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PT. Tirta Fresindo Jaya, specifically its Pasuruan plant as the producer of Pucuk Harum Tea beverage, requires a large supply of steam to support various production processes. This steam is used in the process of heating tea leaves, heating water through a heat exchanger, and heating chemicals and water in the cleaning in place (CIP) process. Steam pressure of 5 bar that is flowed to the process equipment will undergo condensation to produce condensate. To separate steam and condensate, steam traps are used, consisting of several types, namely mechanical, thermostatic, and thermodynamic. However, in operational practice, steam trap malfunctions often occur, either in the form of steam leaks that are wasted with condensate or failure to drain condensate from the system. This condition causes a decrease in the efficiency of the steam piping system and increases the workload of the boiler. As a result, fuel consumption and boiler feed water requirements become greater than ideal conditions. Therefore, this study aims to analyze the energy and operational losses caused by steam trap malfunctions, as well as evaluate their impact on boiler system performance and steam utilization efficiency at PT. Tirta Fresindo Jaya Pasuruan plant.      

A community service program (PKM) entitled "Skincare Product Dispute Resolution" was successfully implemented in Ngadi Village, Mojo District, Kediri Regency, involving village cadres, youth organizations, and community members, particularly housewives as skincare consumers. This PKM aims to provide solutions to skincare product disputes through five stages: outreach, training, technology implementation, mentoring, and sustainability evaluation. The outreach program began with a presentation on halal skincare products and consumer protection against misleading promotional practices, delivered by speakers from the Sharia Research Group (SReG) and the Non-Governmental Consumer Protection Organization. This program also includes in-kind and in-cash contributions, including training to improve customer service quality, ensure raw material quality standards and production processes comply with international standards, and ensure product information transparency. In addition, a loyalty program and transportation cost incentives were provided to affected consumers. PKM partners played an active role in providing information and inviting the community to participate in this activity. The implementation of this activity is expected to strengthen consumer trust and ensure protection of consumer rights related to skincare products.

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.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

The development of digital technology has brought significant changes to the delivery of da'wah messages to young people. Audiovisual media has become an effective means of attracting interest, enhancing understanding, and increasing the participation of mosque youth in development activities. This service aims to analyze the use of audiovisual media as a means of developing mosque youth at Barisan Mujahid Matita (BMM), Medan. The method used is a qualitative approach through observation and audiovisual production training, which can improve the skills of young people in creating da'wah content, strengthen understanding of development material, and boost enthusiasm in participating in mosque activities. In addition, audiovisual media has proven to help BMM enhance the quality of documentation and publication of activities. In conclusion, audiovisual media is an effective instrument in the development of young people, especially in the context of creative digital da'wah. Digital Mosque Youth

In the era of globalization and digital transformation, organizations strive to maintain competitiveness through optimizing internal factors such as work culture, business process engineering (BPR), and decision support systems (DSS). This qualitative research based on a Systematic Literature Review (SLR) analyzes the relationship between these three using the Denison Organizational Culture Model, Hammer & Champy BPR, and the Resource-Based View (RBV). It finds that work culture mediates the implementation of BPR and DSS to increase efficiency by 30-50%, employee productivity, and sustainable competitive advantage. Case studies such as the Toyota Production System (TPS) confirm this synergy, resulting in reduced costs, cycle times, and improved service quality. This research also emphasizes the importance of a strong work culture in supporting the implementation of new technologies and methodologies, which in turn strengthens the company's market position. Thus, organizations that are able to integrate these three elements will be better prepared to face the challenges of globalization and dynamic changes in their industry, creating a sustainable advantage and being able to survive in a highly competitive market.

