clustering methods, namely K-Means Clustering and Hierarchical Clustering, to group Senior High Schools (SMA) in Surakarta City based on various relevant attributes. The attributes used include graduation rates, number of students, teaching quality, and available facilities. The results of the study show that both methods have their own advantages and disadvantages. K-Means is more efficient in terms of processing time, while Hierarchical Clustering provides a deeper understanding of the structure of relationships between SMAs. K-Means clustering provides better clustering results in terms of separation between clusters, with a higher Silhouette Score (0.52) and a lower Davies-Bouldin Index (0.88). This shows that K-Means is more efficient and better in clustering SMA based on the given data.

Abstract. From the perspective of Micro, Small, and Medium Enterprises (MSMEs), fluctuations in raw material prices are highly concerning as they can significantly impact business stability. While MSMEs may tolerate price fluctuations to some extent, from an industrial engineering perspective, such a passive approach contradicts the principles of continuous improvement. This study seeks to predict the price of large red chili peppers using five regression models implemented through Orange Data Mining: Linear Regression, Support Vector Machine, Decision Tree, k-Nearest Neighbors (kNN), and Gradient Boosting. Due to the limited availability of daily data, particularly within a daily timeframe, the study utilized weekly data spanning three years. The results of the Test and Score evaluation shows Gradient Boosting as the best-performing model, achieving a Mean Absolute Percentage Error (MAPE) of 0.7%. However, the MAPE for predictions in January 2025 increased to 15.8%. This error is expected to decrease as more weekly data becomes available to mitigate the inaccuracies inherent in this model. Keywords: prediction, red chilli, regression, supervised learning , orange data mining. Abstrak. Dalam perspektif UMKM, fluktuasi harga bahan baku adalah suatu hal yang paling ditakuti karena berakibat pada ketahanan usaha yang menjadi tidak menentu. Pada suatu kondisi, fluktuasi harga dapat diterima para UMKM, namun dalam perspektif teknik industri, sikap UMKM tersebut tidak sesuai prinsip continuous improvement. Penelitian ini mencoba untuk memprediksi harga cabai merah besar dengan menggunakan 5 model regresi dibantu Orange Data Mining. Yaitu Linear Regression, Support Vector Machine, Tree, kNN, Gradient Boosting. Data yang diperlukan sebagian besar tidak tersedia, khususnya dalam kerangka waktu harian sehingga penelitian ini menggunakan data mingguan selama 3 tahun. Hasil Test and Score menunjukkan model Gradient Boost terpilih menjadi model terbaik dengan tingkat MAPE 0.7% namun MAPE pada tahap Prediction di bulan Januari 2025 menjadi 15.8%. Error tersebut akan berkurang ketika data mingguan sudah cukup banyak untuk menambal kesalahan yang dihasilkan model ini Kata kunci: prediksi, cabai merah, regression, supervised learning , orange data mining.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

The Industrial Revolution in the late 18th to 19th century in England and Europe marked the beginning of the widespread use of fossil fuels in manufacturing, mining, and agriculture. By 2015, global fossil fuel consumption had reached 93.7 million barrels per day, with an average increase of 1.9 million barrels per year, driven by the rapid growth of the transportation and oil industries. However, 20 of the world's largest fossil fuel-producing countries, including Germany, plan to increase production by up to 110% by 2030, exceeding the limit required to restrict global warming to 1.5°C. This production level is 69% higher than needed to keep global temperatures below 2°C. These conditions have triggered protests, including acts of vandalism by the activist group "Letzte Generation" in Germany, as a form of resistance against the massive use of fossil fuels.

Disputes in share divestment in Indonesia, such as the case between the Indonesian Government and PT. Newmont Nusa Tenggara, reflect the complexity of law and economy. This dispute was triggered by differences of opinion regarding the payment mechanism for shares sold by PT. Newmont Nusa Tenggara. The government rejects the payment scheme using funds from foreign capital owners such as PT. Bumi Resources Tbk, PT. Newmont Indonesia Limited, and Nusa Tenggara Mining Cooperation, because it is considered not in accordance with national interests. This study uses the Legal Research method with a normative juridical approach as explained by Prof. Peter Mahmud Marzuki. This conflict is caused by the weakness of the Work Contract, the unclear dispute resolution mechanism, and the lack of legal certainty regarding share divestment in Indonesia. The government and PT. Newmont Nusa Tenggara remain adamant with their respective schemes—the government wants national financial resources, while PT. Newmont offers foreign capital loans. This complicates communication and joint solutions. The main issues discussed are the legality of purchasing shares using foreign loans and the need for DPR approval in the divestment of shares in closed business sectors such as coal mining.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

In today's era, there are already many companies that have been established, from urban to rural areas, various companies have been established, especially companies that provide daily necessities such as supermarkets. And each company competes with each other in selling its products with the expected results. In this study, researchers use data to support this study. Because sales of goods or product stock can be calculated in sales results, the higher the sales, the higher the risk that will be faced. This study aims to apply data mining in analyzing sales results that occur in supermarkets. And to find out its impact on sales. This researcher uses the KNN method, by looking for test results with this method which will be implemented using the Rapid Miner application which will later produce the results of its analysis.

