Publication Search

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.

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.

In the context of Indonesia's post-pandemic financial market dynamics, investment and financing decisions often face challenges of cash flow uncertainty and capital cost volatility, requiring a Profitability Index (PI) and Weighted Average Cost of Capital (WACC) perspective to ensure optimal resource allocation to maximize company value. This study aims to analyze the effectiveness of investment and financing decisions through the integration of PI and WACC based on a synthesis of the latest literature. A descriptive qualitative approach was used through a literature study with secondary data from financial journals and textbooks from 2021-2025, collected from Google Scholar and university repositories, then analyzed thematically with data reduction, presentation, and literature triangulation to interpret the PI, IRR, and WACC indicators. The results show that PI is consistently >1 (ratio of 1.15-1.45) and IRR > WACC (average of 10-12%), confirming the feasibility of 70% of manufacturing projects, while WACC of 9.8% from the optimal capital structure (debt ratio of 40-50%) supports an effective tax shield, despite being constrained by multiple IRRs, conflicting metric rankings, and BI interest rate fluctuations that increase implicit costs by up to 15%. It can be concluded that PI-WACC integration increases theoretical profitability by 12% through precise allocation, but is limited by the generalization of secondary data; a hybrid model with mixed-method validation is recommended for the non-manufacturing sector in emerging markets.

Regional development faces dynamic challenges amid rapid economic growth driven by natural resource extraction. This study aims to identify leading economic sectors, analyze structural economic transformation, and evaluate the role of these sectors in regional development. The research employs a quantitative method with a descriptive approach. Secondary data consist of Gross Regional Domestic Product (GRDP) at constant prices over the past five years. The analytical techniques applied include Location Quotient analysis to identify base sectors, Shift-Share analysis to assess structural changes as well as comparative and competitive advantages, and Klassen Typology to classify sectoral growth patterns. The results reveal a structural shift from primary sectors, such as agriculture and fisheries, toward secondary sectors, including mining and manufacturing. Despite challenges related to development equity, these leading sectors serve as key drivers of regional economic growth. To maximize the contribution of leading sectors to broader regional development, this study recommends that government policies prioritize the strengthening of intersectoral linkages.

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

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

This study aims to examine and analyze the effects of product quality and distribution channel on repurchase intention, with customer satisfaction and customer trust serving as mediating variables. The research employed a quantitative method with a causal-explanatory approach. The study population consisted of customers at the Weber building materials manufacturing company in East Java, with a sample of 275 respondents selected using purposive sampling. Data were collected via questionnaires and assessed using a Likert scale. The data analysis was conducted using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS) software. The findings of the study reveal the following: 1) Product quality has a significant positive effect on customer satisfaction; 2) Product quality has a significant positive effect on customer trust; 3) Product quality has a significant positive effect on repurchase intention; 4) Distribution channel has a significant positive effect on customer satisfaction; 5) Distribution channel has a significant positive effect on customer trust; 6) Distribution channel has a significant positive effect on repurchase intention; 7) Customer satisfaction has a significant positive effect on customer trust; 8) Customer satisfaction has a significant positive effect on repurchase intention; 9) Customer trust has a significant positive effect on repurchase intention.

This study aims to examine and analyze the effects of product quality and distribution channel on repurchase intention, with customer satisfaction and customer trust serving as mediating variables. The research employed a quantitative method with a causal-explanatory approach. The study population consisted of customers at the Weber building materials manufacturing company in East Java, with a sample of 275 respondents selected using purposive sampling. Data were collected via questionnaires and assessed using a Likert scale. The data analysis was conducted using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS) software. The findings of the study reveal the following: 1) Product quality has a significant positive effect on customer satisfaction; 2) Product quality has a significant positive effect on customer trust; 3) Product quality has a significant positive effect on repurchase intention; 4) Distribution channel has a significant positive effect on customer satisfaction; 5) Distribution channel has a significant positive effect on customer trust; 6) Distribution channel has a significant positive effect on repurchase intention; 7) Customer satisfaction has a significant positive effect on customer trust; 8) Customer satisfaction has a significant positive effect on repurchase intention; 9) Customer trust has a significant positive effect on repurchase intention.

