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This study aims to evaluate the effectiveness of various doses of 6.25% Poly Aluminium Chloride (PAC) and 0.1% polymer flocculant in reducing Total Suspended Solids (TSS) and assessing pH changes in coal stockpile wastewater at PT PLN Nusantara Power UP Paiton Unit 9. Stockpile wastewater typically contains high levels of suspended solids originating from water spray activities that carry fine coal particles. The coagulation–flocculation process was performed using the jar test method with PAC dosages of 35 ppm, 50 ppm, and 65 ppm, along with flocculant dosages of 6 ppm and 7 ppm. pH and TSS were analyzed before and after treatment to assess process effectiveness. The results indicate that a PAC dosage of 35 ppm combined with a 6 ppm flocculant achieved the highest TSS removal efficiency of 98.15%. Increasing PAC dosage resulted in reduced performance due to overdosing effects, leading to charge destabilization and impaired floc formation. These findings highlight the importance of optimizing coagulant dosage to improve stockpile wastewater quality for safe reuse in operational activities.

This study explores the roles of innovation and perseverance as critical success factors for young entrepreneurs in the fried chicken business through an in-depth case study of Fried Chicken Smile, located in Medan Tembung Subdistrict. The research focuses on how young entrepreneurs are able to survive and grow in a highly competitive culinary market dominated by global brands such as KFC. Using a qualitative research approach, data were collected through in-depth interviews with business owners and employees, direct field observations, and documentation analysis. The study is supported by several theoretical perspectives, including Grit Theory, which emphasizes perseverance and passion for long-term goals; Entrepreneurial Behavior Theory, which explains proactive and adaptive business behavior; and Schumpeter’s Innovation Theory, which highlights innovation as a driver of competitive advantage. The findings indicate that business success is influenced by continuous product innovation, particularly through adapting menu flavors to local tastes, maintaining consistent operational quality, and implementing effective digital marketing strategies via social media platforms. In addition, strong social support from family, peers, and local communities plays a significant role in sustaining entrepreneurial motivation. Overall, the study demonstrates that the combination of innovation, persistence, and social capital enhances competitiveness and supports the growth of micro, small, and medium enterprises (MSMEs). These findings provide valuable insights for the development of young entrepreneurship in Indonesia’s dynamic culinary sector.

This community service program aims to optimize construction project planning through assistance in preparing a business feasibility study at PT Sahabat Anugrah Sejati. The construction sector is a strategic industry that requires effective resource management, proper risk mitigation, and accurate planning to ensure project success. Therefore, the program supported the partner in analyzing financial, managerial, market, and economic aspects prior to making investment decisions for the proposed project. The results show that the project is feasible based on the Net Present Value (NPV) and Internal Rate of Return (IRR) analyses. An efficient organizational structure, a competent management team, and strong supervision mechanisms further strengthen the project’s operational readiness. Increasing demand for construction services also enhances the company’s market prospects. However, several areas still require improvement, including heavy equipment utilization, material efficiency, and digital marketing strategies. This program provides strategic recommendations to help PT Sahabat Anugrah Sejati improve project feasibility, operational effectiveness, and long-term business sustainability.

The rapidly changing and increasingly uncertain business environment presents significant challenges for micro, small, and medium enterprises (MSMEs), requiring them to adapt quickly and strategically to survive and remain competitive. This conceptual article examines the role of business models as an effective adaptation strategy for MSMEs in responding to dynamic market conditions, technological change, and shifting consumer preferences. Through a comprehensive review of various business model frameworks and dynamic capability theories, this study emphasizes that business models play a crucial role in shaping organizational flexibility and strategic responsiveness. The findings highlight that adaptable value propositions enable MSMEs to continuously align their offerings with evolving customer needs, while agile resource management allows firms to efficiently reconfigure internal and external resources. Furthermore, continuous business model innovation and renewal are identified as essential mechanisms for sustaining long-term resilience and competitiveness. The article argues that a business model should not be viewed merely as an operational or administrative tool, but rather as a core strategic mechanism that integrates strategic vision, organizational capabilities, and market orientation. By actively leveraging flexible and innovative business models, MSMEs can enhance their adaptive capacity, mitigate environmental uncertainty, and achieve sustainable competitive advantage in rapidly changing business environments.