This research aims to identify the presence of metal elements in sandstone, laterite and clay minerals from Nafri Village, Abepura, Jayapura. The method used is X-Ray Fluorescence (XRF) at the Hydrology and Hydrogeochemistry Laboratory, ITB Bandung. Three field samples (ST-1, ST-2, ST-3) showed significant metal content. ST-1: contains, Fe(28.7%), Mn(0.0761%), Ti(0.692%), Ni(0.0297%), Cr(0.0907%), Na(trace), Mg(1.95%), Al(21.5 %), Cu(0.0565%), Zn(0.0294%), Rh(0.554%) ;ST-2 contains Na(0.493%), Mg(1.07%), Al(4.55%), K(0.801%), Ca (62.6%), Ti(0.364%), Mn(0.148%), Fe(14.0%), Cu(0.0342%), Sr(0.218%), Ag(0.135%); and ST-3 contains Na(0.625%), Mg(1.38%), Al(3.87%), K(0.937%), Ca(64.5%), Ti(0.409%), Mn(1.38%), Fe(7.37%). %), Sr(0.305%). The results show potential metal resources, particularly nickel and iron, in the area for further exploration and mining. This research provides important basic information for the study of geology and resource management in Jayapura

In today's era, there are already many companies that have been established, from urban to rural areas, various companies have been established, especially companies that provide daily necessities such as supermarkets. And each company competes with each other in selling its products with the expected results. In this study, researchers use data to support this study. Because sales of goods or product stock can be calculated in sales results, the higher the sales, the higher the risk that will be faced. This study aims to apply data mining in analyzing sales results that occur in supermarkets. And to find out its impact on sales. This researcher uses the KNN method, by looking for test results with this method which will be implemented using the Rapid Miner application which will later produce the results of its analysis.

People's mining activities in Gamey Village, Uwapa District, Nabire Regency, Central Papua Province are carried out to meet daily needs and improve the welfare of the local community. This mining activity is based on alluvial deposit conditions and is applied for mining sediments on the surface and using alcon machines. for high pressure water spray. This community mining activity is divided into several stages starting from preparation of mining land, processing and production processes as well as selling gold products for sale. This research aims to examine the gold processing used by small-scale miners in Uwapa District, Gamey Village, and its impact on the environment and public health. The main focus of this research is on the use of the sluice box method, which is the gold processing technique most commonly used by local communities. This method was chosen for its simplicity and low cost, although the gold recovery efficiency varies between 50-70%. Additionally, this research evaluates alternative technologies such as shake tables and centrifuges that can increase gold recovery efficiency and be more environmentally friendly. The research results show that the implementation of alternative technology requires training, procurement of adequate equipment and infrastructure as well as ongoing monitoring. By adopting better technology, it is hoped that gold mining activities in Gamey Village can run more productively and sustainably, improving community welfare without damaging the environment.

This study aims to determine 1) the effect of implementing green accounting on the company's financial performance, 2) the effect of corporate social responsibility on the company's financial performance, and 3) the effect of implementing green accounting and corporate social responsibility on the financial performance of mining companies listed on the Indonesia Stock Exchange in 2020-2022. The method in this study uses a quantitative method. The data sources used in this study are secondary data types and the sampling technique uses the purposive sampling method by obtaining 10 companies out of 63 companies with a total of 30 annual reports listed on the Indonesia Stock Exchange in 2020-2022 which will be the object. Data analysis used in this study is descriptive statistical analysis, classical assumption tests, and simple regression tests. The results of this study indicate that the implementation of green accounting has a positive and significant effect on the company's financial performance, corporate social responsibility has a positive and significant effect on the company's financial performance and the implementation of green accounting and corporate social responsibility also simultaneously has a positive and significant effect on the company's financial performance

Indonesia is an archipelagic country that has abundant mineral wealth, both metal minerals and other minerals. Metal minerals, including gold, tend to have a higher economic value. Based on data from the Ministry of Energy and Mineral Resources, the world's gold reserves in 2020 were 50,300 tons of Au. Indonesia is among the 5 largest in the world with 5% of the total gold reserves, which is 2,600 tons of Au. Therefore, it is necessary to develop technology and science in the mining industry, especially gold and silver, to improve the optimization of the process and increase the added value of the mining products themselves. One of the gold extraction processes can be done hydrometallurgically with the cyanidation method using a intensive leach test. The results of this experiment show that factors such as cyanide usage and ultra fine particle size will greatly affect the recovery of gold and silver metals.

