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This study aimed to examine the ecological and socio-economic aspects of mangrove utilization in Nasem Village, Merauke Regency, Papua Selatan. The ecological assessment included mangrove species composition, regeneration potential, health status, and Importance Value Index (INP). Socio-economic data were collected through questionnaires and focus group discussions with 30 respondents. The results showed that  Avicennia marina  had good regeneration potential and dominated the community with the highest INP (1.13), while  Avicennia alba  was categorized as new regeneration with a low INP (0.31). The health status of both species was classified as rare/damaged according to national standards, indicating the need for restoration. From a socio-economic perspective, 90% of respondents utilized mangroves for firewood and fisheries, and 95% stated that mangroves significantly contributed to household income, although 83% earned less than IDR 1,000,000 per month. Most respondents (85%) recognized the ecological functions of mangroves, but anthropogenic pressures such as sand mining remained major threats. These findings highlight the urgent need for community-based mangrove management that integrates ecological restoration with sustainable economic development. The study provides scientific evidence to support policy recommendations for sustainable coastal zone management in Merauke.

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.

This study aims to obtain empirical evidence regarding the effect of Environmental, Social, and Governance (ESG) disclosure on firm value. The research sample was obtained using purposive sampling on mining firms listed on the Indonesia Stock Exchange (IDX) during the 2020–2023 period, with a total of 102 observations. Data analysis was conducted using panel data regression to test the proposed hypotheses. The results show that environmental disclosure has a significant positive effect on firm value, while social and governance disclosure have a significant negative effect. The theoretical implication of this study refers to agency theory, which asserts that information transparency through ESG can reduce information asymmetry between management and shareholders. However, if disclosure is carried out merely as a formality or symbolic practice, it may instead generate agency costs that are detrimental to the firm. In addition, these findings are also relevant to signaling theory, in which environmental disclosure can serve as a positive signal of a firm’s commitment to sustainability practices, thereby enhancing investor trust and strengthening the firm’s reputation. Practically, this study contributes to providing a more comprehensive understanding for firms, management, investors, and other stakeholders, while also serving as a reference for future research on ESG and firm value.

Shallots are one of the most strategic horticultural commodities in Indonesia, with high demand and varying production levels across regions. Differences in productivity between areas often create challenges in managing distribution and formulating national food policies. This study aims to analyze shallot production data in Indonesia by applying the K-Means Clustering algorithm using Python. The production data were collected from official agricultural statistics publications, followed by preprocessing, normalization, and determination of the optimal number of clusters using the Elbow method and Silhouette Score. The clustering results show the formation of several groups representing regions with high, medium, and low production levels. Visualization of the clustering results reveals the distribution patterns of shallot production, which can serve as a basis for supporting policy formulation in the development of shallot production centers in Indonesia. Thus, the application of K-Means Clustering with Python proves to be an effective approach to provide clearer insights into regional production variations and can be utilized as an analytical tool to support decision-making in the agricultural sector.

Work safety is always a priority, especially in the mining world which has a high potential for danger. Therefore, there are many regulations that must be obeyed, including the prohibition of smoking in the cabin of the Dump Truck unit. This study aims to design a cigarette smoke detector in the cab of a dump truck as a form of warning to drivers who violate the ban on smoking in the cab. The method used in this research is a quantitative method by conducting experiments. The research conducted is to analyze the effect of smoke thickness levels (20%, 40%, and 60%) on different sensor types (MQ-4 and MQ-7). The expected result is the effect of smoke thickness variation on the response time of different sensors.

A sump is a water reservoir that functions to accommodate runoff water in the pit before the water is pumped. This study was conducted to determine the location plan and sump capacity based on the direction of mine progress and the actual estimation of runoff discharge that enters the pit at PT. Wahyu Murti Garuda Kencana, Central Kalimantan. There is a water problem that interferes with production activities because the actual sump dimensions are insufficient to accommodate the daily discharge of water that enters the sump. So that the impact on the mining front is flooded or there is waterlogging that disrupts mining activities. The progress of the mine is directed towards the north of the sump so that the distance and dimensions of the actual sump are ineffective and unable to accommodate the incoming water discharge in accordance with the direction of the mine progress due to the mining location in the pit which is easily submerged by water. The drainage condition at PT. Wahyu Murti Garuda Kencana uses the mine dewatering method which is a method of removing water that flows into the mining sump using a pump. Theoretically, by conducting direct research on the sump area of PT. Wahyu Murti Garuda Kencana, the actual sump dimensions are insufficient to accommodate the water discharge that enters the pit. The design carried out pays attention to the mining plan so that the sump design to be designed can be applied in the field in accordance with the mine design. The shape of the sump designed is a trapezoidal shape with dimensions of 80 m x 70 m cross-section, 74 m x 64 m bottom section, depth of 4 m and 55 degree tilt angle and a total sump volume capacity of 20,672 m3.

