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Quality culture in educational institutions has become a strategic issue to improve learning service quality and school accountability. However, its implementation still faces challenges such as limited human resources, resistance to change, and weak sustainable quality management strategies. This study aims to answer the question: how are the implementation strategies and supporting factors in strengthening quality culture within educational institutions based on previous research findings? This study employs a Literature Review (LR) method by collecting scientific articles from Google Scholar and Publish or Perish (PoP) using the keyword “budaya mutu” (quality culture) with a publication range from 2020 to 2025. From a total of 30 identified articles, a screening and eligibility process was conducted, resulting in 5 main articles analyzed thematically. The synthesis reveals three key themes: (1) the role of leadership and Total Quality Management (TQM) in reinforcing quality culture, (2) the application of the PPEPP/MBBS cycle for sustainable educational quality, and (3) supporting factors and challenges in developing a culture of quality. In conclusion, strengthening quality culture requires effective leadership, systematic management strategies, and cross-sector collaboration; future research is encouraged to develop field-based intervention models to test the effectiveness of quality culture strategies.

The integration of blockchain technology with Multi-Agent Reinforcement Learning (MARL) presents a promising solution for optimizing resource allocation and ensuring security in decentralized network environments, particularly in 5G and 6G network slicing. This research proposes a model that combines the security features of blockchain with the adaptive, decentralized decision-making capabilities of MARL. Blockchain ensures the integrity and transparency of resource allocation by providing a secure, tamper-proof ledger for transaction validation, while MARL allows agents to dynamically allocate resources based on real-time network conditions. The simulation results demonstrate significant improvements in resource allocation efficiency, fairness among users, and resilience to cyberattacks. By combining these two technologies, the proposed model overcomes many of the challenges posed by traditional centralized systems and offers an enhanced, secure, and fair solution for resource distribution in future mobile networks. However, scalability remains a challenge, especially in large-scale networks where transaction processing and consensus overhead can create bottlenecks. Additionally, training complexity in MARL models presents computational challenges, particularly in highly dynamic network environments. The model's performance trade-offs, including the balance between high security and system overhead, are also discussed. Future research should focus on optimizing blockchain consensus mechanisms to improve scalability and enhancing MARL model training techniques to reduce computational costs and improve real-time decision-making. This integration holds significant potential for revolutionizing resource allocation in 5G and 6G networks, enabling more efficient, secure, and fair management of network resources in the increasingly complex and decentralized digital ecosystem

This research aims to develop a web-based e-learning application at Sekolah Dasar Masehi Tanggaba by applying the Waterfall software development model. The background of this study stems from the need for interactive, flexible, and easily accessible learning media in the digital era, considering that conventional face-to-face methods are still predominantly used in schools. The Waterfall model was chosen because it provides a systematic workflow through the stages of requirements analysis, system design, implementation, testing, and maintenance. Data collection was carried out through observations, teacher interviews, and documentation studies to identify the actual needs of users.The results of this study produced an e-learning application with key features, including learning material management, interactive quizzes, discussion forums, and automated assessment. Teachers can upload structured materials and assignments, while students can access learning content, complete exercises, and interact through forums more flexibly. System testing was conducted using the black box method to ensure that each function runs according to user requirements and specifications. Based on the testing results, the application was proven to function properly, be practical to use, and effective in supporting the learning process. Teachers found it helpful in managing materials and assignments, while students experienced more independent and interactive learning that was not limited to the classroom. Therefore, the developed e-learning application can be considered valid, practical, and effective as a supporting learning medium in elementary schools and has the potential to improve learning quality in the digital era.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

This study examines the application of systems thinking in modern healthcare organizations, with a particular focus on Peter Senge’s Fifth Discipline. Using a systematic literature review of 20 selected articles, the study investigates the potential and challenges of applying systems thinking across key areas such as patient safety, service integration, resource management, and innovation. Findings indicate that systems thinking provides a holistic framework to better understand and address the inherent complexity of healthcare systems. By fostering interconnections among organizational components, it enhances the capacity to improve performance, ensure patient-centered care, and support sustainable change. Nevertheless, implementation is hindered by obstacles such as resistance to change, lack of shared understanding, and difficulties in measuring systemic outcomes. To address these barriers, the study highlights strategies including leadership development, staff training, and the establishment of appropriate evaluation tools. These measures strengthen organizational readiness and support a culture of continuous learning. The study concludes that systems thinking offers valuable insights for healthcare organizations to adapt to evolving challenges, but its success requires long-term commitment, supportive leadership, and systematic implementation. Future research should further explore the integration of systems thinking with emerging digital health technologies and assess its long-term impact on health outcomes and organizational resilience.

