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Processing plastic waste into fuel oil with pyrolysis technology. Research on plastic oil as an alternative fuel has been widely conducted. One of them is research on processing plastic bag waste into fuel oil with pyrolysis technology. In this study, a set of pyrolysis equipment was made by Lecturers and Students of UMNU Kebumen. The plastic waste used in this study was 1 kg of plastic bag. Meanwhile, the pyrolysis process used a temperature of 250oC-300oC. The fuel oil produced in the pyrolysis process of this study was approximately 400 ml. The average density of fuel oil from plastic bag waste was 0.733 gr/ml. The results of this density calculation are in the possibility of the density of gasoline, namely 0.710 gr/ml to 0.770 gr/ml. This pyrolysis process shows great potential for converting plastic waste into an environmentally friendly alternative energy source. Furthermore, the efficiency of this pyrolysis technology can be improved by adjusting the temperature and processing time, as well as by selecting a wider variety of plastic types. This technology has the potential to be applied more widely in plastic waste management within the community as a solution to reduce environmental pollution while generating renewable energy.

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

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.

The development of the Industrial Revolution 4.0 to the Society 5.0 era presents new challenges for education, including Islamic Religious Education (PAI). PAI is required not only to transfer religious knowledge but also to shape character, morality, and 21st-century competencies. This study aims to examine the philosophical foundations, characteristics, and application of PAI learning models to ensure their relevance in modern education. The research employed a qualitative approach with a library research design, involving the exploration of primary and secondary literature, critical reading, systematic note-taking, and content analysis validated through triangulation of classical and contemporary sources. The findings indicate that PAI learning models are conceptual, theoretical, systematic, and rooted in religious, philosophical, psychological, and sociological foundations. Four main models behavioristic, information processing, social, and humanistic each have strengths and limitations, yet their integration is relevant to forming insan kamil, a holistic human being balanced in cognitive, affective, psychomotor, spiritual, and social aspects. Therefore, developing PAI learning models based on Islamic philosophy and adaptive to technological advancements is a strategic necessity to face global challenges.

This study aims to analyze the process of processing fruit peel waste from juice traders into eco-enzymes as an effort to reduce environmental pollution. The research uses qualitative descriptive methods through direct observation, documentation, and literature studies. Fruit peel waste is fermented at a ratio of 1:3:10 (molasses : fruit waste : water) for three months under anaerobic conditions. The fermentation results show good eco-enzyme characteristics, characterized by a pH of 3, a fresh sour aroma, and a brownish color as an indicator of fermentation success. These findings show that the process of processing eco-enzymes from fruit peel waste can be done simply, cheaply, and environmentally friendly. In addition, the use of eco-enzymes has the potential to reduce the volume of organic waste disposed of in landfills and reduce water and soil pollution. The resulting eco-enzyme also has potential applications as natural cleaners, liquid fertilizers, and odor controllers. Thus, the treatment of fruit peel waste not only supports sustainable environmental management practices, but also encourages the implementation of the circular economy as well as community empowerment ecologically and economically.

Halal tourism has continued to grow as a highly attractive segment for Muslim travelers, particularly in the provision of Sharia-compliant accommodation such as Islamic hotels. This study aims to examine consumer perceptions of halal food-and-beverage facilities and Sharia-based operational standards in Islamic hotels across the Greater Jakarta area (Jabodetabek). A descriptive quantitative approach was employed, involving 150 respondents who had stayed in Sharia hotels. Data were collected through closed-ended Likert-scale questionnaires and analyzed using the Weighted Mean Score (WMS) technique to evaluate respondents’ assessments of each indicator. The results indicate that consumer perception of halal food-and-beverage facilities falls into the very high category, with average scores exceeding 4.838. Consumers acknowledged that Sharia hotels maintain halal assurance, hygiene, and food safety through proper processing and the availability of halal certification. Regarding operational aspects, consumer perception is also classified as very good, with an average score of 4.606, particularly for policies prohibiting unmarried couples from sharing a room and banning entertainment deemed inappropriate or immoral. However, the use of Sharia-compliant financial institutions still requires improvement. Overall, the findings affirm that Sharia hotels in Jabodetabek have successfully implemented most Sharia principles, although further enhancement of internal operational practices is needed to achieve more comprehensive Sharia compliance.

