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Reciprocal tariff changes imposed by the United States on its trading partner countries including Indonesia, create changes in export and import tariffs in Indonesia which result in a significant impact on the Indonesian economy, which has an impact on export and import sectors such as oil and gas, then non-oil and gas which includes processing industries, agriculture, forestry, fisheries, mining as well as consumer goods, raw / auxiliary materials, and capital goods. This research aims to analyze the impact of exports and imports in the country of Indonesia due to the reciprocal tax rate policy implemented by the United States. This research uses a qualitative approach with the type of content analysis, which involves collecting data from news articles, research reports, and relevant academic publications. The results showed that the imposition of reciprocal tariffs imposed by the United States against Indonesia caused an increase in imports from 2017 to 2020 by 1.38% in the consumer goods sector. On the other hand, the export side decreased in the oil and gas sector from 2017 to 2020 by 2.37% which resulted in disruption of economic stability in the global supply chain that created inflation, and increased imports in Indonesia resulted in competing local product prices with imported products so that many Indonesian manufacturing companies reduced production and terminated employment. From this research, it is expected that Indonesia will be able to maximize the opportunities that exist from the changes and challenges of global trade.

P is a company engaged in logistics which is located at km 13 in the city of Balikpapan. PT. P focuses on the automotive, mining, oil and gas, materials and knowledge industries, fast-moving consumer goods (FMCG), electronics and technology, food and beverages, and e- commerce. In the PT. P department productivity is often not achieved due to the many problems that occur that hinder the company's target standards. The purpose of this research is to find out what factors can affect work productivity at PT. P department. There are several factors that can affect work productivity, including organizational culture, work ability and employee turnover. This study uses primary data and secondary data. The method used is a quantitative method.The results showed that there was a significant positive effect between organizational culture on work productivity, there was a significant positive effect between work ability on work productivity, and there was a significant positive effect between employee turnover on work productivity. The results of the F and regression tests found that there was an influence of 37.1% organizational culture, work ability and employee turnover together on work productivity and the remaining 62.9% was influenced by other variables.

Stockpiles are accumulations of materials such as coal or ore stored at specific locations. Accurate stockpile volume measurement is crucial in the mining and logistics industries for inventory management and cost efficiency. Conventional methods like Real-Time Kinematic (RTK) GPS rely on surface coordinate measurements but require numerous points, especially for irregular-shaped objects. Newer technologies like Terrestrial Laser Scanners (TLS) offer high-precision alternatives by capturing thousands of points per second, expediting and enhancing the resolution of volume measurements. This study compares TLS and RTK GPS methods in measuring the volume of andesite rock stockpiles at PT Bara Tabang. This research uses a quantitative approach, involving six Ground Control Points (GCPs) measured using the Sokkia GRX2 RTK GPS on October 24, 2024. TLS generated over 8.6 million point cloud data filtered down to 35,197 points, while RTK GPS yielded 2,276 coordinates. Accuracy testing showed very small RMSE values (RMSEr: 0.008 m; RMSEz: 0.007 m), and both LE90 and CE90 demonstrated 90% confidence within a 0.012 m range. Volume calculation using Surpac software with the cut and fill method showed TLS produced a volume of 18,766 bcm (51,982 tons/m³), while RTK GPS resulted in 18,694 bcm (51,782 tons/m³), with a difference of 72 bcm or 0.211%.These results indicate that both methods offer acceptable accuracy; however, TLS provides greater data density, efficiency, and precision, particularly for complex or large-scale stockpile objects. Therefore, TLS is recommended for high-accuracy volume measurement in mining operations that require efficiency and detailed analysis.