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

This study aims to analyze and compare the financial performance of two manufacturing companies in the mining sub-sector in Indonesia, namely PT Adaro Energy Indonesia Tbk and PT Batulicin Nusantara Maritim Tbk during the period 2019-2023. The analysis was carried out using financial ratios including liquidity, operational efficiency, solvency, and profitability. The results of the study indicate that PT Adaro Energy Indonesia Tbk has better financial performance than PT Batulicin Nusantara Maritim Tbk. PT Adaro Energy Indonesia Tbk excels in the stability of the current ratio which is consistently above the safe limit, reflecting the company's ability to meet short-term obligations. In addition, operational efficiency is demonstrated through fast inventory turnover, effective management of receivables, and optimization of the use of fixed assets, all of which contribute to increased cash flow and revenue. These findings indicate the importance of good financial management to support the sustainability and competitiveness of companies in the mining sector.

Coal mining activities involve three main activities, namely loading, hauling, and dumping. This research focuses on the technical study of overburden (OB) dumping highdump activities In Pit Dump area with the aim of planning, implementing and evaluating technical, operational, economic, and HSE aspects. This research method includes field observations, stage plan making, and high dumping over water. Actual conditions show a difference in critical dumping height over water with geotechnical recommendations to minimize landslide risk and potential hazards. The study involved the collaboration of Mine Engineer, Geotech Engineer, Safety, and Production, and the use of equipment such as Komatsu HD 785 and Komatsu Dozer D155 & D85. The results showed that these changes could be carried out productively and safely, supporting coal production targets and adding economic value to the government and Mining Business License holders.

Rock mining along the Bone River, Bone Bolango Regency, contributes to infrastructure development but also has negative environmental impacts. This study aims to analyze the effects of mining activities on river environmental quality, particularly water discoloration, erosion, river widening, and material accumulation. Data obtained indicate that mining activities cause water pollution characterized by discoloration due to increased sediment and mining waste. Moreover, rock excavation accelerates erosion along riverbanks, resulting in land degradation that affects the stability of surrounding areas. Mining activities also trigger river widening, altering the river's natural morphology and increasing flood risks. The accumulation of mining residues around the river worsens conditions by obstructing water flow, causing sedimentation, and damaging aquatic habitats. The analysis highlights the need for better environmental management to mitigate the adverse effects of rock mining. Recommendations include implementing strict environmental policies, monitoring mining operations, conducting reclamation, and ensuring sustainable waste management. Additionally, involving local communities in river rehabilitation efforts can expedite environmental recovery and sustain ecosystem balance. This study is expected to serve as a reference for policymakers in managing mining activities sustainably and preserving the Bone River ecosystem.

PT Astra International BMW Semarang is a company operating in the automotive sector with 3 supporting pillars, namely Sales, Aftersales and Spare Parts for BMW car units. The availability of spare parts is one of the determining factors for consumer satisfaction with the company because if the spare parts stock is empty it will cause consumer disappointment with the company. By using spare parts sales transaction data for the period January 2019 – June 2023, totaling 52,162, it will be utilized using data mining association techniques with the a priori algorithm and the eclat algorithm. The problem in this research is how to find out consumer purchasing patterns so that there is no shortage or empty stock of spare parts in the warehouse. This research aims to determine the association of spare parts purchasing patterns in sales transactions so that partman get recommendations in making decisions about providing priority types of spare parts. This research methodology uses CRISP-DM (Cross-Industry Standard Process for Data Mining) and is implemented with the R programming language with R studio software. In 3 trials using the Apriori algorithm and 3 trials with the Eclat algorithm, The result with the highest confidence appears in a combination of 3 itemsets with minimum support 0.01 and confidence 0.9, namely if a customer buys B11.42.8.593.186 (Set oil-filter Mx) and B83.12.5.A1A.683 (Washer Cleaner) then they will also buy Z99000000333 ( BMW Engine Oil) with confidence 1.00 or 100%. From the results of this association's analysis, it can be used as advice for the management of PT Astra International BMW Semarang in managing spare parts stock.

This study aims to analyze and compare the financial ratios of three mining companies, namely PT Aneka Tambang, PT Adaro Energy, and PT Baramulti Suksessarana, in assessing company stability and growth. The analysis was conducted using a qualitative descriptive approach by utilizing financial ratio data such as liquidity, activity, solvency, profitability, and market in the 2019-2023 period. The results show that PT Aneka Tambang excels in liquidity stability, PT Baramulti Suksessarana has high efficiency in asset utilization, and PT Adaro Energy offers great growth potential with significant fluctuations in profitability. This research provides strategic insights for investors in selecting mining companies according to risk profiles and investment objectives, as well as a reference for academics in understanding the dynamics of the financial performance of the mining sector