This community service program aims to enhance the understanding of Geology Engineering students at Universitas Sulawesi Tenggara regarding environmentally sustainable mining practices. The education was delivered through lectures, interactive discussions, and local case studies relevant to mining activities in Southeast Sulawesi. These methods were chosen to encourage active participation and ensure the materials were contextually relevant to the students’ local environment and future professional field. The results indicate that students gained new insights into the implementation of good mining practice, post-mining reclamation, and mining and environmental regulations, which fostered greater awareness of the environmental and social impacts of mining. In addition, the program facilitated critical thinking and encouraged students to analyze real-world mining problems from a sustainability perspective. Consequently, this program not only improved students’ environmental literacy but also developed a sense of responsibility and commitment to supporting sustainable mining practices. It is expected that the knowledge gained will influence their future decisions as professionals in the mining sector, promoting a more sustainable and responsible industry.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.

Al–Mg–Si aluminum alloys are widely utilized in engineering applications due to their low density, excellent corrosion resistance, and mechanical properties that can be modified through heat treatment. This study investigates the effect of homogenization on the microstructure and hardness of Al–Mg–Si alloys produced by the squeeze casting process. The experimental procedure involved alloy melting, squeeze casting at 76 MPa using preheated metal molds, followed by homogenization at 400 °C for 4 hours. Microstructural characterization was performed using optical microscopy to examine the dendritic morphology and measure the secondary dendrite arm spacing (SDAS). Mechanical properties were evaluated through Vickers microhardness and Rockwell macrohardness testing. The results show that homogenization increases the SDAS from 32.59 μm to 36.88 μm and decreases the volume fraction of interdendritic phases from 15.51% to 13.57%. Furthermore, microhardness decreased from 50.22 VHN to 38.58 VHN, while macrohardness decreased from 54.60 HRE to 46.64 HRE. These reductions are attributed to the partial dissolution of Mg₂Si precipitates into the aluminum matrix during homogenization. Overall, this research provides valuable insight into the optimization of initial heat treatment parameters for Al–Mg–Si alloys produced by squeeze casting. The findings highlight the role of homogenization in improving microstructural uniformity and preparing the alloy for subsequent deformation processes such as cold rolling and extrusion, particularly for structural components used in mining and heavy transportation industries.

This study examines the reclamation plan of PT. Z for the 2023–2027 period with the aim of assessing the technical feasibility, ecological effectiveness, and alignment with national reclamation regulations. A descriptive-quantitative approach was employed through field observations, analysis of geomorphological conditions, evaluation of topsoil management, assessment of revegetation performance, and review of key environmental documents. The planned land disturbance reaches 138.53 ha, with reclamation focused on the disposal and topsoil areas covering 29.67 ha, as the mining pit remains active. Reclamation activities include land recontouring, topsoil spreading, erosion control, establishment of Legume Cover Crops (LCC), and planting of local species such as Rukam. The results indicate that the implemented strategies effectively enhance soil stability, improve vegetation cover, and support ecosystem recovery. Furthermore, the reclamation program meets the key performance criteria set by the Ministry of Energy and Mineral Resources, with vegetation cover exceeding 90% and stable slope conditions. Challenges remain, particularly related to sedimentation management and the absence of pit reclamation during the active mining phase. Overall, the study concludes that PT. Z has developed and executed a reclamation plan that reflects good mining practices and environmental stewardship, contributing to the long-term sustainability of post-mining land use.

Profitability is one of the most important indicators for assessing a company's financial performance, as reflects the extent to which management efficiently manages resources to generate profits for the company. The purpose of this study was to determine the effect of working capital turnover, cash turnover, accounts receivable turnover, and inventory turnover on the profitability of mining companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. The sample was selected using a purposive sampling technique with a non-probabilistic sampling approach based on specific criteria. As a result, 36 companies qualified for this study. Data were processed using multiple linear regression analysis with SPSS version 25. The results of this study indicate that working capital turnover has a positive effect on profitability, while cash turnover has no significant effect. Meanwhile, receivable turnover has a positive effect on profitability, and inventory turnover has a negative effect on profitability. These results indicate that effective current asset management in company can increase profits, while the low contribution of cash turnover indicates that liquidity don”t always correlate with profitability, the negative impact of inventory turnover indicates the potential for decreased profits if inventory management is suboptimal.. This study confirms that working capital management has diverse impact on profitability. Working capital and accounts receivable turnover are driving factors for improved financial performance, while cash turnover does not directly impact profits, inventory turnover can negatively impact profitability if not managed effectively.

