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This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.

Asset management and equipment maintenance are crucial aspects in supporting smooth business operations, particularly in the copper crafts industry. Merapi Karya Cipta, located in Dusun III, Tumang, Cepogo District, Boyolali Regency, owns various production equipment assets that play a direct role in the work process, yet their management remains rudimentary and unplanned. This community service activity aims to increase copper craftsmen's understanding and awareness of the importance of asset management and equipment maintenance to improve operational efficiency. Qualitative methods were used through observation, interviews, and direct outreach to the craftsmen. The results of the activity demonstrated increased knowledge and a shift in participants' perspectives on business management, particularly regarding equipment maintenance and division of labor. The craftsmen began to understand that routine maintenance can be performed simply and does not always require significant costs. Therefore, this activity is expected to help maintain the continuity of the production process, reduce the risk of equipment damage, and support increased productivity and the well-being of the local community.

The analysis methods used in this study include a case study on the use of closed-loop recycling and an evaluation of biopolymer performance across various industries, both of which are important components in the transformation of the manufacturing industry toward a circular economy. The research findings indicate that recycled materials can reduce carbon emissions by thirty to fifty percent and save production costs by fifteen to twenty-five percent. Artificial intelligence-based sorting technology improves sorting efficiency to 95 percent, and closed-loop recycling maintains the mechanical properties of materials up to 90 percent after four cycles. The degradation rate of biopolymers like PLA and PHA reaches 60-80% within six months, although production costs are still 2-3 times higher. The integrated approach increases resource efficiency by 45% and reduces waste by 60%. To achieve successful implementation, Extended Producer Responsibility (EPR) policies, strategic infrastructure investments, and collaboration from various parties thru the triple helix model must work together.

Production planning and inventory control are critical aspects of operations management, as they directly influence cost efficiency, resource utilization, and the continuity of the production process. Ineffective planning and inventory decisions may lead to excessive costs, production delays, or imbalances between supply and demand. The complexity of these problems, which often involve multi-period horizons and multi-stage decision-making processes, has encouraged the application of quantitative optimization methods, one of which is dynamic programming. This study aims to analyze and synthesize the application of dynamic programming in production planning and inventory control through a Systematic Literature Review (SLR) approach. The SLR process was conducted by systematically identifying, selecting, and analyzing 15 relevant national journal articles published between 2015 and 2024 and obtained from various recognized scientific databases. The reviewed literature indicates that dynamic programming is effective in supporting optimal decision-making by determining appropriate production quantities and inventory levels, minimizing total production and holding costs, and managing fluctuating demand conditions. In addition, this method helps reduce the risks associated with overstock and stockouts by considering sequential decision structures. However, the findings also reveal several limitations of dynamic programming, including high computational complexity, strong dependence on deterministic data assumptions, and limited flexibility in handling high levels of uncertainty. These constraints suggest the need for further methodological development or integration with other approaches to enhance practical applicability.

The selection of this research topic is based on the important role of packing machines in the noodle production process. As consumer demand continues to increase and industrial competition becomes more intense, optimizing production efficiency is a critical requirement for manufacturing companies. This study focuses on the Tokiwa W500 Packing Machine used at PT. Indofood CBP Sukses Makmur, Noodle Division, Semarang. The research method involves a comprehensive review of the machine control system to evaluate its operational performance. Data collection was conducted through direct observation, structured interviews with machine operators, and relevant literature review. The review emphasizes system performance, operational efficiency, and the level of automation, while identifying potential areas for improvement. The results indicate that the Tokiwa W500 Packing Machine operates in a stable and consistent manner during the noodle packaging process. However, opportunities were identified to enhance the automation system in order to improve production efficiency and reduce the risk of human error. This study is expected to contribute to the development of more effective and optimized control systems for industrial packing machines.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

