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Soil contamination by hydrocarbons, pesticides, heavy metals, and complex pollutants is rapidly increasing and degrading essential ecosystem functions. Physical or chemical treatments offer faster results, yet they are often costly, energy-intensive, and risk disrupting soil biological integrity without fully eliminating pollution sources. Microorganism-based bioremediation provides a more sustainable alternative by utilizing microbial metabolism to degrade or immobilize pollutants into less toxic and less mobile forms. This article presents a structured literature review on the roles and applications of microorganisms for bioremediation of contaminated soils, covering comparisons between single isolates and microbial consortia, dominant biological mechanisms, and ecological challenges in field application. A Systematic Literature Review approach was applied, using narrative synthesis and thematic clustering of national and international journals published between 2020 and 2025. The review indicates that single microbial isolates are commonly selected for specific pollutant targets, whereas microbial consortia are preferred for mixed or persistent contaminants due to metabolic synergy that enhances microbial adaptability and stepwise pollutant breakdown in highly polluted soils. Adaptive mechanisms such as EPS production and biofilm formation contribute to microbial resilience under stress and help retain contaminants within the soil matrix. Key challenges identified include inoculum stability under extreme conditions and limited microbial access to pollutants trapped in micro-soil pores. The findings highlight that microbial selection strategies must be tailored to pollutant characteristics and soil environmental conditions, while also emphasizing the potential of biofilm-based systems and organic carriers to support broader field implementation of microbial bioremediation.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

Enhanced Recovery After Surgery (ERAS) is a multidisciplinary, evidence-based perioperative care approach designed to minimize the stress response to surgery, preserve organ function, and improve clinical outcomes. A substantial body of evidence has demonstrated that implementing ERAS protocols in elective procedures not only accelerates patient recovery but also reduces healthcare costs. Surgery, one of the most commonly performed medical interventions worldwide particularly major procedures such as abdominal and colorectal surgery carries a high risk of postoperative complications. These complications contribute to increased morbidity, mortality, and economic burden for both patients and healthcare systems. This situation presents a particular challenge in the era of universal health coverage, which demands efficiency in terms of time, cost, and resource utilization. ERAS implementation has been proven to enhance postoperative recovery, shorten hospital stays, and expedite the return of normal physiological function compared to conventional surgical care, especially in lower abdominal surgeries and colorectal resections. A literature review was conducted by searching relevant articles through Google Scholar using inclusion criteria such as publications from 2018 onwards, focused on ERAS in abdominal surgery, full-text availability, and academic journal sources. The data were analyzed using a matrix table comparing research methods, study populations, research locations, and reported outcomes. ERAS protocols have shown to be effective in abdominal surgical procedures for improving patient recovery and reducing postoperative complications.

This research aims to formulate a business development strategy for AK Laundry, a micro, small, and medium enterprise (MSME) located in Palembang that operates in the laundry service industry. As competition in this sector continues to intensify, MSMEs are required to adapt quickly through effective strategic planning. The study employed a descriptive qualitative approach, with data collected through interviews, direct observations, documentation, and questionnaires, ensuring a holistic understanding of both internal and external conditions. The analysis was carried out using the SWOT framework, which identifies internal strengths and weaknesses, as well as external opportunities and threats. The findings reveal that AK Laundry possesses several strengths, including good relationships with its loyal customer base, competitive pricing that appeals to a wide range of market segments, and a strategic location that facilitates accessibility. However, weaknesses were also identified, such as limited promotional efforts, particularly in digital channels, and occasional delays in completing customer orders, which may affect satisfaction and trust. From an external perspective, AK Laundry has opportunities to expand its services, particularly through the growing demand for pickup and delivery facilities, as well as changes in consumer lifestyles that increasingly prioritize practicality and efficiency. Nevertheless, the enterprise must also address potential threats, such as intense competition in pricing strategies among similar businesses and the risk of item loss, which could undermine its reputation. Based on the SWOT matrix, AK Laundry is positioned in Quadrant I, indicating that it holds considerable potential for aggressive growth. Therefore, the recommended strategies include strengthening digital marketing initiatives, introducing innovative services to differentiate from competitors, enhancing employee competencies through training programs, and upgrading equipment to improve service quality and speed. These strategies are expected to help AK Laundry leverage its strengths and opportunities effectively, ensuring sustainable development and competitiveness in the MSME laundry service sector.

