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This research examines the role of public relations (PR) in the digital era, focusing on the relationship between PR and social media, adaptation of communication strategies to technological advancements, and new strategies to enhance digital influence. Through a qualitative literature review approach, this study analyzes relevant literature to identify trends, challenges, and current strategies in PR practice in the digital era. The research findings indicate that social media has become the primary platform for organizations to interact directly with their audience, while adapting communication strategies to technological advancements is crucial in achieving organizational communication goals. Challenges such as the spread of misinformation and online reputation crises are also identified, while new strategies such as storytelling-based content, the use of multimedia platforms, and collaboration with digital influencers are considered crucial steps in enhancing digital influence. This research highlights the importance of openness and transparency in PR practice, as well as the need to continuously adapt communication strategies to technological advancements and user behavior. By understanding and addressing existing challenges, PR practitioners can leverage the opportunities offered by the digital era to strengthen organizational reputation and build strong relationships with their audience.

Introduction and use of artificial intelligence (AI) technology is increasingly widespread in various aspects of human life. In the context of community service, AI has great potential to improve the quality of life of people through a variety of innovative applications. This article discusses how AI can be used in community service, including practical applications in the field of education. Student Data Management: Al Ihsan Foundation can use AI to manage orphan student data such as personal data, academic data, and health data. AI-supported data management systems can integrate this information better, thus enabling more personalized and customized services. Learning and Development: AI can be used to develop online learning platforms that are tailored to individual student learning needs. AI systems analyze student learning preferences and progress and recommend the most effective learning materials and methods. By leveraging AI, younger generations can create exciting, high-quality content without having to have specialized expertise in graphic design or video editing. For example, technologies such as Canva Magic Studio leverage artificial intelligence to make it easier to make exciting designs.The training aims to provide a better understanding to the younger generation of the basic concepts of AI and its applications in everyday life, including in the creation of creative digital content. Hopefully, with a better understanding of AI, the younger generation will be able to develop their creative potential and use it for a variety of positive purposes in their lives.

With the rise of digital media, religious education faces new challenges and opportunities. This article investigates the integration of digital tools in teaching religious studies, focusing on their potential to make learning more engaging and accessible. The study analyzes both the benefits, such as interactivity and inclusivity, and the risks, including misinformation and digital distractions. Practical recommendations are provided for educators to effectively leverage digital platforms while maintaining the integrity of religious teachings.  

The high purchasing power of society and within lengthy periods will cause the state falls into inflation. Inflation has its influence on domestic savings. Hence, the government tries to limit the amount of money which circulates and reduces inflation risk by offering higher interest rates for the people who deposit their money in national banks. The socialization program of early saving is carried out with the purpose of creating national program in creating a generation who isable to manage their finances well. Other than that, the program hopefully has the ability to leverage the interest in saving for children. The children may develop the habit into an insurance for their later life, and autonomously learn to save and to be responsible in managing the money,hence the goals of the program.Teaching the children to save early may also develop positive personality; to save means teaching the children how to be patient, and saving is useful for future savings.

This study aims to determine the impact of the IOS on the quality of profits of companies involved in LQ45 for the 2018/2022. The population of this study is comprised of LQ45 companies that are listed on the Indonesia stock exchange in 2018/2022. The sample employs a purposive sampling method in the selection of samples, this method causes the observed samples to be 41 companies that have been in existence for a period of five years. This results in a data set of 205. This research employs secondary data derived from the company's website. The hypothesis testing employed is a simple technique for regression analysis. The research results indicate that the Investment Opportunity Set and the financial leverage have a significant positive effect on the quality of earnings, the age of the company has no significant effect on the quality of earnings, and liquidity has a significant negative effect on the quality of earnings. Future researchers should consider adding variables that may influence the quality of earnings. Future additions should include other proxies for cash, such as holding cash.  

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.