The rapid advancement of digital technologies and Artificial Intelligence (AI) has fundamentally reshaped the management and development of the tourism industry. Digital transformation strategies offer substantial opportunities to enhance destination competitiveness while simultaneously supporting economic, social, and environmental sustainability. This study aims to systematically examine the role of digital transformation and AI in strengthening sustainable tourism competitiveness through a literature review approach. A total of 42 peer-reviewed journal articles published between 2019 and 2025 were analyzed, drawing from Scopus, Web of Science, and Google Scholar. The analysis employed thematic synthesis to identify dominant patterns, conceptual relationships, and emerging themes across the literature. The findings indicate that AI-driven digital transformation enhances operational efficiency, enables personalized tourist experiences, supports data-informed resource management, and facilitates the development of smart tourism destinations. Nevertheless, persistent challenges related to human resource readiness, digital inequality, data governance, and ethical considerations remain evident. This review provides an integrated conceptual perspective on digital transformation and AI in sustainable tourism competitiveness and offers insights for policymakers, practitioners, and future research.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

This study explores the integration of critical pedagogy with digital literacy, aiming to reconceptualize digital literacy beyond its traditional focus on technical proficiency and operational skills. Traditional digital literacy models emphasize the ability to navigate and use technology, primarily focusing on operational tasks such as managing digital devices, utilizing software, and performing specific functions. However, these models often overlook the broader implications of technology on society, including its role in shaping power dynamics and perpetuating social inequalities. By integrating critical pedagogy, this study argues that digital literacy should not only encompass technical skills but also encourage learners to critically assess the social, political, and cultural implications of digital tools. The critical digital literacy approach promotes social justice, equity, and empowerment by encouraging learners to question, reflect on, and challenge the ways in which technology influences their lives and society at large. Through critical engagement with digital tools, learners are empowered to challenge existing power structures, fostering social transformation. The study discusses how educators can integrate critical digital literacy into teaching practices, moving beyond traditional skill based models by encouraging critical thinking, ethical use of technology, and a deeper understanding of the role digital technologies play in shaping societal structures. Moreover, the study highlights the transformative potential of critical digital pedagogy, proposing it as a tool for educational equity and social change. By reconceptualizing digital literacy through a critical pedagogical lens, this study offers a framework that empowers students to become not just proficient digital users but active participants in societal transformation, contributing to a more inclusive and just digital future. The study also suggests areas for future research, including empirical investigations on the practical implementation of critical digital literacy frameworks across various educational settings and contexts.

Advances in information technology increasingly require communities to adapt to digital communication tools, particularly email, which functions as a key digital identity for account registration, information exchange, and online transactions. Despite its importance, digital literacy remains limited among the general public, especially rural housewives, many of whom lack awareness of the need for email accounts, have limited operational skills, and are vulnerable to cyber risks such as phishing and data theft. To address this issue, a workshop entitled “Fundamentals of Using Gmail and Account Security” was conducted for the PKK women’s group in Jompo Kulon Village to improve basic digital skills and promote safe online practices. The program aimed to introduce the functions and benefits of email, assist participants in independently creating Gmail accounts, provide knowledge on securing accounts through two-factor authentication, and train participants to send and receive emails. The workshop was delivered through an interactive face-to-face approach combining short presentations and hands-on practice using participants’ mobile devices. Initial sessions explained email as a digital identity, followed by guided account creation and security setup. The final session focused on practicing email communication to reinforce understanding. The results showed high participant engagement, with most participants successfully creating Gmail accounts, understanding essential digital security concepts, and demonstrating basic email usage skills. Overall, the activity increased awareness of personal data protection and is expected to serve as an initial step in strengthening digital literacy among women, enabling them to better adapt to ongoing technological developments.

This study aims to analyze the impact of business combinations through acquisition on the financial performance of PT Sarana Menara Nusantara Tbk. The research employs a descriptive quantitative approach, focusing on the acquiring firm in the Indonesian telecommunications infrastructure sector. The data used are secondary data obtained from the company’s annual financial statements for the period 2019–2023, sourced from the Indonesia Stock Exchange and the company’s official website. Financial performance is analyzed using Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Debt to Equity Ratio (DER) by comparing the periods before, during, and after the acquisition conducted in 2021. The results indicate that the acquisition exerted short-term pressure on asset efficiency and profitability, as reflected by the decline in ROA and NPM in the year of acquisition. However, in the post-acquisition period, the company demonstrated an improvement in operational performance, particularly in Net Profit Margin, suggesting that the economic benefits of the business combination gradually materialized. Meanwhile, fluctuations in ROE and DER reflect adjustments in the capital structure following the acquisition. These findings suggest that the success of an acquisition cannot be evaluated solely based on short-term financial performance but requires continuous assessment to capture its medium- and long-term effects. This study provides practical implications for management in formulating post-acquisition integration strategies and contributes empirically to the accounting and finance literature on business combinations in Indonesia.