According to the circular letter from the Head of the Inspectorate General (KaIT) regarding the review of mining accident cases in September 2024 and the review of mining accidents in the third quarter of 2024, the percentage of accidents occurring in workshops reached 16.13%. Over the past five years, the Plant Department of PT Saptaindra Sejati Jobsite Sera has experienced two major incidents classified as Lost Time Injury (LTI) resulting from working with lifting equipment on undercarriage components. The purpose of this study is to identify risks, analyze risk levels, and provide recommendations for risk control in the overhaul work of the PC210-10M0 undercarriage. This research applies the HIRADC method by identifying potential hazards through calculations of likelihood and severity levels to obtain the risk level using a risk matrix. Control measures are then carried out through administrative actions such as documentation and the use of personal protective equipment (PPE). The results of this study indicate a decrease in risk levels after implementing risk controls—from extreme risk to medium risk, and from high risk to low risk. Suggestions from this study include the need to develop updated HIRADC for each section, actively conduct socialization regarding Job Safety Analysis (JSA) before work, and perform inspections as well as observations related to work behavior.

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.

This study analyzes the implementation of Integrated Reporting (IR) in Indonesia based on the International Integrated Reporting Council (IIRC) framework, aiming to evaluate the trends in IR adoption over the past three years and assess the extent to which companies in Indonesia have aligned their reporting practices with IIRC standards. Using content analysis and one-way repeated measures ANOVA, this research examines integrated annual reports of companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2022 period, measuring the quality of IR implementation through a checklist derived from the International Integrated Reporting Framework (IIRF). The findings reveal that IR practices in Indonesia have significantly improved over time, with key dimensions experiencing notable enhancements, including stakeholder relationship, consistency and comparability, operating context, risk, governance, and performance, critical indicators for ensuring transparency and effectiveness in integrated reporting. Furthermore, this study contributes to the development of a matrix or quality checklist for IR, based on a normative interpretation of the IIRF, providing valuable insights into IR implementation in emerging markets, particularly Indonesia, which serves as an interesting case study since previous research has primarily focused on IR adoption in developed regions such as Europe and South Africa. Practically, this study emphasizes the importance for companies to enhance their IR reporting quality by focusing on aspects such as strategic focus and future orientation, connectivity, consistency, reliability, and comparability, thereby ensuring more transparent, accountable, and globally aligned financial and non-financial reporting practices.

This study aims to analyze the product diversification strategy implemented by Pegadaian Purwokerto Branch and its impact on the company's financial stability. A qualitative descriptive approach was applied using a case study method through structured interviews, direct observations, and internal document analysis. The research also integrates the General Electric (GE) Matrix to map the business strength and market attractiveness of each diversified product. The findings reveal that Pegadaian’s diversified products—such as gold savings, vehicle financing, and Hajj/Umrah financing—have significantly contributed to expanding market reach and increasing revenue. This success is supported by comprehensive feasibility studies, structured risk management systems, and the operational role of branch-level human resources. Nevertheless, challenges remain, particularly in service digitalization and HR readiness. This research contributes both theoretically and practically to product development strategies in financial services, especially for state-owned enterprises seeking sustainable business growth through data-driven diversification.

Decoction water betel leaf is a traditional Balinese medicine containing the active compound hydroxychavikol, has antioxidant and antidyslipemic activity. From the results of the study it was reported that decoction water of betel leaf contains the active compound hydroxycavicol (HC). The active compound hiroksikavikol has activity as an antioxidant and antidyslipidemia. As an anti-oxidant, it can scavenge ROS and inhibit the activity of free radicals. As an antidyslipidemia, it can normalize lipid metabolism by lowering total cholesterol, triglyceride, LDL and VLDL levels and increasing blood serum HDL levels. Oxidative stress and dyslipidemia are major risk factors for heart disease caused by atherosclerosis. Atherosclerosis is the occurrence of plaque formation in the lumen of blood vessels triggered by oxidative stress through endothelial cell dysfunction, inflammation and lipid peroxidation. Oxidative stress causes endothelial cell dysfunction, increased contractility, VSMC growth, monocyte invasion and lipid peroxidation, inflammation and increased deposition of extracellular protein matrix. Based on these things, it was concluded that HC loloh boiled water of betel leaf has antioxidant and antidyslipidemic activity to prevent heart disease.