Tax evasion can be defined as the activity of avoiding taxes by citizens or entities that are obliged to pay taxes in order to reduce the tax costs that must be paid by utilizing the tax law which has loopholes and weaknesses. This research aims to explain whether company size, leverage and profitability influence tax avoidance or not. The research took 24 samples of manufacturing companies listed on the Indonesian Stock Exchange for the 2017 - 2019 period. The data used was collected through purposive sampling and using panel data regression analysis techniques through one qualitative and six quantitative tests. The final conclusion concludes that company size as the first variable has a positive influence on tax avoidance, so that it means companies with large sizes tend to avoid tax avoidance. The second variable, namely leverage, has the result of having no influence on tax avoidance. Third, profitability has a negative influence on tax avoidance, meaning that companies with large profits will increase the company's tendency to avoid taxes.

This study aims to analyze and obtain evidence regarding the effect of leverage, company size, and probility on corporate value in manufacturing companies listed on the Indonesia Stock Exhange for the 2017-2019 period. In this study, there are 3 independent vaiables tested, namely Debt to Equity (DER), Indikator Size (LN), and Return On Asset (ROA). And there is 1 dependent variable, namely Price to Book Value (PBV). The numbers of sample used are 21 manufacturing companies sub sector consumer goods onthes Indonesia Stock Exchange (IDX), the sampling technique used was purpose sampling. The analysis method used is the panel data regression analysis method using the Fixed Effect Model.the research result prove that leverage has no effect on corporate value, company size has negatif effect on corporate value, and probility has a positif effect on corporate value.

This research aims to analyze Working Capital Management and Leverage on Profitability Through Good Corporate Governance (Empirical Study of Consumer Cyclicals Sector Companies Listed on the Indonesian Stock Exchange (BEI) for the 2020 – 2022 Period. The research population is all listed Consumer Cyclicals sector companies on the Indonesia Stock Exchange (BEI) for the period 2020 - 2022 with a total of 139 companies, the sample that has been determined in this research is 31 companies using non-probability sampling techniques with the Purposive Sampling method. Data analysis in this research uses the SmartPLS program. Hypothesis testing using the SEM-PLS approach. The results of the analysis and discussion of Working Capital Management have an effect on Profitability, Leverage has no effect on Profitability, Good Corporate Governance has an effect on Profitability, indirectly  Working Capital Management has no effect on Profitability through Good Corporate Governance, and indirectly h Leverage has no effect on Profitability through Good Corporate Governance in Consumer Cyclicals Sector Companies Listed on the Indonesia Stock Exchange (BEI) for the 2020 – 2022 Period.    

The company’s value is evidence that the entity has tried to run the company’s operation well with maximum profit and is a useful indicator for investors and shareholders, as it describes the success of an entity that can be seen from the price of shares. From that understanding it was decided to testing and proving the significance of profitability (ROE), company size (SIZE), leverage (DER), and liquidity (CR) towards company’s value (PBV). This type of research is the entire property and real estate companies listed on the Indonesian Stock Exchange (IDX) and sampling using purposive sampling with the final sample of 90 surveys. Double linear regression for data analysis methods using the help of SPSS software. From this study obtained results and proved that profitability (ROE) and leverage (DER) have an influence towards company’s value. Whereas, company’s size (SIZE) and liquidity (CR) proved to have no influence towards company’s value.

This research aims to examine the effect of disclosure of sustainability reports and intellectual capital on company value with company size, profitability and leverage as control variables. The population in this research are banking companies listed on the Indonesia Stock Exchange in 2018-2022. The data analyzed comes from secondary data taken from annual reports and sustainability reports listed on the Indonesia Stock Exchange. Sample selection was carried out using a purposive sampling technique, so that a sample of 82 data was analyzed. The analytical method used in this research is multiple linear regression. The research results show that disclosure of sustainability reports and intellectual capital have no effect on company value. Testing the control variable for company size has no influence on company value. Meanwhile, profitability and leverage have a positive and significant effect on company value.

