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Cracker drying process is considered inefficient because it still depends on weather factors and manual supervision, which can affect the quality of the production results. To overcome this, a smart shrimpcrackerdryingdevicebasedonan Arduino Uno microcontroller with an automatic closing mechanism and an automatic container has been designed and built. This system is equipped with a rain sensor and an LDR sensor to detect weather conditions around the device, as well as a DHT11 sensor to monitor humidity in the cracker storage container. In addition, an ultrasonic sensor is used to automatically regulate the movement of crackers into the storage container after the drying process is complete. A DC motor controlled by a BTS7960 driver functions to move the cover roof and the cracker transfer mechanism, while a limit switch is used to limit the motor movement.Based on the test results, this device can work automatically Shrimp crackers are one of the typical food products widely produced by the people of Bunga Karang Village, Tanjung Lago District, Banyuasin Regency. The traditional shrimp following changes in environmental conditions, thereby increasing the efficiency of the drying process and maintaining the quality of shrimp crackers produced by the residents of Bunga Karang Village.

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 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.

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.

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.

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.      

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.

Sorghum (Sorghum bicolor L.) is an annual crop that is tolerant to drought and requires relatively low water input, making it suitable for cultivation on suboptimal lands such as peat soils. However, the utilization of peat soils for crop production faces major constraints, including high soil acidity and low availability of essential macronutrients, particularly nitrogen (N), phosphorus (P), and potassium (K). Improving the chemical properties of peat soils can be achieved through the application of dolomite to reduce soil acidity and NPK fertilizer to enhance nutrient availability. This study aimed to evaluate the growth and yield responses of sorghum to different combinations of dolomite and NPK fertilizer rates on peat soil and to determine the most efficient dosage. The experiment was conducted using a factorial randomized block design with two factors, namely dolomite rate and NPK fertilizer rate. Observations were made on growth and yield parameters of sorghum. The results showed that the interaction between dolomite and NPK fertilizer had no significant effect on all observed parameters. Based on efficiency analysis, the application of 1.16 kg dolomite per bed and 200 kg NPK per hectare was identified as the most efficient dosage to support the growth and yield of sorghum on peat soil. These findings indicate that appropriate soil amendment and fertilization management are essential for optimizing sorghum production on peat soils.

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.

This study aims to analyze the goods purchasing accounting system at PT Central Sandang Jayatama. A qualitative approach was employed, with data collected through interviews, observations, and documentation. Data analysis included descriptive analysis of the purchasing accounting system and comparison between the company’s implementation and the elements of a purchasing accounting system, including related functions, procedures, documents, and accounting records. Conclusions and recommendations were drawn based on the analysis. The results indicate that the purchasing accounting system related to auxiliary materials availability supports smooth production processes and provides a significant contribution to management in managing auxiliary materials. However, several elements were not fully aligned with the principles of purchasing accounting systems, as reflected in an unclear organizational structure and overlapping duties between the purchasing and receiving functions. Therefore, it is recommended to strengthen the internal control system in auxiliary material purchasing and redesign the organizational structure to separate purchasing and receiving functions. Improvements in this system are expected to enhance product quality, production efficiency, and company profitability. This study contributes

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

The oil and gas industry plays a crucial role in meeting global energy needs, with crude oil from production wells being the primary product of upstream operations. Prior to further processing, crude oil requires pretreatment at the production site, one of the key stages being phase separation using a flash separator. This study examines the effect of variations in cooling temperature on the performance of liquid phase separation and energy requirements in the flash separation process of light hydrocarbons. The analysis was conducted through process simulation using Aspen HYSYS version 14.2 with the Peng Robinson property package. The feed stream had a mass rate of 10,000 kg per hour, a temperature of 50°F, and atmospheric pressure, with compositions of ethane, propane, isobutane, and normal butane. The process configuration included compression, cooling, and phase separation in a flash separator at a constant pressure of 50 psia. Variations in cooling temperature were applied at 20, 10, and 0°C. The simulation results indicated a thermodynamic critical point at 10°C. At 20°C, no liquid phase was formed, while at 10°C, significant liquid yield was obtained with moderate energy consumption. Lowering the temperature to 0°C dramatically increases liquid recovery, but the cooling energy requirement also increases sharply. Sensitivity analysis confirms a strong inverse relationship between temperature and condensation yield, as well as a surge in energy consumption at low temperatures. The optimal operating condition is set at 10°C, providing a balance between separation efficiency and energy efficiency in accordance with sustainable manufacturing principles.

