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This study aims to determine the effect of Credit Risk, Liquidity Risk, and Operational Efficiency on Financial Performance in the Conventional Banking Sector listed on the Indonesia Stock Exchange for the period 2019-2023. The research method used is quantitative using secondary data obtained from the Indonesia Stock Exchange, namely the annual financial reports of conventional banks. The sampling technique was carried out using the purposive sampling method. The analysis was carried out with credit risk using the Net Performing Loan (NPL) ratio, liquidity risk using the Loan to Deposits Ratio (LDR) ratio, operational efficiency using the Operating Expenses to Operating Income (BOPO) ratio and financial performance was measured using Return On Assets (ROA). The results of the study showed that partially the credit risk variable had a significant negative effect on financial performance, liquidity risk did not have a significant effect on financial performance, operational efficiency did not have a significant effect on financial performance. Simultaneously, credit risk, liquidity risk, operational efficiency had a significant effect on financial performance.

A 300 kVA generator set is an important tool as a backup power source in many sectors. Manual monitoring of generator set performance is often inefficient and can cause delays in detecting problems. Various advances in Internet of Things (IoT) technology have enabled the design of remote monitoring systems to overcome these limitations. This study aims to develop an IoT-based monitoring tool capable of monitoring important generator set parameters such as voltage, current, power, and engine temperature in real-time. This will enhance generator set management efficiency and reduce the risk of damage. The method used is an experimental approach involving the creation of an IoT-based monitoring system prototype. Operational data from the generator set is collected using sensors and then transmitted to an IoT platform for analysis. This research uses PZEM-004T sensors to monitor parameters such as power, current, and voltage. RTD PT100 sensors monitor parameters such as engine temperature, oil temperature, and turbo temperature. Honeywell PX3 sensors monitor oil pressure. Honeywell 1GT101DC sensors monitor engine speed. The results of the study show that the device works well with an average error of 0.76% from the sensors used. The greater the distance between the Wi-Fi modem and the device, the slower the data response speed.

Digitalization has become the driving force behind transformation in the logistics sector, particularly at seaports, which serve as key nodes in the global supply chain. Indonesian ports face persistent challenges such as high logistics costs and long vessel turnaround times. By implementing digital technologies such as the Port Community System (PCS), Artificial Intelligence (AI), and Blockchain, ports can enhance operational efficiency, accelerate service processes, and improve transparency and accountability. Digital systems also enable real-time integration among stakeholders, contributing to faster cargo flow and reduced national logistics costs. However, the implementation of digitalization is not without significant barriers, including limited information and communication technology (ICT) infrastructure, low digital competencies among the workforce, and high initial investment costs. Furthermore, ports face organizational cultural resistance to change and cybersecurity risks that threaten operational continuity. Smaller ports in developing countries often struggle to adopt these technologies independently due to budget constraints. To overcome these challenges, synergy between the government, port operators, and technology providers is essential. Programs such as the National Logistic Ecosystem (NLE) represent a strategic step to accelerate sustainable digital adoption. With a holistic approach encompassing technology, human resources, and business processes, digitalization can become a key catalyst in enhancing the competitiveness of Indonesian ports on a global scale.

Methane gas detection is crucial in the oil and gas sector to enhance safety and operational efficiency. This study examines the impact of three types of gas detectors—catalytic, infrared, and ultrasonic sensors—on accuracy and response time. The research was conducted at PT PHM's onshore and offshore sites to evaluate sensor performance in operational environments. A quantitative approach with direct field observation was used. Data were collected by measuring methane gas concentrations indicated by detectors, which were then compared to standard gas concentrations. Response times were recorded when the detectors were exposed to methane concentrations of 2.5% LEL until the alarm triggered at 40% of full scale. Data analysis was performed using descriptive statistics, homogeneity test, normality test, ANOVA, and post hoc tests. The results show that the infrared detector had a response time of 2.87 seconds with an accuracy of 0.218%. The catalytic detector had a response time of 8.91 seconds and accuracy of 0.489% and the ultrasonic detector had a response time of 6.15 seconds and accuracy of 0.842%. Overall, the infrared detector demonstrated the best performance in response time and is recommended for use at PT PHM.

The increasingly rapid development of digital technology is pushing MSMEs to adapt to change to remain competitive in the market. One strategic step that can be taken is to adopt a Management Information Sistem (SIM) to support information management, accelerate decision-making processes, and improve business efficiency. This article examines how MSMEs can optimize the use of MIS in their business activities, highlighting the benefits, challenges, and appropriate implementation strategies. The analysis shows that MSMEs' digital capabilities, human resource training, and the availability of technological infrastructure are critical components in supporting the successful optimization of MIS in the digital era.  