Pentingnya menganalisis financial distress pada perusahaan adalah dapat mempertimbangkan suatu permasalahn pada keuangan yang timbul pada perusahaan yang akan berdampak secara langsung maupun tidak langsung terhadap operasional perusahaan, oleh sebab itu pemimpin perusahaan harus mengantisipasi kondisi tersebut. Tujuan penelitian untuk mengetahui faktor-faktor yang mempengaruhi financial distress (studi kasus pada perusahaan manufaktur sektor industri barang konsumsi yang terdaftar di bursa efek indonesia tahun 2020-2022. Metode penelitian yang digunakan yaitu metode kuantitatif. Hasil penelitian menunjukkan bahwa variabel likuiditas berpengaruh posisitif signifikan terhadap financial distress. Variabel leverage berpengaruh negatif signifikan terhadap financial distress. Variabel profitabilitas berpengaruh positif signifikan terhadap financial distress. Variabel operating capacity berpengaruh positif signifikan terhadap financial distress. Variabel sales growth tidak berpengaruh terhadap financial distress. Variabel ukuran perusahaan tidak bepengaruh terhadap financial distress.

Background: Audit delay represents a critical factor affecting the timeliness of financial reporting and information usefulness for decision-making. The property and real estate sector faces unique challenges in audit processes due to complex asset valuations, project accounting, and regulatory requirements, making audit delay a significant concern for stakeholders. Objective: This study aims to examine the effect of firm size, leverage, and audit quality on audit delay in property and real estate companies listed on the Indonesia Stock Exchange (IDX). Methods: This quantitative study employed multiple regression analysis using a sample of 65 property and real estate companies listed on IDX during 2020-2024, resulting in 325 firm-year observations. Audit delay was measured as the number of days between fiscal year-end and audit report date. Independent variables included firm size (natural logarithm of total assets), leverage (debt-to-equity ratio), and audit quality (Big 4 auditor dummy). Control variables encompassed profitability, company age, and audit opinion type. Results: The findings reveal that firm size has a significant negative effect on audit delay (β = -8.743, p < 0.01), indicating that larger companies experience shorter audit delays. Leverage shows a significant positive effect on audit delay (β = 4.562, p < 0.05), suggesting that higher leverage increases audit complexity and duration. Audit quality demonstrates a significant negative effect on audit delay (β = -12.385, p < 0.01), confirming that Big 4 auditors complete audits more efficiently. The model explains 68.4% of the variance in audit delay (R² = 0.684). Conclusion: Firm characteristics and audit quality significantly influence audit delay in the property and real estate sector. Companies should focus on maintaining optimal capital structure, engaging high-quality auditors, and leveraging size advantages to minimize audit delay and enhance financial reporting timeliness.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