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

This study investigates the relationship between Environmental, Social, and Governance (ESG) performance and corporate financial distress, with board gender diversity examined as a moderating variable. Using 96 firm-year observations from manufacturing companies listed on the Indonesia Stock Exchange (2022–2024), the analysis employs variance-based Structural Equation Modelling (SEM). The findings reveal that ESG performance does not exert a statistically significant effect on financial distress, and gender diversity does not moderate this relationship. These non-significant results constitute the central empirical contribution of the study, highlighting that ESG engagement and gender diversity have yet to translate into financial resilience in the Indonesian manufacturing context. The study underscores the importance of contextual factors—such as implementation costs, authenticity of ESG disclosures, and limited female representation on boards—in shaping the effectiveness of sustainability practices. The results provide theoretical implications for Stakeholder and Agency Theory and offer practical insights for managers, regulators, and investors in emerging markets.

This study empirically examines the effects of ownership structure, including managerial, institutional, and public ownership, on tax avoidance practices, using profitability as a moderating variable. The population in this study consists of manufacturing companies listed on the Indonesia Stock Exchange (IDX), from which a sample was selected using purposive sampling. A total of 330 observations were collected from 110 manufacturing companies for the period 2022–2024. The variables were tested using multiple linear regression in EViews 12. This study expands on previous research by using profitability as a moderating variable that can influence the relationship between ownership structure and tax avoidance. The results show that institutional ownership has a negative and significant effect on tax avoidance practices. An increase in institutional share ownership can reduce tax avoidance practices. Meanwhile, managerial and public ownership do not affect tax avoidance practices. In the moderation test, profitability strengthened the effect of managerial and institutional ownership on tax avoidance. Still, it did not moderate the impact between public ownership and tax avoidance.

This study investigates the relationship between corporate governance characteristics, financial structure, and Enterprise Risk Management (ERM) disclosure in Indonesian non-financial firms. Focusing on manufacturing companies listed on the Indonesia Stock Exchange in 2023, the analysis examines whether board size, the proportion of independent commissioners, and leverage influence the extent of ERM disclosure. Using a quantitative approach, multiple linear regression is applied to secondary data obtained from firms’ annual reports. The findings indicate that board size and the proportion of independent commissioners do not have a significant effect on ERM disclosure, while leverage exhibits a positive and significant relationship. This result suggests that firms with higher debt levels are more inclined to enhance risk disclosure as a mechanism to address information asymmetry and demonstrate accountability to investors and creditors. The study contributes to the ERM and corporate governance literature by providing evidence from an emerging market setting and highlighting the practical importance of financial structure in shaping risk transparency, offering relevant insights for corporate decision-makers and regulators to strengthen sustainable risk management practices.

This study aims to analyze the application of accounting according to PSAK 65 concerning consolidated financial statements to assess the company's financial performance at PT Warung Begok Indonesia. The object of this study is a company in the field of processed livestock manufacturing for the period 2023-2024. The data in this study are primary data sourced from the annual financial reports of the head office and branches. The total sample in this study was 3 company financial reports. Data collection techniques used interviews and documentation. The hypothesis in this study was tested using descriptive analysis techniques. Based on the data analysis carried out in this study, it shows that the financial statements before and after consolidation have an effect on the assessment of the company's financial performance. This study contributes to increasing knowledge and understanding of the head office and branch consolidated reports according to PSAK 65, and can assess the company's financial performance. Although the consolidated report has been carried out, it is hoped that the company will continue to apply controls and policies in its implementation, because this can affect the assessment of the company's financial performance so that it will be useful in decision making.

