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This study examines the financial performance of PT Unilever Indonesia Tbk during the 2023–2025 period by evaluating key financial indicators, namely the Current Ratio (CR), Debt to Equity Ratio (DER), Return on Assets (ROA), and Return on Equity (ROE). The study aims to assess the company's financial condition and analyze the impact of its business transformation strategy on financial performance. A descriptive quantitative approach was employed using secondary data obtained from the company's published annual financial reports. Data analysis focused on comparing financial ratio trends over the three-year period to evaluate liquidity, solvency, and profitability performance. The findings indicate that the company's financial performance experienced fluctuations during the business transformation process. Liquidity and solvency gradually improved toward the end of the observation period, reflecting stronger short-term financial capability and a healthier capital structure. Profitability also demonstrated increased efficiency in utilizing company assets, although changes in equity returns indicated adjustments in capital management during the transformation process. Overall, the implementation of the company's transformation strategy contributed positively to strengthening financial performance and improving resilience in responding to changing business conditions and market competition. This study provides useful insights for management, investors, and other stakeholders in evaluating the effectiveness of corporate transformation strategies through financial ratio analysis and highlights the importance of maintaining financial stability to support sustainable business growth.

This study aims to describe the types of meaning contained in the short story collection Ihwal Nama Majid Pucuk through a semantic study and to produce a recommendation for handout teaching material in learning short story texts for grade XI of senior high school. This research employs a qualitative approach with a descriptive method and a text-analysis model, while the data consist of word, phrase, and sentence quotations collected through documentation techniques. The main theory used is Abdul Chaer's theory of meaning, supported by Geoffrey Leech's theory of associative meaning, with data analysis following the stages of data reduction, data display, and conclusion drawing. The results show that the three analyzed short stories contain eight types of meaning, namely lexical and grammatical meaning, referential and nonreferential meaning, denotative and connotative meaning, word and term meaning, conceptual and associative meaning, idiomatic meaning, figurative meaning, as well as locutionary, illocutionary, and perlocutionary meaning. Lexical and grammatical meanings dominate the findings, while proverbial meaning is not found in the data source. The diversity of these meanings confirms that this short story collection is suitable to be used as handout teaching material for short story texts in grade XI of senior high school. Abstrak. Penelitian ini bertujuan mendeskripsikan jenis-jenis makna yang terkandung dalam kumpulan cerita pendek Ihwal Nama Majid Pucuk melalui kajian semantik sekaligus menghasilkan rekomendasi bahan ajar handout pada pembelajaran teks cerpen kelas XI SMA. Penelitian menggunakan pendekatan kualitatif dengan metode deskriptif dan model analisis teks, sedangkan data berupa kutipan kata, frasa, dan kalimat yang dikumpulkan melalui teknik dokumentasi. Teori utama yang digunakan adalah teori makna Abdul Chaer dan teori pendukung makna asosiatif Geoffrey Leech, dengan analisis data mengikuti tahapan reduksi data, penyajian data, serta penarikan simpulan. Hasil penelitian menunjukkan bahwa dalam ketiga cerpen yang dianalisis ditemukan delapan jenis makna, yaitu makna leksikal dan gramatikal, makna referensial dan nonreferensial, makna denotatif dan konotatif, makna kata dan istilah, makna konseptual dan asosiatif, makna idiomatikal, makna kias, serta makna lokusi, ilokusi, dan perlokusi. Makna leksikal dan gramatikal mendominasi temuan, sementara makna peribahasa tidak ditemukan dalam sumber data. Keberagaman makna tersebut menegaskan bahwa kumpulan cerpen ini layak dijadikan bahan ajar handout teks cerpen kelas XI SMA.

This study aims to assess the financial performance of 11 non-financial companies that conducted acquisitions in 2025 and are listed on the Indonesia Stock Exchange (IDX). Using a quantitative descriptive-comparative approach with a case study design, six financial ratios were analyzed: Current Ratio (CR), Debt to Asset Ratio (DAR), Debt to Equity Ratio (DER), Total Asset Turnover (TATO), Return on Assets (ROA), and Return on Equity (ROE). Data were obtained from consolidated financial statements as of December 31, 2024 (pre-acquisition) and December 31, 2025 (post-acquisition). The results show that the impact of acquisitions varies across companies. No consistent or significant differences were found in the CR, DAR, DER, ROA, or ROE ratios between the two periods. Meanwhile, the TATO ratio tended to decrease after the acquisition, indicating that the newly consolidated assets have not yet operated optimally. These findings confirm that the short-term financial impact of an acquisition is heavily influenced by the transaction’s funding structure, the size of the acquired entity, and the industry sector. This study contributes to the financial accounting literature on corporate acquisition strategies in the Indonesian capital market.