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

This study aims to analyze the role of innovation in enhancing the competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in Medan City. The research employs a qualitative approach through direct interviews with several MSME owners from various business sectors. The results reveal that innovation plays a significant role in strengthening business competitiveness through product development, digital marketing strategies, and improvements in production processes. Product innovation among MSMEs includes changes in design, quality enhancement, and product diversification based on market demand. Meanwhile, marketing innovation is achieved through the utilization of social media and digital platforms to expand market reach and improve customer engagement. However, MSME actors still face challenges such as limited capital, low digital literacy, and lack of access to training and mentorship. Therefore, collaboration between business actors, government, and supporting institutions is essential to build a sustainable innovation ecosystem that strengthens the competitiveness of MSMEs in the digital era.

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.

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

The problem lies in the inconsistent quality of M10 hexagonal head bolts with a spacing of 1.5 mm, a bolt length of 100 mm and slow production speed for manual production. The purpose of the analysis is to obtain consistent, standard, and productive quality of M10 hexagonal head bolts with a spacing of 1.5 mm, a bolt length of 100 mm. The analysis method includes the selection of AISI 1040 raw materials with a diameter of 10 mm in the form of rolls, the determination of the production process through raw material inspection, diameter reduction from 10 mm to 9.8 mm, the formation of hexagonal heads with a machine, cutting the length of the bolts and the bolt end chamfer, making M10 threads with a range of 1.5 mm with a machine, hardening, 10 m thick Zinc coating, thread profile inspection, sample hardness test, and sample tensile test. The results of mass production with the machine obtained a hexagonal head bolt with a thread size of M10x1.5 mm, a bolt length of 100 mm, a capacity of 500 units/hour in accordance with the ISO 9001:2015 standard with a hardness of 30 HRC and a tensile strength of 830 MPa at a cost of Rp. 1133/bolt and a process duration of 8.3 minutes/bolt which implies that product quality can be more guaranteed to be consistent and uniform.

Sanitation is an important aspect in the fish feed industry to ensure product quality, product safety, and employee health. PT. CentralPertiwi Bahari is a fish feed industry that has implemented sanitation as part of its product quality assurance system. This study aims to examine the implementation of sanitation at PT. CentralPertiwi Bahari to obtain an overview of the implementation of sanitation based on Permenkes No. 70 of 2016. This study uses a qualitative descriptive method carried out in January-February 2025. Data collection was carried out through observation, interviews, document reviews and scientific literature. Aspects studied include external environmental sanitation, factory environmental sanitation, building sanitation, production process sanitation, raw material sanitation, and employee sanitation. The results of the study indicate that PT. CentralPertiwi Bahari has implemented sanitation in accordance with the provisions of Permenkes No. 70 of 2016 as indicated by clean environmental and building conditions, the availability of good sanitation facilities, proper management of raw materials, and employee sanitation that is consistently implemented. The implementation of sanitation at PT. CentralPertiwi Bahari has supported efforts to maintain quality and occupational health. However, conditions requiring attention, such as water pooling outside the factory during rainy seasons, require remedial measures to optimize sanitation implementation. The results of this study are expected to serve as a basis for company evaluation and a reference for similar industries in improving sustainable sanitation practices.

Overburden removal at Pit B did not meet the production targets for June and July 2025, which were set at 570,698.8 BCM and 400,986.56 BCM, respectively, while the actual production achieved was 507,071.66 BCM and 312,128.23 BCM. This study aims to determine the theoretical productivity of loading and hauling equipment and to identify the factors that hinder the achievement of production targets. The primary data used in this study include the cycle time of loading equipment, the cycle time of hauling equipment, bucket fill factor, and delay time. The secondary data required consist of swell factor, number of passes, bucket capacity, and available working time. The productivity of the loading equipment was 332.58 BCM/hour, while the hauling equipment achieved a productivity of 53.16 BCM/hour, with a match factor of 0.74. This indicates that the loading equipment experienced waiting time during loading operations due to the hauling equipment not operating optimally or at full capacity. The factors inhibiting the achievement of production targets include a narrow, waterlogged, and uneven working face; hard overburden material; a road grade of 16%, which exceeds the standard maximum of 12%; a curve road width of 15 m, which is less than the ideal minimum width of 19.95 m; undulating haul roads; narrow haul road width; and dusty road conditions.