This research discusses the implementation of PSAK 221, which regulates the impact of foreign exchange rate changes on the financial statements of PT Adaro Andalan Indonesia Tbk, particularly related to foreign currency transactions. PSAK 221 establishes the methods for recording and reporting foreign currency transactions and recognizing exchange rate differences arising from exchange rate fluctuations. In the context of PT Adaro, which operates in the mining sector and frequently conducts international transactions, implementing this standard is crucial to maintaining the accuracy and transparency of financial reports that stakeholders require. This research aims to guide PT Adaro in managing foreign currency transactions and recording the impact of exchange rate fluctuations by PSAK 221 provisions to ensure accurate, transparent, and compliant financial statements with applicable accounting standards. This research uses a qualitative descriptive method, with a document study approach, to analyze the annual financial statements of PT Adaro Andalan Indonesia Tbk. The data used consists of secondary data obtained from the company’s financial statements and foreign currency transaction records, covering 2024, to identify the application of PSAK 221 in foreign currency transactions and its impact on the financial statements.

The performance of data mining techniques has been proven accurate in many studies, but each method in data mining techniques has different accuracy depending on the type of data that is the object of research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and prediction, where each data mining technique objective has a superior method. Therefore, in this case the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this study is to compare the three methods, so that it can be determined which method has a higher accuracy value. The results of this study indicate that the Back propagation method has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.

In a context where students face increasingly complex academic choices, this work proposes a recommendation system based on Bayesian networks to guide new baccalaureate holders in their university choices. Using a dataset containing variables such as secondary school section, gender, type of school, percentage obtained, age, and first-year honors, we have constructed a probabilistic model capturing the dependencies between these characteristics and the option chosen. The data is collected at the Catholic University of Bukavu, the Official University of Bukavu, and the Higher Institute of Education of Bukavu, preprocessed and then used to learn the structure via the hill-climbing algorithm with the BIC score using R's bnlearn tool. The model enables us to estimate the probability that a candidate will choose a given stream, depending on their profile. The approach has been validated using metrics such as BIC, cross-validation, and bootstrap and offers a good compromise between interpretability and predictive performance. The results highlight the potential of Bayesian networks in constructing explainable recommendation systems in the field of academic guidance. The system produces orientation probability maps for each candidate, which can be used by enrollment service advisers, as well as an ordered list of options relevant to the candidate's profile. With a remarkable performance on a test sample of precision@k=0.85, recall@k=0.61, ndcg=0.8, and Map=0.88, it constitutes an effective lever for reducing the risk of being misdirected in universities in South-Kivu, in the Democratic Republic of Congo

In mining activities, there is often a discrepancy between the plan and the actual conditions in the field, this discrepancy was found after reconciliation at the end of the progress (month). From the mining activities carried out by PT. Jembayan Muarabara (JMB), coal roof floor data was obtained from the survey which was then used to find survey tonnage, model tonnage data and actual tonnage scales. Then from this data it can be used to obtain coal recovery, coal looses in coal cleaning, loading, and coal transportation. The results of the study obtained tonnage data from the survey of 26,201.50 tons, a model of 26,488.50 tons and an actual tonnage of 24,885.09 tons. From the data obtained, the coal recovery value at the research location was 94.98% with coal losses in coal cleaning activities of 4.52% or 1196.39 tons and in loading activities of 0.5% or 120.02 tons with a tonnage discrepancy between planning and actual in the field of 1603.41 tons.

This study aims to analyze market sentiment towards Gold Financing Products (PKE) in Islamic banking before and after the Trump Effect phenomenon using the text mining method. This technique involves extracting information from unstructured text data to then be visualized and analyzed using the Natural Language Processing (NLP) approach and a RoBERTa-based classification model. Data was collected through web scraping from the X application with the help of API and processed using Google Colab. From a total of 4,074 tweets analyzed, it was found that the majority of public sentiment was neutral (59%), followed by negative (24%) and positive (17%). This reflects the public's tendency to discuss informatively rather than emotionally, although there was a spike in negative sentiment in certain periods indicating sensitivity to global dynamics, especially the impact of the Trump Effect on gold prices. The resulting wordcloud reveals key topics such as gold prices, buying and selling activities, and institutions such as Pegadaian Syariah and BSI. Terms such as "sharia", "riba", and "principles" emphasize the importance of Islamic financial values ​​in public perception. The results of this study indicate that text mining-based sentiment analysis is effective in capturing the dynamics of public opinion in real-time and can be a strategic tool for Islamic financial institutions in responding to market changes.