This community service program was implemented to overcome the limited technical capacity of teachers and students at SMK Negeri Jambu in mastering the installation and programming of battery-based electric vehicles, which is a crucial skill in preparing competent human resources for the rapidly evolving green vehicle industry. The program specifically aimed to enhance participants’ knowledge and skills in several key areas, including the installation of electric motor control systems, management of battery energy, and maintenance of essential electric vehicle components. To achieve these goals, the activities were carried out using a blended method that integrated lectures, demonstrations, and direct hands-on practice, supported by continuous mentoring and systematic evaluation. In addition, QR Code–based learning resources were incorporated to provide participants with quick and efficient access to digital instructional materials, allowing for more flexible and independent learning. The evaluation results revealed significant progress in both the theoretical understanding and practical abilities of the participants. They were able to demonstrate improvements not only in operating and maintaining electric vehicle systems but also in identifying problems and performing troubleshooting independently. These findings suggest that the program effectively contributed to building sustainable competencies for teachers and students, equipping them with relevant skills aligned with the needs of the green automotive industry, while also promoting technological adaptation and innovation in vocational education.

Indonesian EFL senior high school students. Traditional EFL instruction often emphasizes lower-order cognitive skills, limiting students' ability to analyze, evaluate, and create in English. This research explored student experiences with PBL and its impact on HOTS development. Semi-structured interviews were conducted with five 11th-grade students participating in environmental-themed PBL activities. Thematic analysis identified patterns in student experiences and perceptions. Findings reveal that students perceive PBL as significantly more engaging than traditional instruction, fostering enhanced motivation, deeper understanding, and collaborative competencies. Students demonstrated active engagement in higher-order cognitive processes through research activities, critical discussions, and creative project development. Collaborative dimensions particularly enhanced analytical and evaluative thinking as students examined diverse perspectives. Implementation challenges emerged, including time management difficulties, group dynamics issues, resource limitations, and presentation anxiety. These challenges highlight PBL's complexity and the need for comprehensive support systems. The study concludes that while PBL effectively promotes HOTS development in EFL contexts, successful implementation requires systematic support, including project management instruction, collaborative skills development, adequate technological resources, and continuous professional development for educators.  

The Merdeka Learning - Merdeka Campus (MBKM) policy is a groundbreaking initiative in Indonesia’s higher education system, aiming to create more flexible learning opportunities, connect students with the workforce, and integrate experiential learning. The key strength of this policy lies in its ability to empower students to develop independent learning through extracurricular activities that align with their competencies. At Yudharta Pasuruan University, MBKM has been adopted as an important strategy to enhance the quality of education while preparing graduates to be adaptive and competent in an ever-changing job market. The main aim of this research is to analyze how the planning, implementation, and evaluation of the MBKM program at Yudharta Pasuruan University contribute to improving student independence in learning. The focus of this study includes examining curriculum management, faculty support, and the university's partnerships with external organizations, which are all vital components of the MBKM program. This research uses a qualitative approach, with data collected through interviews, observations, and document analysis. The data analysis process involves data reduction, data presentation, and drawing conclusions. The results of the study indicate that Yudharta Pasuruan University has successfully implemented MBKM through clear policy planning, curriculum adjustments, and the strengthening of external partnerships. Students have gained significant benefits, including increased independence in learning, enhanced critical thinking skills, improved adaptability, and valuable real-world experiences in their chosen fields. These outcomes show that the MBKM program at Yudharta Pasuruan University plays a critical role in transforming higher education, making it more relevant, sustainable, and aligned with the needs of both students and the workforce. This research underscores the importance of MBKM in encouraging the development of a more independent and competent generation of graduates.