Engine overheating is a critical condition that can cause damage to internal components, reduce operational efficiency, and lead to overall system failure. The absence of an automatic protection system is one of the primary factors contributing to damage. This study aims to design and test a temperature sensor-based safety device that can automatically cut off the engine's working system when the temperature exceeds the safe limit. The method used is descriptive statistical analysis to evaluate the effect of independent variables (operational time duration and workload) on the dependent variable (coolant temperature). The system was tested through water heating simulation using an electric heater and controlled by an Arduino Nano microcontroller, a MAX6675 temperature sensor. Testing was carried out with variations in power load (150, 300, 450, 600, and 750 Watts) and operational duration (20, 40, and 60 minutes). Temperature data were collected and analyzed using ANOVA to determine the effect of load and time on temperature increase. The results showed that the temperature increased significantly with increasing power load, with temperatures approaching 100°C at loads ≥450 Watts in less than 20 minutes. The load variable shows a significant effect on temperature (p < 0.05), while the duration of time shows a nonlinear but not statistically significant upward trend. The safety device is proven to be able to automatically disconnect the system when the temperature reaches the specified maximum limit, thus effectively preventing engine damage due to overheating.

Hospital Information Sistems (HIS) integrating Decision Support Sistem (DSS) modules have been proposed to enhance operational efficiency and clinical decision-making in hospital settings. This study conducted a systematic literature review of 80 empirical articles published between January 2020 and May 2025, following PRISMA guidelines, to examine how DSS integration within HIS supports managerial and clinical decisions. Findings indicate that DSS integration improves patient registration speed by an average of 25, reduces medication errors by up to 15, and facilitates resource allocation and performance monitoring via analytic dashboards. Organizational resistance and inadequate IT infrastructure remain significant barriers. These results underscore the importance of designing user-friendly dashboards, implementing transparent inference engines, and adopting comprehensive change management strategies. The study extends the Technology Acceptance Model with a perceived risk construct and offers practical recommendations for developers and hospital managers aiming to optimize HIS–DSS implementation.

The effectiveness of accounting information systems (AIS) plays a crucial role in sup-porting the smooth recording, processing, and reporting of reliable and timely finan-cial information. AIS effectiveness is a key concern for cooperative managers in en-hancing operational efficiency and decision-making quality. This study aims to empiri-cally examine how work experience, educational background, and managerial support influence the effectiveness of AIS implementation. The research was conducted at savings and loan cooperatives operating in the South Kuta District. The sampling method used was non-probability sampling with a purposive sampling technique, and data were collected from 54 cooperative employees. Multiple linear regression analysis was employed to process the data. The results indicate that work experience, education level, and managerial support have a positive and significant effect on the effectiveness of AIS implementation. These findings suggest that the higher the em-ployees’ work experience and education, and the stronger the support from manage-ment, the more effective the AIS implementation within the cooperative environ-ment.

The study aims to evaluate the flow of material in the handling process and the quality control measures implemented throughout the production stages. The research employs a qualitative descriptive method, utilizing primary data collected through direct observation, interviews, and secondary data from relevant literature. The findings indicate that the IQF method significantly preserves the quality and freshness of the shrimp, ensuring compliance with international export standards. The study also identifies strengths, weaknesses, opportunities, and threats (SWOT analysis) related to the company's operations, highlighting the importance of maintaining high-quality raw materials and efficient production processes. Recommendations for improving quality control practices and enhancing operational efficiency are provided, emphasizing the need for continuous training and investment in modern technology.

The implementation of Supply Chain Management (SCM) makes many businesses manage information, goods, services and money as good as possible so they can provide fast and high-quality services to customers. Therefore, this study aims to implement SCM in Kopi Bintang Salatiga SMEs to decrease production costs, increase revenue, improve operational efficiency and establish good relationships with customers. This study discusses the SCM process and the added value in Kopi Bintang Salatiga SMEs so in the future Kopi Bintang Salatiga SMEs can have good risk management and increase added value. The data was collected through literature study and interviewing the owner of Kopi Bintang Salatiga SMEs. This study found that weather, natural disasters, fires and coffee pests are the factors with the highest risk in the SCM process of Kopi Bintang Salatiga SMEs. The added value of farmers & post-harvest coffee processors of Kopi Bintang Salatiga Kopi Bintang Salatiga SMEs is already quite high at 99%, while at grade I coffee sales is 71% and grade II is 47% it appears that coffee with grade II has lower added value, so it is needing accuracy of coffee processing at farmers & post-harvest coffee processors so the grade I coffee product is increased.

The rapid growth of the Muslim population, both in Indonesia and globally, has driven an increasing need for products to support worship, including Muslim socks. PT Soka Cipta Niaga (PT SCN), a producer of Muslim socks under the brand "SOKA", faces the challenge of low brand awareness, where consumers are more familiar with the product as "wudu socks". This research aims to analyze effective marketing strategies for the "SOKA" brand. The research results show that social media-based promotional strategies, such as Twitter, Facebook, and LinkedIn, are able to reach potential consumers more widely. In addition, activities such as bazaars, Muslim seminars, and Collaboration with Hajj and Umrah organizers has proven effective in expanding the market. The momentum of Ramadan and the Hajj season is a strategic time to increase sales.