Abandoned mining sites often experience a decline in soil fertility, which impacts soil quality and agricultural sustainability. This study aims to explore the potential of Pseudomonas fluorescens in rehabilitating limestone mining lands and enhancing the growth of Sengon (Falcataria moluccana) trees grown in polybag media. Sengon was selected as a pioneer species due to its resilience in degraded soils and its ability to improve soil structure. This study used an experimental design involving eight different treatments, which included the application of manure and varying concentrations of Pseudomonas fluorescens. Evaluations were conducted on the 25th and 50th days by measuring plant growth parameters such as plant height, root length, and key soil chemical properties, including pH, organic carbon, total nitrogen, total phosphorus, total potassium, and cation exchange capacity (CEC). The results showed that the application of 15 mL of Pseudomonas fluorescens on day 50 resulted in optimal plant growth, as indicated by an increase in plant height, root length, and soil quality improvement. The application of Pseudomonas fluorescens also enhanced soil pH, organic carbon, total nitrogen, and cation exchange capacity, all of which contributed to the improvement of soil fertility in the limestone mining land. This study provides evidence that Pseudomonas fluorescens can play a role in the rehabilitation of abandoned mining lands and improve the growth of Sengon trees, contributing to the recovery of degraded ecosystems.

Traffic violations are one of the serious problems frequently occurring in various regions, including Binjai City. Various types of violations, such as disobeying road signs and markings, incomplete vehicle documents, and violations that threaten the safety of drivers and other road users, continue to increase despite preventive and repressive efforts carried out by the authorities. This condition indicates that handling traffic violations cannot rely solely on field enforcement but also requires the support of technology capable of analyzing data more comprehensively. This study aims to predict the level of traffic violations by applying the Naïve Bayes method through data mining techniques. The dataset used consists of traffic violation records in 2023 from the Binjai City Police Department, with the main variables including violations of traffic signs and markings, document completeness, and safety-related violations. The Naïve Bayes method was selected because of its ability to perform classification with good accuracy, simplicity, and efficiency in processing large amounts of data. The implementation of this research is realized by developing a web-based application using Visual Studio Code as the development environment and MySQL as the database system. The results of this study are expected to provide structured information regarding traffic violation patterns, support authorities in making more effective decisions, and serve as an alternative solution in the prevention and handling of traffic violations in Binjai City.

Mining, especially open-pit mining, can have an impact on the environment and biodiversity. PT Semen Baturaja's limestone mining has an impact on the environment. Therefore, reclamation is necessary to restore the disturbed environmental conditions. The objective of this study is to analyze the effect of Paenibacillus polymyxa bacteria on post-limestone mining soil content and sengon plant growth in polybag media. This study uses an experimental method to observe the effect of treatment application. The treatments applied consist of three bacterial doses: 15 mL, 30 mL, and 45 mL. This study will observe sengon growth with the assistance of bacteria and manure fertilizer using polybag media. The parameters observed include soil pH, plant height, root length, as well as organic carbon (C-Organik), total nitrogen (N-Total), potential phosphorus (P Potensial), potential potassium (K Potensial), and Cation Exchange Capacity (CEC).  The results of the study indicate that the application of Paenibacillus polymyxa bacteria can enhance soil nutrient content and support sengon plant growth. Soil nutrient content results show significant improvements in nutrient levels in former limestone mine soil. The best treatment was demonstrated by the 45 mL dose, which exhibited the highest increase in nutrient content.

Mining activities in Mandailing Natal Regency, particularly in Kota Nopan, Huta Bargot, and Batang Natal Districts, play a significant role in the local economy while simultaneously generating complex legal, environmental, and social problems. Despite the existence of comprehensive national regulations such as the Mineral and Coal Mining Law and regional bylaws, enforcement remains weak, as indicated by the persistence of illegal mining (PETI), environmental degradation, and conflicts of interest among various stakeholders. This study aims to analyze the dynamics of mining law enforcement by employing a juridical-empirical approach that combines the review of statutory regulations, secondary data, and field observations in three sample districts. The findings reveal that law enforcement is far from optimal due to structural constraints, including limited institutional capacity and inconsistent implementation, as well as cultural and economic factors, such as the community’s dependence on mining as a primary livelihood. Moreover, political and economic interests often weaken oversight and create selective enforcement, further widening the gap between regulation and practice. The study emphasizes that law enforcement in the mining sector should not rely solely on repressive measures but must be integrated with community empowerment, sustainable livelihood alternatives, and the strengthening of institutional governance. Therefore, reforming regional regulations, improving inter-agency coordination, enhancing law enforcement capacity, and ensuring active community participation are crucial steps to build legal, fair, and sustainable mining governance in Mandailing Natal.