Political disinformation has become one of the most critical challenges in contemporary digital democracies due to the rapid expansion of social media ecosystems. This study investigates the effectiveness of machine learning approaches in detecting political disinformation across online platforms such as Twitter, Facebook, and political discussion forums. Using a qualitative research design with a content analysis approach, the study examines linguistic manipulation, emotional narratives, sentiment polarity, and behavioral communication patterns embedded in misleading political content. The findings indicate that deep learning models, particularly Long Short-Term Memory (LSTM) architectures, demonstrate superior performance in identifying contextual and semantic inconsistencies compared to traditional machine learning algorithms. The study also reveals that algorithmic amplification, echo chambers, and coordinated bot activities significantly contribute to the rapid spread of political misinformation. Furthermore, the research highlights the importance of ethical artificial intelligence governance, transparency, and digital literacy in strengthening democratic resilience and protecting information integrity within digital communication environments

The development of technology in the digital era has significantly altered consumer behavior, particularly for Generation Z, who are known as digital natives. This group possesses unique characteristics, highly relying on social media to seek product information before making purchasing decisions. This study aims to analyze digital marketing management strategies through social media in enhancing brand engagement and loyalty among Generation Z consumers, as well as to identify the key determinants of its success. The research method employed is descriptive qualitative, utilizing literature review as the data collection technique across prior scientific sources. The analysis reveals that the implementation of digital marketing strategies based on visual content and short-form videos on platforms such as TikTok and Instagram is proven to be highly effective in capturing the audience's attention rapidly. In addition to content format, collaboration with highly credible influencers and the application of authentic, responsive two-way communication serve as the main pillars in building emotional proximity and trust among young consumers. Operationally, this strategy offers cost efficiency and a broader market reach. The conclusion of this study emphasizes that the optimal integration between management information systems and interactive, adaptive communication approaches toward digital trends is the main key to winning the market and sustaining the long-term loyalty of Generation Z.

Concrete volume calculation is a crucial factor in preventing cost overruns when preparing the Budget Plan (RAB) for construction projects. In arch bridge projects, the complex structural geometry often makes conventional volume calculation methods complicated and prone to errors, particularly due to the geometric complexity of the arch beams. This study aims to analyze the comparison of concrete volume calculation results between the conventional method and the Building Information Modeling (BIM) method using Autodesk Revit in terms of cost-effectiveness. The case study was conducted on the Short Span II Pulau Balang Bridge Duplication Project in the Nusantara Capital City (IKN), Penajam Paser Utara Regency, East Kalimantan. The research objects include substructures (bore piles, pile caps, abutments, piers, and wing walls) and superstructures in the form of arch beams. The research method employs a quantitative approach by calculating concrete volumes conventionally using AutoCAD and Microsoft Excel, as well as calculating with BIM Autodesk Revit through three-dimensional modeling and Quantity Take-Off (QTO) exports. The results indicate that the BIM Autodesk Revit method generates a larger concrete volume compared to the conventional method, resulting in a higher total cost based on the BIM volume. The total cost for the conventional method amounted to IDR 142,613,245,996.12, while the BIM Autodesk Revit method amounted to IDR 143,127,208,186.60. These differences are influenced by the level of calculation detail, the simplification of shapes in the conventional method, and the precision of modeling in Autodesk Revit.

Artisanal and small-scale gold mining (ASGM) provides short-term income for rural communities but frequently creates environmental, safety, and governance problems when it operates outside formal control. This study aimed to analyze the existing condition of ASGM, identify the driving and inhibiting factors of its arrangement, describe the perceived impacts, and formulate strategic directions for ASGM arrangement in Karang Jaya District, North Musi Rawas Regency, Indonesia. A qualitative descriptive approach was applied using semi-structured interviews supported by secondary data on the study area, population, and agricultural land use. Twenty-two informants represented district officials, village officials, technical agencies, company representatives, community leaders, nearby residents, and ASGM actors. Data were analyzed through reduction, presentation, interpretation, and conclusion drawing. The findings show that ASGM development is driven by gold potential, mining history, household economic pressure, limited alternative employment, and weak field control. The main impacts include land degradation, open mining pits, landslide and occupational accident risks, and mercury-related water and soil contamination. The recommended strategy combines alternative livelihood development, skills training, local economic institutional strengthening, gradual area monitoring, land rehabilitation, and mercury-risk education.