The purpose of this research is to analyze the legal protection of the constitutional rights of communities affected by the C excavation mine in Sukolilo, especially the importance of protecting community rights and effective law enforcement so that people can avoid the negative impact of mining. So it is necessary to emphasize the importance of this research in providing recommendations for improving regulations or policies that protect communities in mining-affected areas. This research is descriptive research using a qualitative approach with a type of descriptive research aimed at solving problems in the present. This research will examine the constitutional rights of communities in the area of C excavation mining activities, in the Sukolilo area of Pati Regency. The data collection in this research is collected through literature study. This method aims to obtain a strong theoretical foundation and understand the development of relevant recent research, so that the analysis carried out can be more comprehensive and supported by credible scientific references. The formulation of the problems raised are: (1) How are the constitutional rights of people affected by C excavation mining activities in the Sukolilo area of Pati Regency, (2) How are the responsibilities of local governments and mining actors in providing legal protection and guaranteeing the constitutional rights of the Sukolilo community affected by C excavation mining. The results of this study indicate.

This research aims to obtain empirical evidence on the influence of Corporate Social Responsibility (CSR) and Good Corporate Governance (GCG) on financial performance. CSR and GCG are measured using data from the 2020–2022 period, while financial performance, proxied by Return on Assets (ROA), is analyzed for the 2021–2023 period. The object of this research is mining sector companies listed on the Indonesia Stock Exchange (IDX). The sample was selected using purposive sampling, resulting in 17 companies with a total of 51 observational data points over three years. The data analysis technique used in this research is multiple linear regression analysis. The results show that CSR and GCG have a positive effect on the financial performance of the companies. These findings indicate that effective implementation of CSR and GCG can enhance a company's legitimacy in the eyes of stakeholders, thereby contributing to improved financial performance in the subsequent period.

This study explores the dual impact of artisanal gold mining on economic livelihood and environmental sustainability in Tedeboe Village, Rampi District, North Luwu Regency. Employing a qualitative case study design, data were gathered through in-depth interviews with four key informants—including the village head and three local miners—and through direct field observation. Data were analyzed using Creswell’s qualitative analysis framework, encompassing coding and thematic development to capture nuanced insights. The findings indicate that artisanal gold mining has significantly improved household income, especially in meeting basic needs and funding education. However, the sector’s informal nature and high dependency expose the community to economic volatility. Moreover, despite limited use of chemicals, awareness of mercury’s environmental and health risks is rising among miners. Environmental degradation, particularly soil and water pollution, alongside potential respiratory hazards, emerges as a critical concern. The study underscores the urgent need for sustainable mining practices, stronger local regulation, and capacity-building through education and economic diversification. These findings advocate for a balanced approach to rural development—one that harmonizes economic benefits with ecological resilience and community well-being.

Rock mining is a key sector contributing to economic development, including in Gorontalo Regency. However, this activity often generates negative environmental impacts such as erosion, water pollution, and ecosystem degradation. This study aims to analyze environmental management policies in rock mining activities using a literature review approach. The research was conducted by examining relevant literature published in the last five years, including national and regional regulations, as well as case study reports. The findings reveal that Indonesia has established a comprehensive regulatory framework through Law No. 32 of 2009 and Government Regulation No. 22 of 2021, which mandate Environmental Impact Assessment (AMDAL) as a key instrument for environmental protection. At the regional level, Gorontalo Provincial Regulation No. 2 of 2019 serves as the main guideline; however, its implementation faces challenges such as weak oversight and inadequate law enforcement. A case study in Motoduto Village shows actual environmental impacts in the form of erosion and sediment load increases in the Paguyaman River, although still within acceptable limits. Additionally, some companies operate without proper AMDAL or legal permits. Therefore, strengthening institutional capacity, digitizing permit data, and engaging communities are essential steps. Policy recommendations include consistent law enforcement, implementation of post-mining reclamation, and integration of spatial data-based policies. This study underscores the importance of a holistic approach to ensure a balance between the economic benefits of the mining sector and environmental conservation.