This research is motivated by the existence of a high level of public trust in banking is closely related to the assessment of banking performance in terms of liquidity ratio analysis. The purpose of the study is to determine the level of bank liquidity and what policies will be carried out by banks against the ratio results. The method used in the research is the literature review method with data in the form of references to evaluate liquidity ratios in banking. The results obtained in this study from Table RQ1 the types of liquidity ratios that are often used in measuring the level of banking liquidity are the quick ratio and cash ratio. While Table RQ2 the advice that can be given is that banks must be able to pay attention and evaluate their assets. So it can be concluded that in general the types of liquidity ratios that are often used to calculate the level of bank liquidity are the quick ratio and cash ratio and further banking policy suggestions to use more diverse types of liquidity ratios. Ajmadayana, C. P., Akmalia, Z., & Hasibuan, A. F. (2022). Analisis Rasio Likuiditas dan Solvabilitas pada Bank Muamalat Indonesia Periode 2019-2020. Jurnal Ekobistek, Volume 11 No.3, 179-185. Fathurrahman. (2022, March). Banking Perfomance Analysis Based On Liquidity Ratio. Nusantara Hasana Journal, Vol.1 No.10, 141-145. Harahap, & Safitri, S. (2015). Analisis Kritis Atas Laporan Keuangan. Jakarta: PT. Rajagrafindo Persada. Harianto, S. (2017). Rasio Keuangan dan Pengaruhnya Terhadap Profitabilitas Pada Bank Pembiayaan Rakyat Syariah di Indonesia. Jurnal Bisnis dan Manajemen, 41-48. Iryani, L. D., & Herlina. (2015). Analisis Rasio Likuiditas, Solvabilitas, dan Profitabilitas dalam Mendukung Pembiayaan pada PT. Bank Danamon Indonesia, Tbk. Jurnal Ilmiah Akuntansi Fakultas Ekonomi, Volume 1 No. 2, 32-40. Jumingan. (2006). Analisis Laporan Keuangan. Jakarta: Bumi Aksara. Kasmir. (2015). Bank dan Lembaga Keuangan Lainnya. Jakarta: PT Raja Grafindo Persada. Kasmir. (2017). Analisis Laporan Keuangan. Jakarta: PT. Rajagfrindo Persada. Khotimah, I. C. (2021). Analisis Dampak Pandemi Covid-19 Bagi Rasio Likuiditas Bank Bukopin Syariah. Jurnal LARIBA Jurnaal Perbankan Syariah, Vol. 2 No. 02, 28-41. Masita, N., Hariatih, & Nianty, D. A. (2023). Analisis Kinerja Keuangan Menggunakan Rasio Likuiditas dan Profitabilitas pada PT. Bank Rakyat Indonesia (Persero) Tbk. Jurnal Manajemen dan Akuntansi, Vol. 1 No. 2, 203-214. Nurul, Suharti, T., & Nuhayati, I. (2020, Mei). Analisis Kinerja Keuangan Berdasarkan Rasio Likuiditas, Solvabilitas, dan Rentabilitas Pada Sektor Perbankan. Jurnal Ilmu Manajemen, Vol.3 No.2, 146-159. Ottay, M. C., & Alexander, S. W. (2015). Analisis Laporan Keuangan Untuk Menilai Kinerja Keuangan Pada PT. BPR Citra Dumoga Manado. Jurnal EMBA, Volume 3 No.1, 923-932. Pangemanan, I. W., Karamoy, H., & Kalalo, M. (2017). Analisis Rasio Likuiditas, Leverage dan Profitabilitas Untuk Menilai Kinerja Keuangan Pada PT. Bank Central Asia, Tbk. Cabang Manado. Jurnal Riset Akuntansi Going Concern, Volume 12 No. 2, 25-34. Permana, I. S., Halim, R. C., Nenti, S., & Zein, R. N. (2022). Analisis Rasio Likuiditas, Solvabilitas, dan Profitabilitas dalam Mendukung Pembiayaan pada PT. Bank Danamon Indonesia, Tbk. Jurnal Aktiva: Riset Akuntasi dan Keuangan, Volume 3 No.3, 132-139. Ramadhanty, T. N., Musriha, & Noviandri, I. (2021). Analisis Rasio Keuangan terhadap Kinerja Keuangan pada PT.Bank Pembangunan Daerah Jawa Barat dan Banten, Tbk. Jurnal Ekonomi dan Bisnis, Volume 1 Nomor 3, 183-188. Setia, I., Clasissa, R., Nenti, S., & Zein, R. N. (2022). Analisis Kinerja Keuangan dengan Menggunakan Rasio Likuiditas, Solvabilitas, dan Profitabilitas pada PT. Bank BNI (Persero) Tbk. Jurnal Aktiva: Riset Akuntansi dan Keuangan, Vol.3 No.3, 132-139. Sihombing, C., Damanik, E. O., & Sriwiyanti, E. (2019). Pengaruh Rasio Likuiditas Terhadap Kinerja Keuangan Pada PT. Bank Perkreditan Rakyat Eka Prasetya Pematangsiantar. Jurnal of Accounting USI, Volume 1 No. 1, 12-22.  

