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Abstract. This research aims to analyze the effectiveness and reliability of production machines in the process of making public street lighting poles (PJU) at PT. XYZ The main problem faced by the company is high machine downtime so that production targets are not achieved. Therefore, a method is needed that is able to measure machine effectiveness as a whole and identify the main causes of production losses. The method applied includes Overall Equipment Effectiveness (OEE) to measure machine effectiveness based on three components, namely availability, performance and quality. The Total Productive Maintenance (TPM) approach is used to identify factors causing low effectiveness through Six Big Losses analysis. Apart from that, Mean Time To Repair (MTTR) and Mean Time Between Failure (MTBF) are calculated to determine the level of machine reliability. The data used includes machine working hours, downtime, operating time, production quantities, defective products, as well as machine damage and repair data. The analysis results are expected to show the level of machine effectiveness and identify the dominant factors causing downtime. Based on these results, improvement proposals are prepared to reduce downtime, increase machine reliability and improve production productivity Keywords: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime, Efektivitas   Abstrak. Penelitian ini bertujuan untuk menganalisis efektivitas dan keandalan mesin produksi pada proses pembuatan tiang penerangan jalan umum (PJU) di PT. XYZ Permasalahan utama yang dihadapi perusahaan adalah downtime mesin yang tinggi sehingga target produksi tidak tercapai. Oleh karena itu, diperlukan metode yang mampu mengukur efektivitas mesin secara menyeluruh dan mengidentifikasi penyebab utama kerugian produksi. Metode yang diterapkan meliputi Overall Equipment Effectiveness (OEE) untuk mengukur efektivitas mesin berdasarkan tiga komponen, yaitu availability, performance, dan quality. Pendekatan Total Productive Maintenance (TPM) digunakan untuk mengidentifikasi faktor penyebab rendahnya efektivitas melalui analisis Six Big Losses. Selain itu, dilakukan perhitungan Mean Time To Repair (MTTR) dan Mean Time Between Failure (MTBF) untuk mengetahui tingkat keandalan mesin. Data yang digunakan mencakup jam kerja mesin, downtime, waktu operasi, jumlah produksi, produk cacat, serta data kerusakan dan perbaikan mesin. Hasil analisis diharapkan dapat menunjukkan tingkat efektivitas mesin dan mengidentifikasi faktor dominan penyebab downtime. Berdasarkan hasil tersebut, disusun usulan perbaikan untuk mengurangi downtime, meningkatkan keandalan mesin, dan memperbaiki produktivitas produksi Kata kunci: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime Mesin, Efektivitas Mesin

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

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

The increasing use of mobile applications in the retail industry has generated a large volume of user reviews that contain valuable insights regarding customer experience and service quality. However, the unstructured nature of these reviews requires an automated approach to extract meaningful patterns efficiently. This study aims to perform sentiment analysis on user reviews of the Indomaret Poinku application by integrating lexicon-based labeling with machine learning classification. A total of 10,000 reviews were collected from Google Play Store and processed through a series of text preprocessing steps, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using the Indonesian Sentiment Lexicon (InSet), producing three sentiment classes: positive, negative, and neutral. The labeled data were vectorized using CountVectorizer and classified using two algorithms: K-Nearest Neighbors (KNN) and Random Forest (RF). Evaluation results show that Random Forest outperforms KNN, achieving an accuracy of 82.5%, compared to 69% for KNN. Random Forest demonstrates superior performance in handling high-dimensional sparse text features and yields more stable predictions across sentiment classes. This study contributes to the growing body of research on Indonesian sentiment analysis by demonstrating the effectiveness of combining lexicon-based labeling with ensemble learning methods, offering practical implications for developers seeking to improve the quality and user satisfaction of digital retail applications.

The development of artificial intelligence (AI) has transformed organizational landscapes and driven fundamental changes in leadership practices and strategic management. This article aims to critically examine AI-based leadership by highlighting its opportunities, risks, and implications for strategic management. The study employs a qualitative literature-based approach using an integrative literature review strategy. The data consist of secondary scholarly literature relevant to AI, leadership, governance, innovation, and strategic management, which were analyzed through qualitative thematic analysis and conceptual content analysis. The findings show that AI-based leadership creates opportunities in the form of improved decision quality, faster strategic analysis, operational efficiency, stronger innovation, and enhanced organizational agility. However, AI integration also presents risks, including algorithmic bias, limited decision transparency, technological dependency, accountability challenges, and ethical concerns. This study confirms that AI does not fully replace human leaders; rather, it fosters a hybrid leadership model that requires technical, adaptive, transformational, and ethical capabilities. The study implies that the effectiveness of AI-based leadership depends on responsible governance, organizational cultural readiness, and balanced human–machine collaboration in supporting strategic management.