The use of e-wallets is increasing in Indonesia, especially during the Covid-19 pandemic. The most popular digital wallets in Indonesia, such as Go-Pay, OVO, ShopeePay, Dana, and LinkAja, have adapted the mobile banking system, making it easy for users to download the app through the Play Store or App Store. According to Insight Asia, DANA is ranked third in terms of number of users after Gopay and OVO, with 221 respondents from major Indonesian cities. As an e-wallet platform created by PT Espay Debit Indonesi Koe Indonesia in 2018, DANA, as a local company, attracts people's interest and has become a popular choice. This study aims to identify the relationship and influence between Perceived Usefulness and Perceived Ease of Use on Decision to Use E-wallet, especially DANA, with Intention to Use E-wallet as an intervention variable. Through regression tests, the research findings show that Perceived Usefulness and Perceived Ease of Use have a positive and significant effect on Intention to Use DANA, and Intention to Use DANA has a significant effect on Decision to Use DANA. Intention to Use E-wallet mediates the relationship between Perceived Usefulness and Perceived Ease of Use with Decision to Use DANA. This result is obtained by looking at the results of statistical processing, where the p-value <0.005. These findings imply that the perceived usefulness and ease of use of the DANA application has the potential to encourage user interest in using e-wallets, which in turn can influence users' decisions to adopt DANA as an electronic payment method.

This study was conducted to address contamination risks in Refill Drinking Water Depot (DAMIU) operations when sanitation, equipment maintenance, and quality control are not consistently implemented. This emphasizes the critical importance of systematic risk monitoring and review processes to ensure water quality and safety. The research aims to analyze the implementation of risk monitoring procedures, identify operational risks at each processing stage, and evaluate the effectiveness of risk mitigation strategies at Depot Isi Ulang Air Galon Barokah. A descriptive qualitative approach was employed, utilizing data collection techniques including in-depth interviews, direct field observations, and documentation analysis. The findings reveal that monitoring and risk review processes are conducted routinely, encompassing raw water quality checks, equipment sanitation protocols, and customer gallon container inspections. No significant risks were identified during the study period, as potential hazards such as microbiological contamination and equipment malfunction were effectively prevented through timely filter replacement and daily equipment cleaning procedures. Overall, the depot's risk management system operates effectively and consistently maintains water quality standards, ensuring product safety for consumers.

Industrialization is an economic development process characterized by the growth of the industrial sector as a key driver of economic progress in Indonesia. Beyond its role in creating added value and enhancing national competitiveness, industrialization is closely interconnected with other strategic sectors, including agriculture, services, infrastructure, and employment. Strong linkages between industry and these sectors are essential to ensure that economic growth is inclusive and sustainable. This study aims to analyze industrialization strategies that integrate and strengthen relationships with other sectors in the development process. The research employs a literature review method using a qualitative descriptive approach. The data are derived from secondary sources, including scientific journals, research articles, policy reports, and official publications relevant to industrial and sectoral development. The findings indicate that industrialization strategies aligned with agriculture can increase productivity and value-added processing, while linkages with the service sector and infrastructure development can improve efficiency, distribution networks, and market access. Furthermore, integrated industrialization contributes significantly to employment creation and regional development, reducing economic disparities between areas. Therefore, the formulation of an industrialization strategy that is well-coordinated with other sectors is crucial to achieving balanced economic growth, strengthening structural transformation, and supporting sustainable development in Indonesia.