This study analyzes the forms of risk and risk management strategies in melon farming in Brebes as an impact of climate change. A descriptive qualitative approach was applied using a case study through in-depth interviews with a farmer who has more than ten years of experience, supported by secondary data from BPS, BAPPENAS, and related literature. The findings reveal four categories of major risks: market, financial, operational, and socio-environmental. High-level risks include climate variability, yield decline, and pest attacks. Medium-level risks involve production costs, water management, and price fluctuations, while low-level risks consist of limited weather information and agricultural extension services. Current strategies are still basic, including fertilization, pesticide application, and water channel arrangement. Strengthening adaptation is needed through irrigation technology, climate-resilient varieties, marketing diversification, and improved access to information and institutional support.

This research was conducted at PT PLN Nusantara Power UP Paiton, one of the largest coal-fired power plants (PLTU) in Indonesia, which focuses on reducing the generation of hazardous and toxic oil waste through the implementation of an oil purification system. The use of large amounts of lubricating oil in the Electro-Hydraulic System (EHS) has the potential to produce high amounts of used oil waste. To address this, the company implemented two main technologies, namely Water Content and Varnish Removal, which function to reduce water content and varnish layers so that the oil can be reused without reducing engine performance. This research used a quantitative descriptive method with data collection techniques through field observations, interviews, and operational documentation from 2021 to 2024. The results showed that the oil purification system was able to reduce hazardous and toxic waste generation by 11.46 tons over four years. In addition to providing environmental benefits, the implementation of this system also resulted in savings in oil waste costs of approximately Rp6,200,560,000. Technically, purification maintains engine performance by reducing water and varnish content, while from an environmental perspective, this activity supports the principle of reduce in hazardous and toxic waste management. Overall, the oil purification system has proven effective in improving operational efficiency, extending oil life, and supporting sustainable waste management for industrial operations.

This study aims to analyze the strengthening of higher education governance in realizing transparent and accountable academic services. In the context of the globalization of higher education and increasing public accountability demands, universities are required not only to excel academically but also to provide services that are open, responsive, and accountable. This research employs a qualitative approach with a descriptive-analytical design. Data were collected through in-depth interviews, observations, and documentation studies involving university leaders, academic administrators, lecturers, and students. Data analysis was conducted interactively through data reduction, data display, and conclusion drawing. The results indicate that the principle of transparency has been implemented through digital-based academic information systems; however, consistency in updating information still needs improvement. Accountability has been supported by standard operating procedures and service evaluations, although performance measurement based on indicators has not been fully integrated. Stakeholder participation has been facilitated through evaluation forums, but involvement in strategic decision-making remains limited. Overall, the dimensions of transparency, accountability, participation, effectiveness, and responsibility are interrelated in shaping the quality of academic services. This study emphasizes that strengthening governance must be systemically internalized within organizational culture and institutional operational systems to enhance trust and stakeholder satisfaction.

The increasing intensity of business competition requires companies to maintain strong financial conditions to avoid financial distress that may disrupt business continuity. This study aims to assess the financial stability and predict the potential bankruptcy of PT Sido Muncul Tbk for the 2022–2024 period using the Altman Z-Score model. A descriptive quantitative approach was applied, utilizing secondary data obtained from annual reports published by the Indonesia Stock Exchange and the company’s official website. Five key ratios in the Altman model were used as indicators to evaluate the company’s financial position and resilience. The results show Z-Score values of 4.74 in 2022, decreasing slightly to 4.66 in 2023, and rising again to 4.79 in 2024. These scores are significantly above the safe threshold of 2.675, indicating that the company is in a healthy financial state with a very low risk of bankruptcy. Overall, PT Sido Muncul Tbk demonstrates stable financial performance, supported by a strong capital structure and consistent operational results. The Altman Z-Score model also proves to be an effective early-warning tool for identifying potential financial problems.