Penelitian ini bertujuan untuk menganalisis pengaruh kinerja keuangan terhadap nilai perusahaan pada Badan Usaha Milik Negara (BUMN) yang terdaftar di Bursa Efek Indonesia (BEI) selama periode 2020-2023. Teknik pengambilan sampel perusahaan menggunakan teknik sampling jenuh, populasi dan sampel pada penelitian ini adalah 48 perusahaan dari kriteria yang ditetapkan. Metode analisis yang digunakan adalah metode deskriptif kuantitatif. Teknik analisis yang digunakan adalah analisis regresi linier berganda. Hasil dari penelitian ini mengindikasikan bahwa likuiditas menunjukkan pengaruh negatif dan tidak signifikan terhadap nilai perusahaan, leverage memiliki dampak negatif dan signifikan terhadap nilai perusahaan, ukuran perusahaan serta profitabilitas menunjukkan pengaruh positif dan signifikan terhadap nilai perusahaan. Hal ini mengindikasikan bahwa perusahaan yang memiliki ukuran lebih besar dan tingkat profitabilitas yang tinggi cenderung memiliki nilai perusahaan yang lebih tinggi. Namun, dalam penelitian ini, likuiditas tidak menunjukkan pengaruh yang signifikan terhadap nilai perusahaan.  Implikasi dari studi ini menunjukkan bahwa perusahaan harus fokus pada pengelolaan utang, pengembangan usaha, dan peningkatan laba untuk meningkatkan nilai perusahaan

Collaborative filtering systems rely heavily on matrix factorization techniques, which often face scalability issues when handling large datasets. This paper presents an efficient parallel algorithm that leverages distributed computing to perform largescale matrix factorization. Experimental results show that our algorithm significantly reduces computation time while maintaining high accuracy. The approach has practical implications for recommendation systems, particularly in ecommerce and social media platforms.

This research is motivated by the complex relationship between operating leverage, financial leverage, and earnings per share (EPS) in the context of corporate finance. This research aims to analyze the impact of operating leverage and financial leverage on earnings per share in manufacturing companies listed on the Jakarta Islamic Index during the period 2018-2022. Utilizing financial data from the sample companies, the study adopts a quantitative approach with regression analysis using Eviews to evaluate the relationship between operational and financial leverage with the performance of earnings per share. The research results indicate that operating leverage does not have a significant effect on earnings per share, with a partial test probability value of 0.9744 > 0.05. Financial leverage also does not have a significant effect on earnings per share, with a partial test probability value of 0.7368 > 0.05. Simultaneously, operating leverage and financial leverage do not have a significant effect on earnings per share, with a simultaneous test probability value of 0.94578 > 0.05. The calculation of the coefficient of determination shows that the independent variables in the model fail to explain the variation in the dependent variable well. Subsequent research is recommended to increase the sample size of manufacturing companies listed on the JII and replace insignificant independent variables with others that can significantly influence earnings per share.

This study aims to analyze the factors that influence tax avoidance in pharmaceutical sub-sector companies listed on the Indonesia Stock Exchange. The independent variables analyzed in this study include profitability, sales growth, and leverage. This study uses secondary data in the form of annual financial reports of companies obtained from the official website of the Indonesia Stock Exchange. The method used in this study is multiple linear regression analysis with samples selected through purposive sampling method. This study examines the effect of each independent variable on tax avoidance, which is measured using the Effective Tax Rate (ETR) proxy. The results of the study indicate that profitability has a significant effect on tax avoidance, where companies with higher levels of profitability tend to avoid taxes. Meanwhile, sales growth has a significant effect on tax avoidance. Leverage also shows a positive effect on tax avoidance, indicating that companies with high debt levels tend to use interest expenses to reduce their tax liabilities. This study provides implications for regulators and policy makers to strengthen supervision of tax avoidance practices, especially in companies that have certain characteristics such as high profitability sales growth and leverage.

This research aims to determine the influence of company size, profitability, leverage and financial distress on earnings management in insurance companies listed on the Indonesia Stock Exchange (BEI) for the 2019-2022 period. This research method is a quantitative method using secondary data. The population in this study were all insurance companies listed on the Indonesia Stock Exchange (BEI), 18 insurance companies listed on the IDX in 2019-2022, by taking samples using a purposive sampling method to obtain a sample of 11 companies. The data analysis techniques used are descriptive statistics, classical assumption testing, multiple linear regression analysis, and hypothesis testing. The results of this research show that profitability partially influences earnings management, while company size, leverage and financial distress do not influence earnings management.