Environmental-based diseases remain a major public health issue associated with unsafe drinking water, inadequate sanitation, poor hygiene behavior, improper household waste management, and unsafe domestic wastewater disposal. Community-Based Total Sanitation, known in Indonesia as Sanitasi Total Berbasis Masyarakat or STBM, is a community empowerment approach designed to change hygiene and sanitation behavior through five pillars. This article aims to develop an STBM program planning design and an impact evaluation model for reducing environmental-based diseases, particularly diarrhoeal disease. This study used a narrative review method by analyzing national regulations, official guidelines, global WASH reports, and recent scientific literature. The proposed program design includes situation analysis, multisectoral advocacy, community triggering, household mentoring, strengthening sanitation facilities, monitoring, verification, and impact evaluation. The evaluation model uses process, output, outcome, and impact indicators, with diarrhoea incidence as the main measurable health indicator. The findings indicate that an integrated STBM program supported by community participation, valid baseline data, and periodic evaluation can strengthen the prevention of environmental-based diseases. This article recommends implementing STBM as a sustainable community-based public health intervention rather than a short-term sanitation activity. 

Bank stock price prediction is an important topic in the application of information technology because stock price movements are dynamic, sequential, and influenced by historical market patterns. This study aims to predict Indonesian banking stock prices using the Long Short-Term Memory method and evaluate the effect of Bayesian Optimization on model performance. The data used in this study consists of daily historical stock data of BBCA, BBNI, BBRI, BBTN, and BMRI from May 4, 2020, to May 4, 2026, obtained from Yahoo Finance. The input features include opening price, highest price, lowest price, closing price, and trading volume, while the prediction target is the stock closing price. The results show that the baseline model produced MAPE values ranging from 1.892% to 3.147%. The best baseline performance was obtained on BBCA with an R² value of 0.933, followed by BBTN with an R² value of 0.902. After optimization, performance improvement occurred on BBTN, with MAPE decreasing from 3.147% to 2.482% and R² increasing from 0.902 to 0.935. For BMRI, MAPE decreased from 2.385% to 2.206%, and R² increased from 0.687 to 0.743. This study concludes that Long Short-Term Memory can be used to predict Indonesian banking stock prices, while Bayesian Optimization can selectively improve model performance depending on the characteristics of each stock dataset.

Dredger operations in water-based mining areas have a high occupational risk because they involve a combination of wet environments, mechanical equipment, and electrical systems. The presence of electrical panels, power cables, terminal connections, electric motors, inverters/drives, and protection components on the Dredger requires a sound understanding of Occupational Health and Safety, particularly electrical safety. Water splashes, wet walking surfaces, high humidity, and improper procedures during inspection or cleaning may increase the risk of short circuits, protection trips, and electric shock. Therefore, structured basic safety training is required so that operators are able to identify electrical hazards and implement safe working procedures. This activity aimed to improve operators’ understanding of hazardous electrical components, initial safety briefing procedures, measurement practice and safe handling during trips or short-circuit indications, and the correct and safe use of water pumps for unit cleaning. The activity was carried out through field observation, technical briefing, direct demonstration on the electrical panel and work area, simple measurement practice, and safe cleaning practice. The results indicate that field documentation can serve as an effective learning medium to strengthen operators’ understanding of electrical hazard sources on the Dredger, the application of simple lock out-tag out procedures, the control of wet areas, and the limitation of water spray direction during cleaning activities. Overall, the activity shows that improved compliance with safety procedures may reduce the risk of short circuits and electric shock during operation and maintenance.

Knee septic arthritis is an orthopedic emergency that requires prompt and appropriate management to prevent permanent joint damage and systemic complications. Inflammatory markers such as erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are commonly used for diagnosis and monitoring of treatment response; however, their prognostic value after surgical debridement remains variable. This systematic review aimed to evaluate the role of ESR, CRP, and platelet count as prognostic indicators following irrigation and/or surgical debridement in patients with knee septic arthritis. A literature search was conducted in January using PubMed, Google Scholar, ScienceDirect, Wiley Online Library, SpringerLink, and Semantic Scholar, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Study quality was assessed using the National Heart, Lung, and Blood Institute (NHLBI) tools. Fifteen studies meeting the inclusion criteria were qualitatively analyzed. The findings indicate that elevated preoperative CRP levels and delayed postoperative CRP reduction are associated with unfavorable clinical outcomes, including failure of infection eradication and the need for repeat debridement. Although ESR is consistently elevated during the acute phase, its slower decline limits its short-term prognostic utility. Platelet count may increase as part of the systemic inflammatory response but shows inconsistent prognostic value.