The attendance recording system at PLN ULP Tanjungbalai still relies on manual, paper-based methods, resulting in delays in data recap, reduced efficiency, and a high potential for recording errors. This condition affects the accuracy of employee attendance information, which is essential for administrative activities and managerial decision-making. Based on these issues, this practical work aims to design and develop a web-based e-attendance application as a solution to enhance efficiency, processing speed, and the accuracy of attendance recapitulation. The system was developed using PHP as the programming language and MySQL as the database management system, following several stages including requirement analysis, system design using UML, and implementation of a web-based user interface. The application provides essential features such as user login, daily attendance recording, employee data management, attendance notes (permission, sickness, etc.), and automatic attendance report generation. The system is designed for two types of users—Admin and Employees—each with specific access rights. The implementation results indicate that the e-attendance application significantly improves the efficiency of attendance administration at PLN ULP Tanjungbalai. Data collection and recapitulation become faster, more structured, and less prone to errors, while also enabling administrators to monitor employee attendance in real time. Therefore, this web-based e-attendance application serves as an effective solution to support operational activities and enhance the quality of employee attendance management.

The rapid growth of e-commerce has increased the strategic role of freight forwarding and delivery service companies in maintaining the efficiency of supply chains. However, the increasing complexity of delivery processes also raises various operational risks, such as package damage, loss, delivery delays, and distribution errors. This study aims to analyze the implementation of risk management in freight forwarding companies operating in Brebes Regency and to assess the effectiveness of the risk management practices applied. This research employs a descriptive qualitative approach, with data collected through in-depth interviews, observations, documentation, and questionnaires. Informants were selected using purposive sampling, involving company management, operational staff, couriers, and customer service personnel. Data analysis was conducted using the Miles and Huberman interactive model and supported by average risk score analysis. The results show that most aspects of risk management fall into the high and very good categories, with an overall average score of 3.89. This indicates that the company has implemented risk management effectively, particularly in incident evaluation, risk measurement, and cooperation with insurance providers. Nevertheless, improvements are still needed in reducing the frequency of operational risks and enhancing the use of digital technology for risk monitoring to ensure a more comprehensive and sustainable risk management system.

As a maritime nation with vast sea territories, Indonesia faces significant challenges in safeguarding its waters, particularly in strategic regions such as the Natuna Sea. This area is vulnerable to various threats, including territorial violations, marine resource theft, and illegal underwater activities. This study aims to formulate a concept for the utilization of Autonomous Underwater Vehicles (AUVs) as an alternative solution to enhance underwater security systems in the region. The method employed is Research and Development (R&D), adopting model development steps and complemented by a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to assess the feasibility and effectiveness of AUVs in the context of Indonesian maritime defense. The research findings indicate that the main strength of AUVs lies in their technological capability to effectively detect underwater threats. Although they have weaknesses, such as high operational costs, dependency on imported components, and limitations in battery endurance and sonar capabilities, these factors do not significantly hinder AUV effectiveness. Major opportunities arise from global technological advancements, yet challenges remain due to insufficient government policy support. The threats faced include the risk of sabotage and extreme weather conditions. Therefore, the recommended strategies are to maximize the use of technology, strengthen supporting regulations, and address logistical and financial barriers to realize effective and sustainable AUV operations in the Natuna Sea.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

The KRI Nagapasa Class is a submarine of the Indonesian Navy that has the capability to deploy sea mines, launch anti-surface missiles, and release countermeasure torpedoes. However, to face the increasingly complex dynamics of the regional strategic environment, it is necessary to increase combat power through the installation of a Sub Launch Missile weapon system. Until now, the Indonesian Navy does not have a submarine equipped with this underwater missile, especially in the Chang Bogo class. This study aims to analyze and determine the most appropriate choice of Sub Launch Missile missiles to support submarine training and operations. The method used is the Analytic Hierarchy Process (AHP) with a quantitative approach. The results of the ranking of five respondents showed that the SM-39 Exocet missile was in first place with a value of 0.46424 followed by the CM708UNB with a value of 0.41461 and the UGM-84 Harpoon with a value of 0.12115. The SM-39 Exocet missile was chosen as a priority because it has technical specifications that best meet the operational needs of the Indonesian Navy (TNI AL), as well as significantly contributing to enhancing the deterrence effect and combat capability of the Nagapasa-class submarines.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.