E-learning has become a vital pillar in higher education, especially since the COVID-19 pandemic forced a sudden shift towards digital learning methods. This article examines the effectiveness of e-learning platforms through a comparative literature review (2020–2025), focusing on Moodle, Google Classroom, Microsoft Teams, Zoom, and CIDOS 4.0 as the local platform. The study evaluates aspects of student engagement, accessibility, assessment, and technological innovation. Findings reveal that although e-learning enhances flexibility, challenges remain in terms of the digital divide, system usability, and reliability of assessment. This review highlights that Moodle provides strong interactive and assessment features, while Google Classroom offers simplicity and accessibility but lacks advanced analytics. Microsoft Teams supports collaboration and integration with Office 365, yet requires stable internet. Zoom is highly effective for synchronous interaction but less suitable for task management. CIDOS 4.0 addresses Malaysia’s TVET context but continues to face issues with server stability and user interface design. Beyond platform-specific strengths and weaknesses, the analysis emphasizes that student engagement, fair access, and reliable evaluation methods are central to e-learning effectiveness. Hybrid approaches that combine face-to-face and online learning are identified as the most effective strategy within the Malaysian higher education context. The study concludes that institutions must not only choose appropriate platforms but also enhance digital literacy, invest in infrastructure, and explore innovations such as AI and gamification. These measures can maximize e-learning’s potential to improve teaching, learning quality, and the broader digital transformation of higher education.

PT. Surya Pratista Hutama, as a company committed to improving the quality of its human resources, continues to strive to provide innovations to support employee competency development. One strategic step taken is the implementation of a Learning Management System LMS as a form of learning innovation in the digital era. This LMS implementation is not only intended as a means of knowledge transfer, but also as a medium to create a culture of sustainable learning within the company environment. The observation conducted aims to determine the extent to which the LMS can support the learning process in the company, with a focus on its effectiveness, challenges, and impact on improving employee competency. The method used is a descriptive study with a qualitative approach, through interviews, direct observation, and documentation related to digital-based learning activities. The observation results show that the implementation of the LMS provides various benefits, including easy access to learning anytime and anywhere, flexibility that allows employees to adjust their learning schedules to work activities, and increased motivation and independence in learning. However, there are several obstacles that require attention, such as limited digital literacy among some users, lack of direct interaction between instructors and participants, and the need to adjust content to be more relevant to daily work demands. Overall, the LMS has great potential to improve the effectiveness of training at PT. Surya Pratista Hutama.

Cahaya Amal Soleh Foundation is a social institution that focuses on community service through various institutional programs engaged in social, education, and community empowerment. However, a number of operational staff who do not have a financial background have difficulty in understanding and managing financial reports effectively. This study aims to assist staff through the On-the-Job Learning and Development Program (OLDP) themed Finance for Non-Finance with the SIAPIK (Islamic Boarding School and Social Charity Accounting Information System) application approach. The method used is a qualitative approach with a participatory model, involving direct training, transaction recording simulations, and evaluation of participant understanding. The results of the assistance showed a significant increase in staff understanding of basic accounting principles, transaction recording, and SIAPIK-based financial reporting. These findings indicate that a practical approach through an application based on the foundation's needs can improve the financial capacity of non-accounting staff. The implications of this activity reinforce the importance of digitizing the financial system of social organizations and the need for ongoing training for human resources so that accountable and transparent financial governance can be realized sustainably. In addition, the results of this program highlight that the integration of technology and participatory training can overcome barriers in financial literacy among non-finance staff. The SIAPIK application provides an accessible interface and structured modules that are easy to understand, thereby reducing the complexity usually associated with financial management. Staff who previously felt unconfident in processing financial transactions gradually gained competence and independence in applying accounting procedures. The training also fostered collaborative learning, where participants supported each other in solving case simulations, enhancing not only technical knowledge but also teamwork and problem-solving skills. Furthermore, the foundation’s management expressed that the program positively contributed to institutional performance by ensuring more reliable and transparent financial reporting.