This study aims to analyze the potential of the region in utilizing the demographic bonus in Aceh Tamiang Regency. The data used in this study is the PDRB data of Aceh Tamiang Regency and Aceh Province in 2019-2023. The data analysis method uses location question (LQ) and shift share analysis. Based on the results of the LQ analysis, it is known that there are 4 (four) business fields that can become leading sectors in Aceh Tamiang Regency and at the same time are the economic base for further development, namely the agriculture, forestry, and fisheries sectors; mining and quarrying; processing industry; and other services. Meanwhile, based on the shift share analysis, it can be seen that the sectors that have a positive impact on the utilization of the demographic bonus in Aceh Tamiang Regency are the wholesale and retail trade sector, car and motorcycle repairs. In addition to providing a good contribution to the utilization of the demographic bonus in Aceh Tamiang Regency and can increase the PDRB of Aceh Province.

Oil palm is a key commodity supporting Indonesia’s economy through exports and employment. The industry’s success depends heavily on the selection of superior seedlings, which determine productivity, crop quality, and resistance to pests and diseases. Manual selection, however, often leads to subjectivity and inconsistency due to limited human resources and genetic variation. To address this, the study applies the Naïve Bayes algorithm for classifying oil palm seedlings based on seven variables: height, stem diameter, number of leaves, leaf color, disease resistance, root growth, and fruit yield. Using an explanatory quantitative method, the study follows seven stages: identifying problems, literature review, collecting 1,000 data entries from PT Intitama Berlian Perkebunan, data pre-processing, system modeling (UML), algorithm implementation, and evaluation using a confusion matrix and black box testing. Data was split into 80% training and 20% testing. The Naïve Bayes-based classification achieved 95% accuracy and perfect recall (1.00) for the superior seedling class. However, its performance on the minority class (non-superior seedlings) was weaker due to dataset imbalance. Black box testing verified all system functions worked correctly, enabling effective and efficient use by administrators. The study concludes that Naïve Bayes improves objectivity, efficiency, and accuracy in seedling selection. Nonetheless, attention is needed on data balancing and optimization to maintain consistent performance across classes. This system shows strong potential as a decision-support tool in plantations and promotes digital transformation in agricultural processes.

Background : The capital market plays an important role in the Indonesian economy as an investment vehicle for investors and a source of funding for companies. This study aims to analyze the effect of operating cash flow, investment cash flow, and accounting profit on stock returns in mining subsector manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2018–2023 period. This finding indicates that financial information, especially operating cash flow and accounting profit, can be a reference for investors in making investment decisions. Therefore, companies are advised to increase transparency in financial reports in order to provide a positive signal to investors. Method : The research used a quantitative approach with secondary data obtained from the company's financial statements published on the IDX. The analysis techniques used include descriptive statistical tests, classical assumption tests, and multiple linear regression analysis to test the research hypothesis. Results of the study : Shows that investment cash flow and accounting profit have a significant effect on stock returns, while operating cash flow does not have a significant effect. Simultaneously, operating cash flow, investment cash flow, and accounting profit affect stock returns.   Keywords: Operating Cash Flow, Investment Cash Flow, Accounting Profit, Stock Returns, Indonesia Stock Exchange

Firm value reflects investors’ perception of a company’s success, often associated with stock price. An increase in firm value can serve as a positive signal for investors, indicating promising future prospects. This study aims to examine and explain the influence of leverage, company size, and asset structure on the value of mining companies listed on the Indonesia Stock Exchange. The study involves a sample of 77 mining companies for the period 2020–2024. Data was collected using a non-participant observation method. The analytical technique employed is multiple linear regression analysis. The results show that leverage has a positive and significant effect on firm value, company size has a negative and significant effect on firm value, and asset structure has a positive but insignificant effect on firm value.

