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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.

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.

This research aims to analyze the role of the Special Job Exchange (BKK) in increasing the absorption of Palembang City State Vocational School alumni in the world of work. BKK is one of the units that supports the labor absorption process by connecting the world of education and the world of industry. This research uses a qualitative approach with a case study method involving primary data through in-depth interviews with BKK, alumni and collaborating companies. The research results show that BKK has a significant role in facilitating Palembang City State Vocational School alumni in obtaining employment opportunities. Apart from that, BKK also provides training and information regarding job vacancies that are relevant to the skills possessed by alumni. However, several challenges are still faced, such as a lack of certain skills needed by companies and limited collaboration networks with the industrial world. This research recommends the need to improve the quality of training and expand the cooperation network between vocational schools and the industrial world to increase the effectiveness of BKK in accelerating workforce absorption.  

Intensive exploitation of fish for caviar production currently threatens the extinction of sturgeon, one of the ancient fish species. Sturgeon egg harvesting is often carried out in inhumane ways, such as dissecting and killing parent fish, causing the natural sturgeon population to decline. The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) regulates the international trade of sturgeon and caviar, but weak law enforcement, high market demand, and widespread illegal trade all exacerbate the conservation status of this species. Conversely, caviar aquaculture, often considered an alternative, raises new bioethical issues, including long-term stress on fish, disease spread, decline in animal health, and the risk of hybridization that could threaten the genetic integrity of sturgeon populations. From a bioethical and environmental ethics perspective, it is difficult to justify the consumption of caviar as a luxury commodity at the expense of ecosystem sustainability and animal welfare. Therefore, sturgeon conservation requires a comprehensive approach that includes reducing consumer demand, improving legislation and enforcement, and preserving and restoring sturgeon's natural habitats.

The production sector at both the district and city levels is crucial for fostering structural change and boosting economic growth in specific areas. Still, many regions struggle with issues such as linking supply chains, readiness for technology, quality of labor, and efficient policies. This research intends to examine the strategies of the manufacturing sector at the district and city levels to enhance regional competitiveness and promote sustainable economic growth. The study utilizes a descriptive qualitative method based on a review of literature from academic journals, policy papers, and official statistics related to manufacturing progress. Results reveal that several important factors strongly affect regional manufacturing growth. These include the connection of local supply chains, industry strategies focused on the market, the implementation of digital and smart manufacturing methods, innovation encouraged by educational institutions and organizations, and the influence of local governments in developing an effective industrial policy atmosphere. Furthermore, creating designated industrial areas and managing operations efficiently significantly helps attract investments and boost the manufacturing output of regions. The research concludes that a cohesive and tailored manufacturing strategy for each region is vital for improving local productivity, generating jobs, and enhancing economic stability at both district and city scales.

The principle of comparative advantage explains that every country or society, like individuals, can gain benefits from their trade activities by exporting goods or services in which they have a major comparative advantage and importing goods or services in which they do not. Based on the law of comparative advantage, even though a country may be less efficient (having an absolute disadvantage) compared to other countries in the production process, the structure of industrial performance can be seen through the analysis of industrial sector behavior analyzed through various strategies such as Price, Product, and promotion. The theory of comparative advantage related to the exchange of goods is relevant as long as the traded goods are still useful. In other words, Performance is defined as the result of activities influenced by the structure and behavior within the industrial sector, where these results are often measured by the size of a company's market share or profitability in an industry. In more detail, performance can also be reflected in the form of efficiency, development (including market expansion), job creation, employee welfare, and a sense of group pride.

The problem faced is the decline in the performance of a 650 liter/minute centrifugal pump due to wear on components, especially the impeller, rolling bearings, and mechanical seals in supplying process water and clean water in industrial production systems. The planning objective is to obtain a maintenance schedule for a 650 liter/minute centrifugal pump for the operational period of 2026 and the ratio between maintenance costs and profits generated by the machine. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing centrifugal pump specifications, applying the inspection, replace, repair, and overhaul (IRRO) approach, estimating the age and price of components that are expected to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the 2026 period, and determining the ratio of maintenance costs to profits. The planning results are in the form of a maintenance schedule for the 2026 period worth IDR 4,290,000,-, a maintenance cost to profit ratio of 7.44% and the implications indicate that the machine is still suitable for use and prospective for operations in the next few years.  

The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

The development of small and medium enterprises (SMEs) and household industries is often regarded as the economic foundation of a region. However, much of the existing research in Indonesia remains focused on quantitative descriptive analysis, while providing limited attention to spatial dynamics and interregional disparities. This study aims to critically evaluate the spatial distribution patterns of SMEs and household industries at the regency and city levels, with particular emphasis on clustering tendencies, unequal distribution, and their relationships with regional characteristics. A spatial analysis approach is employed to identify spatial autocorrelation and industrial clustering patterns, which is complemented by a structural analysis of infrastructure availability, market accessibility, and regional institutional capacity. The findings indicate that the distribution of SMEs and household industries is not geographically random, but rather forms clusters that are predominantly concentrated in areas with higher levels of accessibility and economic activity. This condition reflects spatial inequality that may exacerbate development disparities between regencies and cities. Furthermore, the results reveal that uniform industrial development policies are insufficient to accommodate the diverse spatial characteristics across regions. Therefore, this study underscores the importance of formulating spatially informed and context-sensitive policies for the development of SMEs and household industries in order to promote more balanced and sustainable regional industrial development.