The primary purpose of this activity is to enhance the capabilities of micro, small, and medium enterprises (UMKM) in managing wet cake production in a more hygienic and efficient way in order to generate products with higher market value. The skill development emphasizes technical improvements in the production process by prioritizing cleanliness, food safety, and consistent quality, as well as increasing efficiency in the utilization of raw materials and labor. This activity was implemented for UMKM operating under Dynala Food in Gentengan Hamlet, Padang Village, Singojuruh District, Banyuwangi. In addition to production aspects, the program also aims to strengthen marketing strategies at both local and digital levels, enabling tofu and wet cake products to be more easily accessed by consumers. The implementation was conducted through door-to-door visits and direct field observations. The program stages included socialization, hands-on training, technology application, mentoring, evaluation, and achievement of program objectives. Focus Group Discussions (FGDs) were conducted to identify challenges, formulate concrete action plans, and support the adoption of technology and digitalization in business development.

The high-value horticulture sector has gained increasing attention in modern agricultural development, particularly in the cultivation of premium melon through greenhouse and fertigation systems. The Satria Tani Hanggawana Cooperative has initiated premium melon farming to enhance members’ income; however, investment decisions in high-value commodities require a comprehensive financial feasibility assessment to ensure business sustainability. This study aims to analyze the financial feasibility of premium melon farming by examining production costs, revenue, income, and financial efficiency indicators. Using a descriptive method with qualitative and quantitative approaches, the research was conducted through direct observation and interviews in two active greenhouses. The results show that the total production cost for one planting season reached Rp20,413,750, dominated by variable costs, reflecting the intensive input requirement to maintain product quality. The total revenue of Rp33,950,000 generated a net income of Rp13,536,250, indicating that the enterprise is financially profitable. The R/C Ratio of 1.67 confirms that the business operates efficiently, while the B/C Ratio of 0.67 indicates that net benefits remain below total costs due to reduced production caused by pest disturbances. The break-even analysis further shows that actual production far exceeded the minimum threshold required to avoid losses. Overall, the findings demonstrate that premium melon farming is financially viable, yet improvements in cost management, production monitoring, and greenhouse operational efficiency are essential to enhance profitability and long-term sustainability for the cooperative.

The rolling process is a material forming method that is greatly influenced by the roll rotation speed parameter. Inappropriate rotation variations can cause various defects such as waves, tears, thickness irregularities, and ovalization. This review aims to compare the effect of variations in the roll machine rotation speed on the quality of rolling results on various types of materials, including Al6061-O aluminum plate, rubber sheets, and hollow and pipe-shaped materials. The method used is a literature study by collecting and comparing data on roll speed, defects that appear, deformation results, and process efficiency from several relevant journals. The results of the analysis show that high rotation speeds increase productivity but also increase the risk of defects because the material does not have time to adapt to deformation. Conversely, low rotation speeds produce more stable shapes and minimize defects, but are less efficient for mass production. Each material has a different optimal rotation range, including 23–36 rpm for Al6061-O plate, 45–72 rpm for rubber sheet formation, 24.4 rpm for hollow and pipe rolling, and around 21 rpm for corrugated plate. Overall, this study confirms that rotational speed control is a critical factor in achieving a balance between deformation quality and machining efficiency.

Blasting and crushing are essential stages in the limestone mining process; however, both stages may contribute to material volume loss due to technical factors and geological conditions. This study aims to analyze the blasted volume, crushed volume, and the amount of volume loss occurring throughout these processes. The research utilizes primary data including blasting geometry, blasting patterns, crushing production, and secondary data such as regional geology and equipment specifications. Based on 15 blasting activities conducted from October to December 2024, the total blasted volume reached 71,691 tons with an average powder factor of 0.23 kg/m³. Meanwhile, the total volume produced from secondary crushing was 71,575 tons. The comparison indicates volume loss influenced by suboptimal fragmentation, rock characteristics, work efficiency of the crushing unit, and operational constraints in the field. The results of this study are expected to serve as a reference for optimizing blasting design and crushing operations to minimize volume loss and improve overall mining productivity.