This study examines the price elasticity of fresh cow’s milk on the demand for raw materials at Anisa Yoghurt Company using a demand theory approach. Milk is the main input in yoghurt production, making price fluctuations potentially influential on production decisions. The objective of this research is to analyze the responsiveness of raw milk demand to price changes and to identify the elasticity characteristics of milk demand in a small-scale food processing business. This study employs a quantitative case study method using primary data collected through weekly observations of milk prices and quantities purchased over a three-month period, from September to November. The analysis applies the midpoint elasticity formula to measure price elasticity of demand. The findings indicate that the demand for raw milk is consistently inelastic across the observed periods, as shown by elasticity values with absolute magnitudes less than one. Both price decreases and increases result in relatively small changes in the quantity demanded, indicating low sensitivity of demand to price fluctuations. These results suggest that raw milk is an essential production input, and purchasing decisions are driven more by production needs than by short-term price changes. The study implies that yoghurt producers should prioritize supply stability and production efficiency rather than adjusting purchase volumes in response to minor price changes. This research contributes empirical evidence on demand elasticity in dairy-based agro-industries, particularly at the small enterprise level.

Traditional snack micro, small, and medium enterprises (MSMEs) in Indonesia face increasing market competition and rapidly changing consumer preferences, particularly among younger consumers seeking innovative and symbolic food experiences. This community engagement study aims to strengthen the profitability and sustainability of traditional snack MSMEs by integrating local flavor innovation with simple business governance practices. Using a participatory action research approach under the Merdeka Belajar Kampus Merdeka (MBKM) program in Palangka Raya, this study involved co-creation between students and local entrepreneurs in product development, production standardization, and basic financial management. Qualitative data were collected through participatory observation and stakeholder discussions, while quantitative data were obtained from sales records and simple financial reports. The results demonstrate that local flavor-based innovation, combined with standardized operating procedures and cost control mechanisms, improved product differentiation, operational efficiency, and financial performance. The intervention generated a positive net profit and strengthened the partner’s capacity for independent business management. This study contributes to the literature by positioning traditional food MSMEs as sites of cultural innovation and micro-governance, while supporting Sustainable Development Goals related to inclusive economic growth, cultural preservation, and responsible production.

The shift in economic structure from a reliance on traditional agriculture to modern industrialization has yielded various competitive advantages for a region. The presence of leading sectors at the local level is a strong indicator of increased added value and high productivity, fueled by a healthy competitive climate among business actors. This phenomenon is closely related to the characteristics of the industrial sector, which possesses very strong forward and backward linkages, making it a primary driver of national economic growth. Progress in the manufacturing sector is not merely a measure of economic success but also serves as a driver of fiscal stability through massive employment absorption and increased foreign exchange earnings. Industrial growth is considered highly strategic because it holds significant potential for future expansion through technological innovation and production efficiency. This condition makes industrial cluster development an instrument for driving sustainable economic growth in Indonesia, while simultaneously strengthening the competitiveness of local products in the global market through the integration of a more organized production system.

Kopirates Maumere is a local micro, small, and medium enterprise (MSME) engaged in the processing and marketing of specialty coffee products from Sikka Regency. This study aims to analyze the management strategies implemented by the MSME based on interviews with the business owner. The research focuses on product strategy, marketing, operations, business development, and the challenges faced in running the MSME amid increasingly competitive market conditions. The research method employs a descriptive qualitative approach through in-depth interviews and direct observation. The results indicate that Kopirates Maumere applies a product differentiation strategy by highlighting the quality of local coffee, supported by increasingly developed digital marketing. The MSME also strives for operational efficiency despite facing challenges related to limited capital and production equipment. This study concludes that adaptive management strategies based on local potential are crucial for maintaining MSME sustainability. Furthermore, improved managerial capacity along with support from the government and local communities is needed to strengthen the MSME’s position in a broader market.

Business ventures are activities carried out by individuals or organizations involving the production, sale, purchase, or exchange of goods and services, with the aim of generating profit. A basic necessities store (commonly known as a “sembako” store in Indonesia) sells daily staple needs, especially the nine essential commodities (sembako), which include items such as rice, sugar, cooking oil, eggs, salt, and other key food ingredients. UD. Putri 2, located in Dusun 1A, Sumber Harapan Village (21261), Tinggi Raja Subdistrict, Asahan Regency, was established in 2018 and has since become an essential part of the local community. This has required UD. Putri 2 to constantly monitor their stock inventory. However, the company still faces inefficiencies in managing sales data processing, which often leads to inventory shortages. When the supply of goods is insufficient to meet customer demand, customers may turn to other stores. If this occurs repeatedly, the store risks losing profit due to the unavailability of goods. Supply Chain Management (SCM) refers to the integrated processes and production activities starting from the acquisition of raw materials from suppliers, the value-adding processes that turn raw materials into finished products, the inventory storage process, and the distribution of finished goods to retailers and consumers. The implementation of SCM can optimize inventory management of staple goods, minimize inventory costs, and improve supply chain efficiency at UD. Putri 2.