The instability of the climate is becoming increasingly prominent across Southeast Asia, creating uncertainty in agricultural systems that are highly dependent on seasonal weather patterns. Indonesia, where rice remains the primary staple food, is particularly vulnerable to the effects of rising temperatures and rainfall deficits. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict rice production while incorporating indicators of extreme climate anomalies. Using publicly available datasets, including FAOSTAT production statistics, NOAA rainfall and temperature anomalies, and climate indices from the World Bank, this model was developed following the Box-Jenkins procedure. Among the configurations tested, the SARIMA model (1,1,1)(0,1,1)₁₂ showed the strongest performance, reflected in a MAPE of 4.62% and low RMSE values. The model indicates that significant El Niño events can reduce annual rice production by 3–7%, while wetter La Niña conditions may support production recovery. These findings highlight the importance of integrating climate-sensitive data into agricultural forecasting. The model presented here could support early warning systems, adaptive farming strategies, and long-term food security planning in Indonesia.

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 normal flora of chickens refers to the microbial communities that naturally inhabit the gastrointestinal tract and body surfaces of chickens, playing a crucial role in health, digestion, and immunity. This community consists of beneficial bacteria such as Lactobacillus, Bifidobacterium, and Bacillus, which assist in feed fermentation, vitamin synthesis, and inhibition of pathogenic growth. Additionally, the normal flora may include potential pathogenic bacteria such as Escherichia coli, Salmonella spp., and Campylobacter, which can cause diseases if microbial balance is disrupted. The diversity and balance of the microbiota are influenced by various abiotic factors, including feed quality, access to clean water, ambient temperature, humidity, and environmental hygiene. Biotic factors, such as microbial interactions, rearing systems, and contact with other animals, also play a significant role in determining microbial composition. Several studies have shown that the use of feed additives, such as probiotics and phytogenics, can enhance populations of beneficial bacteria while suppressing pathogenic bacteria. Extensive rearing systems, which provide chickens with more space and exposure to natural environments, tend to increase microbiota diversity compared to semi-intensive systems with more restricted conditions. Understanding these factors is essential for developing effective health management strategies and optimizing safe and sustainable poultry production.

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.

Heritage language loss among immigrant and diaspora communities represents a critical challenge to cultural identity, intergenerational communication, and cognitive diversity. Traditional heritage language maintenance approaches face significant barriers including limited resources, lack of qualified instructors, geographic dispersion, and competing demands of dominant language acquisition. This study investigates the effectiveness of bilingual digital story applications as innovative tools for heritage language maintenance among children aged 4-12 years. Through a 24-month longitudinal mixed-methods study involving 1,843 families across eight language communities (Spanish, Mandarin, Arabic, Korean, Tagalog, Vietnamese, Hindi, and Polish), we examined language proficiency development, cultural identity formation, family engagement patterns, and app usage behaviors. Quantitative analysis of pre- and post-intervention language assessments revealed statistically significant improvements in heritage language vocabulary (effect size d=0.68), listening comprehension (d=0.54), and oral production (d=0.47) among children using bilingual story apps for at least 20 minutes daily. Qualitative findings from parent interviews and child focus groups highlighted the apps' role in making heritage language learning enjoyable, facilitating parent-child interaction, connecting children to cultural narratives, and normalizing bilingualism. However, effectiveness varied substantially based on app design features, with interactive elements, culturally authentic content, parent involvement scaffolds, and adaptive difficulty showing strongest associations with outcomes. This research demonstrates that thoughtfully designed bilingual story apps can serve as accessible, scalable tools for heritage language maintenance, though they function most effectively as complements to rather than substitutes for rich home language environments and community connections. The study contributes empirical evidence to inform app development, family language planning, and policies supporting linguistic diversity in increasingly globalized societies.