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 investigates the strategic strengthening of the rambak cracker industry as an instrument for local economic development in Penanggulan Village, Pegandon District, Kendal Regency. Rural agro-processing enterprises have increasingly been recognized as territorially embedded production units capable of generating value-added outputs and absorbing surplus labor within localized economic systems. Field-based empirical observations reveal that rambak production in the village operates through household-managed processing systems characterized by traditional production techniques, informal managerial practices, and limited digital marketing adoption despite contributing significantly to community income generation. Data obtained from expert respondents were analyzed using the Analytical Hierarchy Process to identify strategic priority determinants influencing industrial competitiveness and sustainability. The results indicate that product innovation and quality improvement constitute the primary strategic priority, followed by digital marketing development and institutional partnership strengthening, while production capacity expansion remains comparatively less influential in enhancing market competitiveness. These findings suggest that adaptive innovation and digitally enabled commercialization pathways function as critical mechanisms for improving value-chain integration and expanding market accessibility among rural food-processing industries. Strengthening innovation ecosystems within the rambak sector may therefore contribute to employment creation, income diversification, and sustainable community-based economic transformation in rural production clusters.

The Information System registration module for the Regional Taxpayer Identification Number (NPWPD) was developed using the Laravel framework and implemented by the Taxpayer Identification Agency (BPPKAD) at Kediri City. The system was designed to digitize the NPWPD registration process, which was previously done manually. This traditional approach often led to long queues, extended processing times, and, at times, errors in data entry. The new system offers several key advantages, including an online registration form that allows taxpayers to upload required documents such as photos of ID cards, business locations, and other necessary paperwork. Data validation is performed by officers to ensure accuracy, and automatic notifications are sent to taxpayers, informing them of the status of their applications. The implementation of this system has had several positive impacts, such as significantly improving the efficiency of administrative processes, reducing the manual workload for officers, and increasing transparency and accountability in public services. Moreover, it has improved customer satisfaction by providing faster, more accurate, and more responsive services. This system supports the creation of a streamlined, user-friendly, and effective method for taxpayers to register for NPWPD online, enhancing the overall quality of public sector service delivery.

Flooding is one of the most recurrent natural disasters in Central Java Province, particularly during the rainy season. Diverse geographical characteristics, high rainfall intensity, and rapid urban development contribute to the region’s high vulnerability to flood hazards. According to the Central Java Statistics Agency, a total of 414 flood events and 407,784 affected victims were recorded between 2019 and 2021. This study aims to develop a web-based Geographic Information System (GIS) capable of mapping the spatial distribution and impact levels of floods across Central Java. The methodology includes collecting flood event data from the Central Java Statistics Agency, processing spatial data such as administrative boundary shapefiles, performing attribute integration between spatial and non-spatial datasets, and creating thematic maps using QGIS. The visualization outputs were exported into an interactive web format using the qgis2web plugin and subsequently integrated into a website developed with HTML, CSS, and JavaScript. The results show that the GIS system successfully visualizes flood-prone areas, identifies regions with high flood intensity, and enables users to explore detailed information through interactive digital maps. Additional website features—such as historical flood data, statistical summaries, and descriptive impact indicators—enhance the system's usefulness for disaster analysis. This study demonstrates the crucial role of GIS in supporting disaster mitigation, spatial planning, and policy evaluation related to flood management. Future research is recommended to incorporate more recent datasets and additional non-spatial variables such as rainfall intensity and floodwater depth to improve the system’s analytical accuracy and comprehensiveness.

This study examines the impact of industrial activities in Bontang City, a major center for natural gas processing and fertilizer production, which potentially increases heavy metal contamination in coastal waters. Specifically, the research focuses on the accumulation of lead (Pb) and copper (Cu) in Anadara granosa, a benthic bivalve selected as a bioindicator due to its capacity to accumulate pollutants. The study aims to analyze Pb and Cu concentrations in Anadara granosa tissues collected from the waters of Selangan Hamlet, Bontang City, as an indicator of local environmental quality. The research was conducted over a six-month period, involving shellfish and water sampling from four observation stations. Heavy metal analysis was performed using Atomic Absorption Spectrophotometry, and the results were evaluated against relevant environmental quality standards, including Government Regulation of the Republic of Indonesia Number 22 of 2021. The findings revealed the presence of Pb and Cu at all sampling stations, with varying concentrations. The highest Pb and Cu levels were recorded at the South Station, while the lowest concentrations were observed at the East and North Stations. Overall, the measured concentrations of both metals significantly exceeded established safety limits, indicating serious risks to aquatic ecosystems and human health. These results confirm Anadara granosa as an effective bioindicator of heavy metal pollution and highlight the urgent need for further studies and mitigation efforts.