This study aims to analyze the paradox of stunting in Yemen, particularly the disconnect between humanitarian response priorities and children's actual nutritional needs amid the ongoing conflict. Stunting, a form of chronic malnutrition, is a significant public health issue in Yemen, exacerbated by the conflict's disruption of food, healthcare, and sanitation systems. Despite the clear evidence of rising stunting rates, international humanitarian interventions tend to focus on addressing acute crises, such as extreme hunger, while neglecting long-term nutritional needs. Using a qualitative literature review approach, the research synthesizes data from archival documents, reports, and secondary sources to explore the structural causes of stunting and the challenges faced by humanitarian actors in Yemen. The findings reveal that the protracted conflict, economic collapse, and humanitarian blockades have worsened food insecurity and malnutrition. Moreover, the humanitarian response often prioritizes short-term survival over long-term prevention of stunting, resulting in a lack of integrated, multisectoral interventions. The study emphasizes the need for a paradigm shift in humanitarian policies to incorporate long-term development strategies and integrate stunting prevention. This research contributes to the academic understanding of the humanitarian-development nexus and offers recommendations for more holistic interventions.

This study aims to evaluate how petty cash management practices contribute to improving operational efficiency at PT Anugerah Langgeng Berkat Abadi. This research focuses on examining the implementation of the petty cash management system, applied procedures, and its impact on the smooth execution of daily operational activities. The study employs a descriptive qualitative approach, with data collected through interviews, direct observation, and documentation during the internship period. The collected data were analyzed systematically to describe the actual condition of petty cash management within the company. The results indicate that PT Anugerah Langgeng Berkat Abadi implements a fluctuating fund system in managing petty cash. Expenditures are initially recorded manually and then re-entered into the company’s internal digital system to maintain control and accountability. Petty cash is used to finance routine and urgent operational needs, such as office stationery, transportation costs, and other short-term expenditures. The company has established standard operating procedures governing the use, recording, and accountability of petty cash. Several challenges were identified, including delays in the disbursement and reimbursement process, which may affect time efficiency. However, overall, the petty cash management system is considered effective in supporting short-term operational needs without disrupting the stability of the company’s main cash. This study concludes that systematic and well-controlled petty cash management plays an important role in the company’s cost efficiency strategy and supports daily operational activities. These findings align with strategic management principles, where appropriate financial decision-making contributes to the achievement of long-term organizational objectives.

This study examines the effect of working capital on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Working capital is operationalized through three distinct proxies derived from Akgün and Memiş Karatəs (2021): the Cash Holding Level (CHL), which measures the proportion of cash and cash equivalents relative to total assets; the Cash Interactive Effect (CIE), which captures the efficiency of converting revenue into operating cash flow; and the Gross Working Capital Ratio (GWCR), which reflects the share of current assets within total assets. Firm performance is assessed through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is measured using the model proposed by Gerson et al. (2025), expressed as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies. The study employs the Fixed Effect Model (FEM) for ROA and ROE, and the Random Effect Model (REM) for Tobin’s Q, as determined by the Hausman specification test. The findings reveal that CHL and CIE exert significant positive effects on ROA and ROE, while CIE is the only proxy to produce a statistically significant positive effect on Tobin’s Q. With respect to sustained growth, CHL and GWCR demonstrate significant negative effects, whereas CIE shows a significant positive effect, indicating that operational efficiency dimensions of working capital actively support long-term growth sustainability. These results reinforce the liquidity management theory and contribute empirical evidence that the structure and efficiency of working capital are strategic determinants of both short-term financial performance and long-term growth sustainability in Indonesia’s consumer goods manufacturing sector.

his study examines the concept of sustainable business development through the lens of Islamic economics, focusing on the integration of three core dimensions: financial profit (profit), social empowerment (people), and environmental stewardship (planet). The urgency of this research is driven by the global environmental crisis and social inequality, which demand business models that pursue long-term welfare rather than merely short-term gains. Using a qualitative approach through a systematic literature review, this research analyzes how Islamic economic principles—including tawhid, justice ('adl), and the responsibility of khalifah—align with and reinforce the Sustainable Development Goals (SDGs). The findings indicate that the Profit-People-Planet framework is not only compatible with Islamic teachings but is deeply embedded in Islamic economic ethics and the maqashid al-shariah framework. Instruments such as zakat, waqf, and Islamic financing serve as practical mechanisms that simultaneously support halal profit generation, enhance community welfare distributively, and ensure environmental protection through the principle of hifz al-bi’ah. This study concludes that Islamic economics offers a holistic, ethical, and value-based framework for building sustainable businesses that are profitable, socially responsible, and environmentally conscious in the modern era.