Post-harvest handling of chili in Indonesia is still largely done manually, requiring a relatively long processing time and producing products with a non-uniform level of fineness. This condition impacts low production efficiency and the quality of processed chili. Therefore, the application of appropriate technology in the form of a chili flour machine is needed to increase production capacity and consistency. This study aims to analyze the effect of the number of blades and mesh size on the production capacity of a disk mill type FFC 15 chili flour machine. The research method used was an experiment with a factorial design, involving two variations in the number of blades, namely three and four blades, and three variations in mesh size, namely 0.5 mm, 1 mm, and 3 mm. Each treatment combination was carried out three times. The results showed that the number of blades and mesh size had a significant effect on the machine's production capacity. The best treatment combination was obtained by using four blades with a mesh size of 3 mm, which resulted in a production capacity of 6.84 kg/hour and a Composite Desirability value of 0.983. These results indicate that proper arrangement of machine components can increase the efficiency of post-harvest chili processing.  

Mining sequence design is one of the important stages in open pit mining activities that aims to organize the excavation stages so that production activities run effectively, efficiently, and in accordance with the established targets. This research was conducted at PT Putra Perkasa Abadi Site SKS with the aim of designing a mining sequence in January and February 2025. The methods used include analysis of topographic data, geology, pit design, and monthly production targets. The data was processed using mining software to determine the excavation sequence based on elevation, overburden volume, and coal reserves. The design results show that the total planned overburden and coal volume is able to meet the company's production targets by considering slope stability and the efficiency of digging and loading equipment. In addition, the resulting sequence design also takes into account aspects of work safety and field operational conditions, such as mine road access and drainage systems. With a structured design, it is expected that mining activities during this period can run according to schedule, minimize operational obstacles, and support the achievement of production targets and cost efficiency at PT Putra Perkasa Abadi Site SKS.

The purpose of this research is to formulate a model for the Strategy to Enhance Competitiveness and Sustainability of SMEs in Supporting Independence in East Java. The research method is descriptive qualitative. Data collection techniques include observation, interviews, and documentation. Data analysis techniques use 1) Data reduction, 2) Data display, 3) Data triangulation, 4) Data verification. The informant determination technique uses indepth interviews with key informants such as SME actors, relevant agency officials, representatives from East Java's SME Banks, and academics. The results of data analysis and literature review indicate that the Strategy to Enhance Competitiveness of SMEs in East Java focuses on the adoption of digital marketing (utilization of social media, marketplaces, and e-commerce), product and process innovation (development of unique and quality products, production efficiency), as well as strengthening partnerships and collaborations with various parties (government, financial institutions, large companies) to expand market access and resources. The strategy for enhancing the sustainability of MSMEs in East Java heavily relies on prudent financial management practices (cash flow management, access to affordable financing such as Prokesra and Dagulir), compliance with legal and certification requirements (PIRT, halal, BPOM), as well as the potential implementation of circular economy concepts for resource efficiency and waste reduction. The strategy supporting the independence of MSMEs in East Java focuses on a comprehensive empowerment program, including entrepreneurship training and intensive mentoring for capacity building, ease of access to capital, and facilitation of business matching and exhibitions/bazaars for network and market expansion. The active role of the government and supporting institutions is crucial in creating a conducive ecosystem. The relationship between competitiveness, sustainability, and independence of SMEs is synergistic and mutually reinforcing. Increasing competitiveness directly contributes to economic sustainability. Sustainability creates stability that allows SMEs to continue innovating and enhancing their competitiveness. Meanwhile, independence empowers SMEs to be proactive in achieving competitiveness and sustainability, with the ability to make strategic decisions and adapt to changes. Overall, SMEs in East Java have great potential to continue growing and contributing to the economy. However, sustained support through appropriate policies, relevant empowerment programs, and facilitation of access to essential resources will be key to realizing SMEs that are not only competitive and sustainable but also fully independent.

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.

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.

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 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.

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.

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.