Chat Generative Pretrained Transformer (ChatGPT) has rapidly gained popularity and is increasingly utilized across various fields, including education, where it plays a significant role as a supporting tool for academic tasks. In the educational context, ChatGPT can assist students in preparing and completing a wide range of assignments, providing quick access to structured information and alternative perspectives. This study was designed to explore the views of both students and lecturers on the integration of ChatGPT in academic activities, specifically as a support system for handling student assignments. Employing a descriptive qualitative approach, data were collected through interviews with 5 students and 6 lecturers from the Business Education Study Program at the State University of Surabaya. The findings reveal contrasting yet complementary perspectives. From the students’ point of view, ChatGPT offers considerable benefits, particularly in enhancing independence, efficiency, and time management when working on lecture assignments. However, these advantages are accompanied by drawbacks, such as decreased creativity, reduced critical thinking, and diminished interest in consulting traditional learning resources like books. On the other hand, lecturers acknowledge the usefulness of ChatGPT in simplifying student work and accelerating the completion of assignments but express concerns regarding the overreliance on artificial intelligence, which could potentially hinder the development of essential academic skills. Overall, the research suggests that while ChatGPT presents valuable opportunities to enhance learning processes, it must be applied thoughtfully, with careful guidance from educators to balance efficiency with the cultivation of creativity, critical thinking, and academic integrity.

This study aims to analyze the improvement functions and principles of education management in supporting digital transformation, emphasizing the use of technologies such as school management systems (SIS), learning management systems (LMS), and performance monitoring applications. The research method used was a literature review of 15 scientific articles published between 2020 and 2024, which discussed relevant issues related to digital education management. The results of the study indicate that the main functions of management in education, namely planning, organizing, directing, and controlling, are increasingly important when integrated with the principles of effectiveness, efficiency, innovation, and participation. The planning function emphasizes the development of school digitalization strategies, organizing plays a role in allocating human and technological resources, directing includes empowering teachers and students in the use of technology, while controlling functions to ensure the quality of education services is maintained during the digital transformation process. In addition, adaptive education management principles are essential. Effectiveness emphasizes the measurable achievement of digitalization goals, efficiency highlights the optimal use of resources, innovation encourages the creation of creative technology-based learning strategies, and participation invites all education stakeholders to be actively involved. Key factors supporting successful digital transformation include infrastructure readiness, improved teacher digital competency, visionary and adaptive leadership, and government policy and political support. However, this study also identified several challenges, such as disparities in access to digital infrastructure between regions, limited digital literacy among teachers and education personnel, and resistance to change. Therefore, improving the functions and principles of educational management in line with technological developments is believed to strengthen the quality, equity of access, and competitiveness of education in the digital era.

This study evaluates the effectiveness of the SAP accounting information system in supporting the implementation of the Independent Learning–Independent Campus (MBKM) Internship Program at PT Japfa Comfeed Indonesia Tbk, Gedangan Unit. SAP, as an integrated Enterprise Resource Planning (ERP) system, was adopted to replace the previous desktop-based system, which had significant limitations, such as restricted local access, dependence on additional licenses, and lower integration capabilities across departments. The main objective of this research is to determine how SAP contributes to improving organizational efficiency while simultaneously providing practical learning opportunities for students participating in the MBKM internship program. A descriptive qualitative approach was employed, with data collected through direct observation of company operations, semi-structured interviews with student interns and field supervisors, and analysis of company documentation. The findings indicate that the use of SAP has enhanced efficiency, accuracy, and speed in recording and reporting financial transactions. Through their internship, students engaged directly in business processes, particularly in handling purchase requisitions, reimbursement procedures, and asset management activities. This exposure enabled students to develop a deeper understanding of integrated financial systems and the workflow of a large-scale agribusiness corporation. Although several technical challenges were encountered, particularly in the form of temporary network disruptions, such issues did not substantially hinder the system’s overall performance. The availability of prompt technical support from the company’s IT team minimized potential negative impacts and ensured system continuity. Beyond improving corporate financial governance, SAP also offered valuable experiential learning for interns, bridging theoretical knowledge acquired in academic settings with real-world industrial applications. Overall, the study concludes that SAP functions not only as a strategic enterprise tool for the company but also as an effective educational medium that supports the success of the MBKM internship program.