Bullying among adolescents is an aggressive behavior carried out intentionally, repeatedly, and involving an imbalance of power between the perpetrator and the victim. This phenomenon is an important issue because it can affect adolescents’ psychological well-being, social relationships, and academic achievement in both the short and long term. This study uses a literature review method by collecting and analyzing recent national and international studies regarding the causes, forms, and impacts of bullying. The findings indicate that bullying is influenced by internal factors, such as low empathy, poor self-control, negative self-concept, and the desire for social recognition. In addition, external factors also contribute, including parenting styles, peer influence, unfavorable school climate, and the development of social media that increases the risk of cyberbullying. The impacts of bullying are experienced not only by victims, who may suffer from low self-esteem, anxiety, depression, and social withdrawal, but also by perpetrators, who are at risk of developing persistent aggressive behavior and reduced empathy. Therefore, cooperation among families, schools, and communities is needed to prevent and address bullying effectively.

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.

Suspicious urban sound recognition is a critical component of intelligent public safety and urban monitoring systems, enabling the automated identification of anomalous acoustic events such as gunshots, sirens, and other security-sensitive sounds. However, existing deep learning approaches often struggle to simultaneously capture long-range temporal dependencies and global contextual relationships, particularly under noisy and acoustically complex urban conditions. This limitation can reduce reliability in safety-critical scenarios where missed detections carry significant risk. To address these challenges, this study proposes a Multi-Branch Bidirectional Long Short-Term Memory (BiLSTM) framework with Multi-Head Self-Attention (MHSA) for enhanced sequential and contextual feature modeling. Mel-frequency cepstral coefficients (MFCCs) are extracted from a curated subset of the UrbanSound8K dataset, comprising five suspicious sound classes, and used as input to the proposed architecture. The multi-branch design enables complementary temporal representations, while the self-attention mechanism provides lightweight contextual weighting of BiLSTM outputs. Experimental results demonstrate that the proposed model achieves a test accuracy of 95.59%, outperforming conventional Dense and LSTM-based baseline models under identical experimental settings. An ablation study further confirms the contribution of multi-branch integration and attention-based enhancement to overall performance. Class-wise evaluation reveals consistently high recall across all sound categories, particularly for safety-critical classes such as gunshots and sirens. These findings indicate that the proposed framework provides robust and reliable performance, making it suitable for real-time smart city surveillance and public safety applications.

The highly competitive healthcare industry in Cirebon poses a significant challenge for Klinik Utama X, especially as a non-BPJS facility. This condition requires a paradigm shift from a provider-centric marketing mix to a patient-centric approach to retain patient loyalty. This study aims to evaluate and formulate the marketing strategy of Klinik Utama X using a comparative 4P (Product, Price, Place, Promotion) and 4C (Customer Solution, Customer Cost, Convenience, Communication) approach based on SWOT analysis. This research used a qualitative descriptive design with purposive sampling, gathering primary and secondary data through in-depth interviews, observations, and document studies. The data were systematically evaluated using Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices. The findings reveal that the clinic is strategically positioned in Quadrant I, with an IFAS score of 2.53 and an EFAS score of 2.60, indicating strong internal capabilities to seize external opportunities. The recommended aggressive growth strategy involves integrating psychiatric and aesthetic services, penetrating the Business-to-Business (B2B) market through corporate medical check-ups, accelerating digitalization via telemedicine and online queuing, and preparing for BPJS accreditation. The managerial implications suggest that the clinic must prioritize resolving internal human resources and strengthening financial record-keeping systems before investing heavily in medical assets, while actively expanding digital convenience and corporate partnerships to secure short-term financial stability.

Submarine pipelines are vital infrastructures in aviation fuel distribution, carrying high risks of operational disruption, safety issues, and environmental pollution. In 2025, the aviation fuel receiving submarine pipeline at Integrated Terminal Ampenan was deformed due to mechanical pulling by MT Anargya I, causing system shutdown. This incident created risks of seawater intrusion, marine pollution, and potential fuel shortages at Lombok International Airport. This study evaluates the effectiveness of applying a double-ended draining method combined with an oil bubble trap as a fast, safe, and sustainable solution for submarine pipeline repair. Using a case study and descriptive-analytical approach based on the Continuous Improvement Program (CIP), the research analyzed quality, cost, delivery, safety, environmental, and workforce aspects. The results showed the method effectively drained aviation fuel without seawater contamination, prevented marine spills, maintained fuel quality within specifications, and completed repairs before critical stock levels were reached. Additionally, it provided significant cost savings compared to emergency supply operations and achieved zero safety incidents and environmental pollution. The integration of double-ended draining and oil bubble trap proved to be an effective, adaptive innovation with strong potential for replication and standardization in other aviation fuel submarine pipeline systems.