Language barriers represent one of the most significant obstacles to educational equity and access worldwide. This study investigates the application of Natural Language Processing (NLP) technologies in multilingual educational contexts to facilitate cross-linguistic learning and improve educational outcomes for linguistically diverse student populations. We implemented and evaluated a comprehensive NLP-powered multilingual learning platform across 47 educational institutions in 12 countries, serving 8,450 students speaking 23 different languages. Our experimental framework integrated machine translation, speech recognition, multilingual content generation, and adaptive language learning algorithms. Results demonstrate that NLP-enhanced multilingual education improved student comprehension by 43.6% (p<0.001), increased participation rates by 67.8%, and reduced achievement gaps between native and non-native speakers by 52.4%. Students using NLP-assisted learning tools achieved test scores averaging 78.3% compared to 54.7% for control groups. However, challenges persist regarding cultural context preservation, idiomatic expression handling, and equitable performance across language families. This research provides evidence that NLP technologies can effectively democratize education across linguistic boundaries while identifying critical areas requiring continued development.

This study examines the implementation of RoRo ship services in Indonesia, highlighting the implementation of RoRo ships and the current national logistics framework potential strategies, particularly in the 3T region. This research employs a qualitative descriptive methodology that includes a literature review. It utilizes secondary data from previous studies, comprising books and original scientific reports published in articles or journals. The findings indicated 1) The use of RoRo ships has gone through progressive developments, like the government's initiative on “Sea Toll” designed to support the logistics sector, particularly in 3T regions. 2) Enhancing the logistics framework through the integration of National Logistic EcosIstem (NLE) alongside the Internet of Things (IoT) to increase logistics efficiency that is rapid, effective, and clear. Nevertheless, these initiatives faces challenges. Consequently. The proposed strategies focus on improving growth center development through logistics integration (shipping, port management, and intermodal) and implementing digitalization in logistics document processing.

This Community Service activity aims to foster a spirit of mutual cooperation and to assist local fishermen engaged in seaweed businesses in the processing and cleaning of their harvests in the Amal Beach Area, East Tarakan District. Seaweed harvested from the sea needs to undergo a thorough cleaning process to maintain its quality, ensure market readiness, and meet industry standards. The activity was carried out by six implementers who collaborated directly with the local fishermen through mutual cooperation, seaweed cleaning, and education on environmentally friendly sea product management. The implementation methods included field observation, guidance, and hands-on practice at the fishermen’s business locations to provide practical understanding. The results indicated improved efficiency in the seaweed processing chain, from cleaning to storage, thereby reducing the risk of damage and quality loss. Additionally, the fishermen’s awareness of the importance of maintaining cleanliness and the balance of coastal ecosystems increased significantly. This activity also strengthened social bonds between students and the fishing community, fostering harmonious communication, and instilling values of togetherness, environmental care, and social responsibility. The positive impact of this program is expected to serve as a sustainable model for sea product management while promoting a collaborative culture between academics and coastal communities.

The incidence of degenerative diseases, which are part of Non-Communicable Diseases (NCDs), remains a major cause of death worldwide and in many regions of Indonesia. Meanwhile, efforts to prevent degenerative diseases affecting the adolescent age group are still rare, such as the establishment of a Youth Posyandu, including in Sungai Duren Village. There is no health post that provides youth care services in Sungai Duren Village, while there are many teenagers living there (students under 21 years old) with various lifestyles that are at risk of degenerative diseases. This study aims to analyze the increase in adolescent knowledge about CERDIK between before and after the establishment of the Youth Posyandu in order to prevent degenerative diseases early. This study is a quasi-experimental study involving 30 adolescents by measuring the dependent variable of knowledge. The research instrument used is a questionnaire. Computerized data processing and analysis were conducted through descriptive statistical analysis and T-test analysis. The results showed a significant difference in respondents' knowledge of CERDIK before and after the establishment of the Youth Posyandu in Sungai Duren Village (p=0.000). This study indicates that structured health education can improve adolescents' understanding of CERDIK and can be used as an effective promotive and preventive strategy.

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