Shadow puppet craft training is a strategic intervention in preserving cultural heritage and strengthening the creative economy sector in Indonesia. To ensure the effectiveness and efficiency of training, a planning approach is needed that is not only conventional, but also based on quantitative analysis and intelligent systems. This community service proposes a training planning strategy using an interdisciplinary approach involving Operation Research, Design of Experiment (DoE), Simulation, Metaheuristic Algorithms, and Data Mining. This study begins with the identification of key training variables, such as duration, number of participants, initial competency level, teaching materials, and instructor resources. Through the DoE approach, various combinations of variables are systematically tested to identify the optimal training design. Next, Simulation is used to model the dynamics of training implementation and evaluate implementation scenarios. To predict training needs and participant behavior, Data Mining techniques are applied to historical data of arts community training. In the final stage, Metaheuristic algorithms such as Genetic Algorithm and Simulated Annealing are used to solve complex and large-scale scheduling and resource allocation problems. The results of the integration of these approaches show an increase in training efficiency of up to 27% as well as increased participant satisfaction and the quality of work results. This activity demonstrates that applying a quantitative, data-driven approach to traditional crafts training planning can provide significant added value. This model can be replicated in other training programs based on local wisdom and other creative industry sectors.

Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.

The land of the former Pit B limestone mine of PT Semen Baturaja has failed to plant and grow dwarf plants at several locations due to low nutrient content, such as Nitrogen, Phosphorus, Potassium, Soil PH, C-Organic, and cation exchange capacity (KTK). This condition causes the plant not to grow optimally. This study aims to determine the effect of the application of Rhizobium sp bacteria on the growth of Sengon plants (Falcataria moluccana) using polybag media on the land. The method used was an experiment with the treatment of giving Rhizobium sp in various doses. The results of the study showed that the administration of Rhizobium sp had a real effect on increasing the growth of sengon plants. Based on laboratory observations and analysis, there is a change in soil pH from slightly alkaline to neutral after treatment. The C-Organic content increased from medium to very high, while the Nitrogen content rose from medium to high. The elements of Phosphorus, Potassium, and KTK have also increased significantly and are classified as very high. The best treatment is obtained at a dose of 30 grams of Rhizobium sp, which gives optimal results on an increase in plant height and root length. These findings suggest that the application of Rhizobium sp can be an effective biotechnology alternative in post-mining land rehabilitation, particularly in improving soil fertility and forestry crop productivity. This research contributes to environmental management and the use of microorganisms in the restoration of degraded ecosystems.

Stock returns represent a crucial parameter that serves as a reference for investors in evaluating company performance. A decline in returns has occurred in several mining companies listed on the IDX, despite the sector’s vital role in the national economy. This study aims to examine the effect of Corporate Social Responsibility (CSR), Return on Assets (ROA), Return on Equity (ROE), Debt to Equity Ratio (DER), and Firm Size on the stock returns of mining companies listed on the IDX during the 2022–2024 period. The sample was determined using purposive sampling, resulting in 56 observational data after outliers were removed. To meet the assumptions of classical tests, several variables were transformed using natural logarithms, and data were analyzed using multiple linear regression. The results indicate that CSR, ROE, and Firm Size have no significant effect on stock returns, whereas ROA and DER show a significant positive effect. These findings suggest that investors tend to emphasize financial fundamentals, particularly profitability and capital structure, rather than non-financial aspects such as CSR activities. The implication for companies is the need to enhance operational efficiency and optimize financial structures to attract investors and improve returns. Future researchers are encouraged to incorporate external variables such as global commodity prices, market risk, and macroeconomic indicators, as well as expand the observation period and apply more diverse methodological approaches to provide a more comprehensive understanding of stock return dynamics in the mining sector.

The Computer-Based National Assessment (ANBK) is an essential instrument designed to comprehensively measure student competence, including literacy, numeracy, and character aspects. However, in practice, many students still face various challenges during preparation, such as cognitive limitations, psychological readiness, and technical barriers, which affect their overall readiness to participate in ANBK. This study aims to analyze the readiness level of students at SMP Negeri 2 Kuala by employing the Rough Set method. The variables examined include digital literacy, subject matter understanding, psychological readiness, and school facility support. Data were collected from 250 ninth-grade students through structured questionnaires and subsequently processed using the Rosetta software to perform attribute reduction and generate decision rules. The findings indicate that digital literacy, subject matter understanding, and psychological readiness are the most influential variables in determining student readiness, while facility support serves only as a complementary factor. The extraction process generated seven decision rules with an accuracy level of 100%, which effectively classified students into three readiness categories: highly ready, ready, and less ready. These results confirm that the Rough Set method is highly effective for identifying dominant factors and producing decision rules that can guide schools in developing targeted strategies to enhance student readiness for ANBK.