Electricity has emerged as an essential requirement in modern life. As demand escalates, electricity costs rise, making wastefulness a drain on financial resources. Consequently, forecasting electricity usage can enhance our management of consumption. This study presents an IoT-based monitoring and forecasting system for electricity consumption. The system comprises two NodeMCU micro-controllers, a PZEM-004T sensor for collecting real-time power data, and three relays that regulate the current flow to three distinct electrical appliances. The data gathered is transmitted to a web application utilizing the k-Nearest Neighbor (k-NN) algorithm to forecast future electricity usage based on historical patterns. We evaluated the system's performance using four weeks of electricity consumption data. The results indicated that predictions were most accurate when the user’s daily consumption pattern remained stable, achieving a Mean Absolute Error (MAE) of approximately 1 watt and a Mean Absolute Percentage Error (MAPE) ranging from 1% to 1.7%. Additionally, predictions were notably precise during the early morning hours (3:00 AM to 8:00 AM) when k=6 was employed. This study demonstrates the effectiveness of integrating IoT-based systems with machine learning for real-time energy monitoring and forecasting. Furthermore, it emphasizes the application of data mining techniques within embedded IoT environments, providing valuable insights into the implementation of lightweight machine learning for smart energy systems.

This study also highlights the importance of ongoing professional development for pre-service teachers, emphasizing that preparation for handling diverse classroom needs is crucial for effective DI implementation. Pre-service teachers identified the need for more targeted training in understanding students' individual learning styles and needs, especially when dealing with large classrooms and limited resources. The findings also suggest that collaborative learning among educators, through workshops and peer mentoring, plays a significant role in overcoming the challenges of DI. In addition, institutional support was identified as a key factor in facilitating the effective use of DI. Participants reported that a lack of resources, such as teaching aids and time, was a primary barrier to the full implementation of DI. Schools and educational institutions were urged to provide more resources and structured support systems to enable teachers to tailor their teaching methods to accommodate the varying abilities of their students. Furthermore, the integration of technology was highlighted as an effective strategy to differentiate instruction, allowing teachers to cater to diverse learning needs in a more flexible and engaging manner. For instance, digital tools such as learning management systems and interactive applications were utilized by some participants to create personalized learning paths for students, fostering both engagement and motivation. These strategies not only helped overcome some of the limitations faced by the teachers but also allowed students to engage more actively with the learning material. Ultimately, this study emphasizes that the successful implementation of Differentiated Instruction requires a collaborative effort between pre-service teachers, their educational institutions, and ongoing support systems.

This community service program is designed to address the need for digitalization in administrative management and improve digital literacy in schools. The main focus of the activity is training on the use of Google Workspace as an integrated knowledge management system to improve work efficiency and collaboration among staff. Problems faced by schools include the continued use of manual administrative systems and a low understanding of how to use digital platforms. The training was conducted as a workshop with a learning-by-doing approach, allowing participants to learn directly and contextually. The training material covered the operation of various Google Workspace features, such as Google Forms, Sheets, Calendar, Docs, Drive, and Meet. This activity was designed so that participants not only understand the function of each application but also are able to integrate them into daily administrative activities. The training evaluation was conducted through pre- and post-tests, which showed a significant increase in participants' technical understanding. In addition to improving digital competency, this training also encouraged a shift in work culture towards a more collaborative, efficient, and data-driven one. The program's success demonstrates that digital transformation in educational environments can be achieved through an educational, participatory approach tailored to local needs. With positive results, this activity has the potential to be replicated in other educational institutions as a sustainable strategy for cloud-based administrative management. This training is proof that adopting digital technology in schools can strengthen administrative governance comprehensively and sustainably.