This study explores the role of Business Intelligence (BI) in improving operational effectiveness and company performance amid rapid digital transformation, big data, Artificial Intelligence (AI), and data analytics development. Using a Narrative Literature Review (NLR) approach, this study analyzed 48 national and international scientific articles related to BI, firm performance, digital transformation, and big data analytics. The findings reveal that BI positively influences decision-making quality, operational efficiency, organizational agility, innovation capability, marketing effectiveness, customer relationship management, and competitive advantage. Furthermore, integrating BI with Big Data Analytics, predictive analytics, machine learning, and AI enhances organizational responsiveness to dynamic business environments. The study also identifies key success factors for BI implementation, including organizational readiness, management support, technological infrastructure, information quality, and system integration. These findings indicate that BI has evolved from a technological tool into a strategic organizational capability that supports sustainable business performance and digital transformation. This study contributes to the Business Intelligence literature by providing a comprehensive understanding of BI’s strategic role across various industries and organizational contexts.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.

PT UVW uses jigs in the vacuum metalizing process that require washing, which is currently done manually, resulting in inefficiency and risks to operators, thus, jig washer machine was created in order to reduce the risk for operators and increasing efficiency, which based on horizontal mixer. This study analyses the effect of agitator design on fluid flow patterns in a jig washing machine to obtain an optimal design. Two agitator design alternatives were developed and tested using Computational Fluid Dynamics (CFD) simulations at rotational speeds of 70, 75, and 100 rpm, followed by prototype fabrication and performance testing. Simulation results show that the second design produces a higher fluid velocity (20.4 m/s) and a more turbulent and uniform flow pattern compared to the first design (1.7 m/s). Field tests confirmed that the second design achieves higher washing effectiveness (97.14%) with reduced water consumption and shorter washing time. The optimally designed agitator washing machine increases productivity by six times compared to manual washing and reduces operator exposure to hazardous chemicals. This study recommends the second agitator design for improving washing efficiency and effectiveness.

Financial distress is a condition of declining financial health of a company that can develop gradually and lead to business failure if not detected early. With the increasing complexity of the business environment and the limitations of conventional statistical methods, Artificial Intelligence/AI is increasingly being adopted in the development of early warning systems (EWS) to predict financial distress. This study aims to examine the development of AI-based EWS research, identify the most widely used algorithms, and evaluate the effectiveness of AI models compared to conventional methods in predicting financial distress. The method used is a comprehensive systematic literature review of 15 relevant scientific articles. The results show that the paradigm has shifted from statistical models to machine learning and deep learning. Random Forest and Artificial Neural Network are the most widely used algorithms and have better predictive performance. This study offers a conceptual synthesis of the progress, effectiveness, and challenges of applying AI in predicting financial distress and opens opportunities for further research on the development of contextual and interpretative EWS.

The rapid advancement of Information and Communication Technology (ICT) has accelerated the digital transformation of public services, including land administration. However, public complaint services at the Land Office of Southwest Sumba Regency still encounter challenges such as unstructured complaint procedures, manual data processing, risk of data loss, and limited public access to clear information. These issues highlight the need for an innovative and accessible complaint information system. This study aims to design and implement a chatbot-based public complaint service information system to enhance accessibility, transparency, and service effectiveness. A qualitative research method with a system development approach was applied. Data were obtained through interviews, observations, and documentation. The system was developed using a rule-based approach with a Finite State Machine (FSM) algorithm and implemented through the Typebot.io platform. The findings indicate that the chatbot provides structured, consistent, and user-friendly information, reduces manual workload, and improves public readiness before submitting complaints directly, while supporting future integration and system enhancement.

This study is motivated by the importance of financial and asset management strategies in supporting the operational effectiveness of the Regional Disaster Management Agency (BPBD) of Central Sulawesi, which plays a strategic role in disaster mitigation, preparedness, emergency response, and post-disaster recovery. The main problems addressed are how financial and asset management strategies are implemented, to what extent they affect operational effectiveness. The objectives of this research are to analyze the applied strategies, assess their influence on operational effectiveness, and identify challenges as well as relevant solutions.The literature review refers to public financial management theories, regional asset management, and previous studies highlighting the relationship between financial governance, accountability, and public sector performance. This study employs a quantitative approach with a descriptive design. Data were collected through literature study, observation, interviews, and questionnaires distributed to BPBD staff in finance and asset divisions. The analysis included validity and reliability tests, along with multiple linear regression to examine the effect of independent variables on operational effectiveness. The findings show that BPBD Central Sulawesi’s financial management strategy in 2024 achieved a realization rate of 89–100% in most programs, although imbalances were found in certain activities such as the disaster management system arrangement, which only reached 38%. In terms of asset management, fixed assets dominate with a book value of IDR 19.6 billion, with significant growth in equipment and machinery. Regression analysis results indicate an R² value of 0.817, meaning that 81.7% of operational effectiveness is influenced by financial and asset management strategies, while the remaining 18.3% is explained by other factors.The study concludes that financial and asset management strategies significantly affect BPBD’s operational effectiveness. Nevertheless, challenges such as limited human resources, inadequate information systems, and discrepancies in budget realization require solutions through capacity building, technology utilization, and improved planning mechanisms to optimize disaster management effectiveness.

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.