Penelitian ini bertujuan untuk mengevaluasi implementasi Tingkat Komponen Dalam Negeri (TKDN) pada pekerjaan elektrikal proyek konstruksi bangunan gedung pemerintah di Indonesia. Metode yang digunakan adalah pendekatan kuantitatif deskriptif–evaluatif melalui studi kasus pada delapan lokasi proyek pemerintah. Evaluasi dilakukan terhadap capaian TKDN pada pekerjaan MVMDP, LVMDP, pemasangan kabel, pencahayaan, genset, trafo distribusi, dan biaya transportasi berdasarkan dokumen kontrak dan ketentuan regulasi yang berlaku. Hasil penelitian menunjukkan bahwa capaian TKDN antar proyek sangat bervariasi, dengan kecenderungan nilai TKDN rendah pada komponen berteknologi tinggi dan proyek di luar Pulau Jawa. Temuan ini mengindikasikan bahwa keterbatasan material lokal dan ketimpangan distribusi industri menjadi tantangan utama dalam implementasi TKDN. Penelitian ini memberikan kontribusi empiris dalam mengevaluasi kebijakan TKDN pada pekerjaan elektrikal serta menjadi dasar rekomendasi kebijakan peningkatan TKDN yang lebih terarah.

Smart industry has become an important trend in the development of Industry 4.0, especially in promoting the creation of efficient systems in the manufacturing sector. Various countries and studies are encouraging the application of technologies such as IoT, digital twins, artificial intelligence, and smart factories to improve industrial efficiency and sustainability. Therefore, studies related to smart industry are important and necessary especially on the context of smart manufacturing in order to see the direction of future research trends. This study uses a qualitative approach with literature data from the Scopus database covering the period 2020 to 2025. Research trend analysis was conducted through data processing using Bibliometric analysis in R Studio and the VOSviewer applications. To identify the latest research trends regarding smart industry, particularly in the context of Industry 4.0 and smart manufacturing, this analysis can provide a comprehensive picture of future research developments and directions within a global context.

The implementation of industrial engineering technologies, such as automation, the Internet of Things (IoT), artificial intelligence (AI), and lean manufacturing, has significantly transformed human resource (HR) capabilities in the production sector, particularly in response to the Industry 4.0 paradigm. This study aims to examine the relatively low level of technology adoption in Indonesia, estimated at only 6–20% of manufacturing companies, and its impact on the development of HR competencies. The analysis focuses on changes in technical skill requirements, including digital literacy, data analytics, and technology-based decision-making, as well as the shift in job roles from manual tasks to more strategic functions. This research employs a qualitative descriptive approach grounded in sociotechnical systems theory and the strategic alignment model. The findings indicate that existing skill gaps can be addressed through continuous upskilling and reskilling programs, supported by strengthened triple helix collaboration among government, industry, and educational institutions. The implementation of these strategies has been shown to increase productivity by approximately 30–72% and enhance the competitiveness of the national production sector in the global industrial landscape.  

This study aims to describe the production process, business strategy, and product offerings of the COSIK chocolate factory located in Maumere, Sikka Regency, East Nusa Tenggara. The COSIK chocolate factory utilizes local raw materials, such as cocoa beans, obtained directly from local farmers, and implements a processing process without the use of chemical preservatives. This approach not only increases the added value of local products but also supports community economic empowerment through partnerships with cocoa farmers in the region. Furthermore, the use of simple yet modern technology enables a hygienic production process that meets quality standards. The results show that the COSIK chocolate factory contributes to improving the quality of local cocoa, developing innovative products based on natural ingredients, and strengthening the region's identity as a producer of quality chocolate. Thus, COSIK serves not only as a production unit but also as a sustainable business model that integrates environmental, social, and economic aspects in a food processing industry based on local resources.