This research was conducted from June to July 2025 in Binjai City, with the primary focus being analyzing the readiness of the Binjai City Regional Disaster Management Agency (BPBD) to implement a flood early warning system utilizing artificial intelligence (AI). The data collection process was conducted through a literature review, which involved reviewing various theories and previous research results regarding the application of AI and Internet of Things (IoT) technology in the context of disaster mitigation. Based on the results of the study, it was found that the use of technologies such as ultrasonic sensors, microcontrollers, fuzzy logic, and automatic notification systems can provide real-time warnings with a high level of accuracy and a fast response. This system enables early detection of rising river levels through automatic measurements, intelligent data processing, and sending notifications to authorities and affected communities within seconds. By integrating historical data and machine learning-based predictions, this system is also able to depict potential flooding before it occurs, providing a longer response time for evacuation. However, the readiness of the Binjai City BPBD still faces various challenges, such as limited digital infrastructure, the need for human resource training in the technology field, and inadequate budget allocation. Therefore, cross-sector collaboration and ongoing policy support are needed for optimal implementation of this system. The use of AI and IoT in early warning systems is not only technically relevant but also urgent in the face of increasing climate change and flood risks. A strategy involving cross-sector collaboration between government, academia, and the private sector is needed to develop an adaptive and sustainable early warning system.

Human resource management (HRDM) is a crucial factor in supporting the success of companies in the digital era which is full of rapid change and global competition. Companies are required to not only adapt to technology, but also be able to build strategic and innovation-oriented MSDM systems. This study aims to examine the strategic role of MSDM in PT Telkom Indonesia (Persero) Tbk in supporting digital transformation and achieving organizational goals. Using a literature study approach, data was obtained from books, scientific journals, company annual reports, and official online sources. The results of the study show that PT Telkom Indonesia manages human resources strategically through various initiatives, such as the Great People Development Program (GPDP), digitization of the Human Capital Management (HCM) system, and strengthening the value-based work culture of "The Telkom Way" which includes Integrity, Enthusiasm, and Totality. This approach has a positive impact on increasing employee engagement, productivity, innovation, and the company's competitiveness in the highly competitive telecommunications industry. In addition, the implementation of digital technologies in HR management, such as the use of analytics platforms for performance measurement and online learning systems, has helped companies in accelerating the internal transformation process. However, challenges such as the digital divide between employees, differences in expectations between generations, and resistance to change are still obstacles that need to be addressed with a more inclusive, adaptive, and employee-well-being-oriented management strategy. This research provides the implication that the integration of technology, organizational culture, and talent development is an important combination in creating a resilient and sustainable HR system. The limitations of the research lie in the literature approach that has not touched the empirical aspect, so it is recommended that there be field studies or in-depth interviews with related parties in the next research to get a more holistic and accurate picture.

The Paiton Steam Power Plant (PLTU) is one of the main sources of electrical energy in East Java, which plays a vital role in maintaining a sustainable electricity supply. The reliability of generator units is a key element in maintaining stable energy distribution. However, the high frequency of sudden generator failures poses serious challenges, such as increased downtime and increased maintenance costs. To address these challenges, this study aims to design a generator maintenance prediction model based on the Naive Bayes algorithm with a predictive maintenance approach. This study uses historical maintenance data and key sensor parameters such as temperature, oil pressure, and vibration as input. The data is analyzed through several stages, namely data preprocessing, selection of relevant features, and labeling generator conditions into three categories: Normal, Warning, and Critical. The Naive Bayes model is trained to classify the data probabilistically to generate predictions of future generator conditions. Model evaluation using accuracy metrics and a confusion matrix shows that the model successfully achieved an accuracy rate of 89% and was able to provide early warnings of potential failures up to 3 days before failure occurs. The implementation of this system is expected to support the shift in maintenance strategies from reactive and scheduled systems to data-driven predictive systems. Implementing failure predictions allows the technical team at the Paiton PLTU to conduct planned maintenance, avoid sudden disruptions, and extend equipment lifespan. Thus, this model has the potential to reduce operational downtime by up to 25%, while providing significant savings in operational and logistics costs. This research also shows that integrating machine learning technology into energy facility management can improve the efficiency and resilience of the overall electric power system.