Tax avoidance can be detrimental to the country because it reduces the state's revenue. This study aims to analyze the effect of sales growth, capital intensity, and earnings management on tax avoidance with company size as a moderating variable. The population of this study comprises 221 manufacturing companies listed on the IDX in 2020-2024, with a sample of 64 companies selected via purposive sampling based on specific criteria, yielding a total of 320 observations analyzed using panel data regression (E-Views 12). The results show that sales growth directly affects tax avoidance, and company size moderates the relationship between sales growth and tax avoidance. However, capital intensity and earnings management do not have a significant effect, and company size cannot moderate the relationship between capital intensity and earnings management with tax avoidance. These findings emphasize that high sales growth can encourage companies to comply with tax regulations, thereby reducing tax avoidance, and that this effect can be suppressed by large company size due to greater reputational pressure and scrutiny. This study expands on previous research by making company size a moderating variable in the relationship between sales growth, capital intensity, and earnings management and tax avoidance.

This study examines how company size, asset growth, tangibility, leverage, and total asset turnover affect profitability in consumer manufacturing companies listed on the Indonesia Stock Exchange from 2019 to 2023, using secondary data collected via purposive sampling. The independent variables in this study include the natural logarithm of total assets, asset growth (this year’s total assets relative to the previous year), and tangibility (the fixed asset ratio to total assets). Leverage uses the debt-to-asset ratio, and total asset turnover uses the total asset turnover ratio, while the dependent variable of profitability uses return on assets. Of the 108 companies in the population, 19 that met the research sample criteria were selected, yielding 95 observations. Data analysis was conducted using multiple linear regression, accompanied by classical assumption tests and hypothesis testing through F-tests and t-tests. The findings of this study reveal that asset growth has a significant positive effect on profitability, while leverage shows a significant negative effect. However, firm size, tangibility, and total asset turnover do not exhibit significant relationships with profitability. This study contributes both theoretically and practically to understanding the internal determinants of financial performance in the consumer sector and serves as a reference for management.

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.

This study aims to analyze the quality control of white crystal sugar production at ABC Sugar Factory using the Statistical Quality Control (SQC) method. The research employed a descriptive quantitative approach with a case study design. The primary data consisted of production volume and defective product data during the 2024 production period, while supporting data were obtained through observation and interviews with the Quality Control department. The analytical tools applied included check sheets, histograms, Pareto diagrams, p control charts, and fishbone diagrams. The results show that from a total production of 190,745.89 tons, defective products amounted to 66.70 tons, representing 0.33 percent of total output. The identified defects consisted of wet sugar at 45 percent, brownish sugar color at 30 percent, and oversized sugar crystals at 25 percent. Defective products occurred only during the first to third production periods, while no defects were observed from the fourth to seventh periods. The p chart analysis indicates that the production process was statistically out of control in the early periods but became stable and controlled in the subsequent periods. From a managerial perspective, these findings provide practical guidance for improving manufacturing quality through enhanced process control, equipment maintenance, and workforce capability development.

This study aims to analyze inventory control of catfish seeds at ABC Company by comparing the conventional inventory method represented by the Economic Order Quantity (EOQ) approach and the Just In Time (JIT) system in order to improve cost efficiency. This research employed a descriptive quantitative approach using a case study design. Data were collected through direct observation, semi-structured interviews with company management, and documentation of inventory and cost records for the 2024 operational period. The analysis method involved calculating optimal order quantities, ordering frequency, delivery frequency, and total inventory costs using EOQ and JIT formulas, followed by a comparative cost efficiency analysis. The results show that the conventional method resulted in a total inventory cost of Rp 75,050,000 per year with high ordering frequency. In contrast, the implementation of the JIT system reduced inventory costs to Rp 18,762,500 per year, achieving a cost efficiency of 72%. These findings indicate that the JIT system is more cost-efficient than the conventional method; however, its implementation requires careful consideration of supplier capacity, logistics readiness, and biological risks associated with live inventory. This study contributes empirical evidence on the applicability of JIT in the aquaculture sector, which has different characteristics from manufacturing industries.