Global economic developments show the acceleration of the adoption of circular business models, where resource efficiency and waste reduction are the main focuses to answer sustainability demands and SDGs targets. Although this trend is strengthening globally, MSMEs, especially those based on family businesses, are still lagging behind in their implementation, even though digitalization is increasingly opening up opportunities for operational optimization and innovation. The urgency of this research arises from the need to understand how the digital mindset and transformation of family business management can accelerate the readiness of MSMEs in implementing circular business practices. The novelty of this research lies in the integration of the three strategic concepts into a single analytical framework through the Systematic Literature Review (SLR), a perspective that has rarely been combined in previous studies. With the SLR method based on the PRISMA protocol, this study shows that MSMEs already have a strong foundation in the digital mindset, including technology readiness, innovation orientation, and adaptation of digital business models. Family business values have also been proven to support business continuity through knowledge transfer and long-term investment. However, the implementation of circular business practices is still weak, especially in resource cycle management, sustainable product design, and environmental impact measurement. The implication is that the synergy of digital mindset and family business governance can strengthen the transition of MSMEs to a circular economy that directly supports the achievement of the SDGs, especially in the areas of sustainable consumption-production and industrial innovation.

Food-based micro, small, and medium enterprises (MSMEs) play a crucial role in supporting household economic growth, including in Sialang Village, Bangkinang Subdistrict. One of the local businesses, “Keripik Cabe dan Mie Kuning Bu Inun,” produces cassava chips using traditional frying methods, which result in high oil absorption, inconsistent quality, short shelf life, and low production efficiency. Limited knowledge of food processing technology, sanitation, and food safety standards further hinders business development. This community service program aims to address these challenges through the application of low-oil frying technology, training on Good Manufacturing Practices (GMP), production assistance, and periodic evaluation. The implementation method includes field observation, technical training, technology demonstration, production mentoring, and monitoring. The results show significant improvements in product quality, indicated by lower oil content, crispier texture, brighter color, and longer shelf life. Efficiency in oil usage and process stability also increased. Moreover, the knowledge and skills of MSME actors in sanitation, food safety, and production management improved considerably. Overall, the program successfully enhanced the competitiveness of the MSME, strengthened production sustainability, and provided positive economic impacts. This initiative can serve as a replicable model for other food MSMEs facing similar challenges.

This study aims to analyze and compare rice inventory valuation using the FIFO, FEFO, and Average methods in determining the cost of goods sold (COGS) at UD. Rahayu Indah. This study uses a quantitative descriptive approach with a perpetual inventory recording system. Data were collected through observation, interviews, and documentation from January to December 2024. The results show that each method produces different COGS values: the FIFO method produces the lowest COGS, followed by FEFO, while the Average method produces the highest COGS. This difference is influenced by cost allocation based on the order of goods in and out and price fluctuations during the production period. These findings indicate that the FIFO method is the most effective method to be applied at UD. Rahayu Indah because it reflects the logical physical flow of goods, supports cost efficiency, and increases the company's gross profit. In addition, this method is also in line with the company's operational characteristics, which have stable purchasing patterns and are in accordance with the principles of PSAK No. 14 on inventory. The results of this study are expected to assist UD. Rahayu Indah's management in determining an inventory valuation strategy that is efficient, accurate, and supports business sustainability.

PT Mandiri Banana Indonesia still requires further development and arrangement of its layout and facilities to enhance the efficient and optimal use of land. This study aims to determine whether the existing layout at PT Mandiri Banana Indonesia is effective in supporting operational flow and the uninterrupted progression of the production process. The research adopts a quantitative descriptive approach, which is used to measure the correlation between activities through the Activity Relationship Chart (ARC) and Total Closeness Rating (TCR) methods. These methods enable the analysis to be presented in a measurable and objective manner as a basis for improving the facility layout. This study proposes a new layout design for the main production area and supporting facilities. The proposed layout is based on the results of ARC and TCR analyses, which indicate several critical spatial interrelationships among the facilities. Based on the findings and discussion regarding the application of the Activity Relationship Chart (ARC) and Total Closeness Rating (TCR) in designing the facility layout at PT Mandiri Banana Indonesia, it may be inferred that layout improvement exerts a significant impact on enhancing workflow efficiency and the overall work environment. The proposed alternative layout is expected to achieve a safer, more hygienic, and more productive spatial arrangement by establishing a clear separation between the production zone, administrative zone, and supporting areas.