This study aims to analyze the effect of the unemployment rate on economic growth in Riau Islands Province in 2024 using a descriptive–quantitative approach based on official data published by Statistics Indonesia (BPS). The analysis was conducted through year-on-year and quarter-to-quarter measurements of Gross Regional Domestic Product (GRDP), as well as the dynamics of the Open Unemployment Rate (OUR) throughout the year. The findings show that the average economic growth of the province in 2024 reached 5.02 percent, while the unemployment rate decreased from 6.94 percent in February to 6.39 percent in August. Pearson correlation analysis resulted in r = –0.98 (p < 0.05), indicating a very strong negative relationship between unemployment and GRDP growth. Meanwhile, the simple linear regression model produced the equation Ŷ = 5.45 – 0.22X with an R² of 0.96, implying that 96 percent of the variation in economic growth can be explained by changes in the unemployment rate. These findings are consistent with Okun’s Law, suggesting that increased industrial and trade activities in the province contribute to reducing unemployment. The study concludes that strengthening sectoral job creation, improving labor productivity, and enhancing labor market monitoring are crucial to support inclusive and sustainable economic growth in the Riau Islands Province.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

The cigarette industry in Dawe District, Kudus, has been the backbone of the local economy for decades. However, this heavy dependence on a single industrial sector creates economic vulnerability, especially when production fluctuations or policy changes affect the tobacco industry. An economic empowerment program through agricultural entrepreneurship skills is highly relevant in this context. Modern agriculture using wick hydroponic technology and mini Nutrient Film Technique (NFT) offers an appropriate solution because it has characteristics that are suited to the geographic and socioeconomic conditions of the Dawe community. This PKM program has proven that economic empowerment through technology and skills transfer can be an effective strategy for increasing community income. Based on the results achieved, it is recommended to replicate similar programs in other areas with similar characteristics. Furthermore, ongoing monitoring and evaluation are necessary to ensure the sustainability of the businesses pioneered by participants. Collaboration with relevant agencies and microfinance institutions can strengthen participants' access to capital for broader business development.

The objective of this study is to directly analyze and illustrate the compositioneof the auditecommittee, which consists of financial knowledge, independence and the quantity of members on the committee, concerning the financial statement quality of energy sector industries listed on the IDX in 2023-2024.High-quality financial statements are a crucial component reflecting the outcome of the accounting process and are vital for stakeholders in decision-making. Despite regulatory requirements for audit committees, corporate financial statements in Indonesia often contain earnings management or accounting irregularities, indicating that the audit committee's very existence is insufficient to guarantee financial statements' quality. A numerical approach with a causal-comparative approach is utilized in this investigation. The secondary quantitative data are obtained from companies’ yearly financial statements, annual reports, and corporate governance disclosures published on the official IDX website. The data are examined using EViews software for panel data regression, going through many steps, including descriptive statistics, classical assumption testing, panel data model selection, and regression analysis for hypothesis testing. The audit committee's size, objectivity, and financial acumen make up the study's independent variables. Meanwhile, financial statement quality as the dependent variable is measured through earnings quality proxy using the discretionary accruals calculation approach (Jones model or Modified Jones model). Specifically, this research seeks to deliver theoretical and practical benefits for regulators in formulating corporate governance policies, give companies a comprehension of the importance of an effective audit committee, and help investors make informed investment choices.

General education at Islamic Religious Higher Education Institutions (PTKI) plays an important role in developing students’ critical thinking skills so they can analyze religious and social issues reflectively and contextually. However, learning practices in PTKI are still dominated by teacher-centered approaches and low cognitive achievement, which limits the development of analytical, evaluative, and problem-solving abilities. Meanwhile, studies show that integrating digital media, particularly video, can improve conceptual understanding, motivation, and cognitive engagement, yet a digital video-based meaningful learning model specifically designed for the PTKI context remains limited. Therefore, this study aims to formulate a Digital Video-Based Meaningful Learning Model (MPBBVD) to enhance students’ critical thinking skills. This research employed a literature review method by analyzing books, scientific articles, research reports, and digital sources related to meaningful learning, constructivism, multimedia learning, digital video use, and critical thinking development. Data were analyzed descriptively and qualitatively to identify the theoretical foundations, learning syntax, and design principles of the model. The findings indicate that MPBBVD integrates Ausubel’s Meaningful Learning theory, constructivist perspectives from Piaget, Vygotsky, and Bruner, Mayer’s Multimedia Learning theory, and the critical thinking framework of Facione and Ennis. The model uses digital video as the main learning medium and applies the ACTIVE stages (Attend, Connect, Think, Interpret, Verify, Evaluate) to promote contextual and interactive learning. Integrating pre-, during-, and post-video activities supports knowledge construction, reduces cognitive load, and increases student engagement, contributing theoretically and practically to improving critical thinking in PTKI in the Industrial Revolution 4.0 era.

This study examines the legal dilemmas in Employment Termination (PHK) within the banking sector, arising from the tension between Labor Law and banking regulations. A normative analysis reveals that termination practices are often caught in a conflict between the principle of worker protection and the bank's obligation to comply with prudential principles and maintain its financial health. The most critical points of friction lie in termination due to efficiency measures, serious disciplinary violations, and the clash between banking secrecy and the evidentiary process in the Industrial Relations Court. It is concluded that the lack of harmonization guidelines creates legal uncertainty, necessitating more concrete regulatory synergy between labor authorities and the Financial Services Authority (OJK) to achieve certainty and fairness for all parties involved.