This research is motivated by the importance of improving the quality of passport making services at the Pontianak City Immigration Office which still faces obstacles such as complicated procedures, limited quotas, lack of officer direction, and mismatches in passport collection schedules that cause public dissatisfaction. This research aims to classify the level of satisfaction of passport making services using the Naïve Bayes algorithm, measure classification accuracy, and develop a website-based system that helps evaluate and improve service quality effectively and efficiently. The method used is a quantitative approach with data collection through questionnaires, interviews, and direct observation of 205 respondents, then the data is processed using the Naïve Bayes algorithm which assumes independence between variables to classify satisfaction levels based on variables such as officer friendliness, officer ability, ease of procedure, and timeliness of service. The main findings show that the Naïve Bayes algorithm is able to classify satisfaction levels with 73% accuracy, 76% precision, 70% recall, and 73% F1-score, signaling the effectiveness of this method in identifying community satisfaction patterns. However, the results also indicate the need for improvement in user interface aspects and system responsiveness so that the system can be widely accepted and provide optimal benefits. The implication of this research is that the application of Naïve Bayes-based data mining methods can be an effective tool in evaluation and decision-making to improve the quality of public services, especially in the field of passport making, and encourage the development of interactive and empirical data-based public service information systems.

This research aims to analyze the impact of green accounting and environmental costs on the profitability of mining companies listed on the Indonesia Stock Exchange (BEI) during the period from 2020 to 2023. A quantitative descriptive method with an associative quantitative approach was employed, relying on secondary data obtained from the companies' annual reports available on the official BEI website. The study sample consisted of 11 companies, and data analysis was conducted using IBM SPSS version 29. The findings reveal that the implementation of green accounting significantly and positively contributes to the profitability of these companies. Conversely, expenditures on environmental costs did not demonstrate a significant impact on profitability. Nevertheless, when examined together, both variables collectively influence an increase in the companies' profitability.

Investment is an activity undertaken to allocate funds with the expectation of generating future returns. In a dynamic economic environment, identifying profitable investment opportunities can be a complex task. This study aims to determine potential investment opportunities in Semarang City using a classification method that facilitates business actors or investors in selecting appropriate business sectors. The study utilizes valid data to help investors make informed decisions when establishing a business in the region. Data collection was conducted through research at the Investment and One-Stop Integrated Services Agency (DPMPTSP) of Semarang City, employing a quantitative approach with the K-Nearest Neighbor (K-NN) method. The dataset was divided into training and testing sets with an 80:20 ratio. The experimental results show that the implementation of the K-NN algorithm, conducted using Google Colab, achieved an accuracy of 86% based on 60 testing data points. This demonstrates that the K-NN classification algorithm is effective and produces accurate predictions. Therefore, applying data mining classification techniques to identify investment opportunities can serve as a viable solution to support strategic decision-making for investors.their business development strategies with sector-specific prospects in Semarang City.

In his research, the author focuses on three problem formulations, (1). What are the indicators used in evaluating the success of da'wah activities? (2). To what extent does the application of da'wah activity evaluation contribute to improving the quality of da'wah? (3). What are the supporting and inhibiting factors in the process of evaluating da'wah activities? This study uses qualitative research, namely research that aims to gain a deep understanding of (Implementation of Da'wah Activity Evaluation Aimed at Improving the Quality of Da'wah) Data collection techniques used in this study include: interviews, observations, and documentation. While the data analysis used is by using qualitative descriptive analysis using inductive techniques, namely data mining in the field, then data reduction and data presentation are carried out, then conclusions are drawn. The results of the study show that: Evaluation in da'wah communication provides an understanding that preachers (D'ai) need to assess and evaluate the communication process carried out. This aims to improve and increase the effectiveness of da'wah. Evaluation of da'wah activities is an important tool for improving the quality of da'wah communication. By conducting systematic evaluations, preachers can identify strengths and weaknesses in their approach, and adjust strategies to achieve better results in the future. Implementing effective evaluation and showing how evaluation can improve the overall success of the da'wah program.