This study proposes a hybrid approach combining Frequent Itemset Mining (FIM) and Algorithms and Genetic Algorithm for product layout optimization with a case study at PT. MPI Pharmacy. The FIM Algorithms is employed to extract association rules from 1,000 beauty product sales transactions, while the Genetic Algorithm is utilized to perform product placement based on these rules generated, with storage space constraints. Implementation results demonstrate that this hybrid approach successfully identifies 18 key association rules (support >15, confidence >80%) and proposes an optimal layout configuration model that reduces customer travel distance by 25 compared to conventional layouts used by MPI Pharmacy. The Genetic Algorithm solves complex rule-based optimization problems for product placement, which are limited by traditional market basket analysis (MBA) approaches that rely solely on association rules. This hybrid sistem not only improves pharmacy operational efficiency at PT MPI (reducing service time by 18) but also increases cross-selling opportunities by 22. Hence, inventory operations management impproved efficiently. The research findings contribute to the field of retail space optimization by effectively integrating association rule mining and evolutionary computation.  

The low proportion of Grade A harvest quality in Hokkaido corn farming remains a challenge for horticultural agribusiness practitioners. This study aims to develop an operational strategy based on a managerial evaluation of key production factors to improve technical efficiency and harvest quality. The research was conducted at PT Agricole Indonesia Makmur, Cianjur, using data from 24 planting periods in 2024. Six production input variables were analyzed to identify the most influential factors on yield quantity and quality. The results show that only three variables watering frequency, organic fertilizer, and plant age at harvest consistently contributed significantly to the production of Grade A output. Although these variables are inelastic, they play a critical role in maintaining quality. The proposed strategy includes a 10% increase in harvest quantity and a 30% conversion from Grade B to Grade A. Simulation results indicate an additional 7.67 kg of Grade A yield per planting period, generating a value increase of IDR 239,490. The strategy is considered feasible with a positive Return on Investment (ROI) of 14.04% assuming a Grade A selling price of IDR 35,000/kg.

This study explores how Aman Kuba Coffee Shop in Yogyakarta manages its raw material inventory to maintain product quality and deliver excellent customer service. Effective inventory management is crucial to ensuring consistent coffee flavor and customer satisfaction. This research employs a qualitative method using a case study approach, involving direct observation of inventory management practices at the shop. Data were collected through interviews with the owner, manager, baristas, and customers; on-site observations; and examination of documents such as stock records and quality standards. The findings reveal that a structured and consistent inventory system plays a key role in preserving raw material quality. Moreover, efficient inventory management positively influences customer satisfaction and supports the overall operational efficiency of the coffee shop.

This study discusses the application of inventory accounting in micro-enterprises, particularly at Toko Benih Tamban Tani, located in Kedawung Village, Mojo District. The main issue addressed is the suboptimal recording and calculation of inventory costs in accordance with the Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM). As a vital component of business operations, inventory should be managed systematically to accurately reflect the financial position and support operational efficiency. This research employs a descriptive qualitative approach. Data were collected through interviews, observations, and the collection and analysis of relevant documents, complemented by quantitative calculations using the Economic Order Quantity (EOQ) method to assess purchasing efficiency. The analysis aimed to describe current practices and compare them to the standards set forth in SAK EMKM. The findings indicate that inventory recording is still performed manually without a formal accounting format, and several operational costs such as shipping fees, electricity, and other supporting expenses have not been included in the calculation of the cost of goods sold. Additionally, based on EOQ analysis, the actual purchasing frequency is higher than the optimal amount, indicating inefficiencies in inventory procurement.

The food and beverage subsector of the manufacturing industry, despite experiencing positive growth, still faces challenges in maintaining financial performance stability, such as high operational costs, suboptimal capital structure, and efficiency differences across company scales. This study aims to examine the effect of operational efficiency, leverage, and firm size on financial performance, both partially and simultaneously, in food and beverage manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2022–2024 period. The sample consists of 16 companies selected using purposive sampling, with a total of 48 observations. This research applies a quantitative approach using panel data regression analysis and hypothesis testing through partial (t-test) and simultaneous (F-test) methods with STATA version 14. The best model used is the Random Effect Model (REM), selected through Chow, Hausman, and Lagrange Multiplier tests. The findings indicate that leverage has a significant negative effect on financial performance when tested partially, while operational efficiency and firm size do not have a significant partial effect. However, when tested simultaneously, operational efficiency, leverage, and firm size significantly influence the financial performance of food and beverage manufacturing companies listed on the IDX.  

This study aims to evaluate the financial performance of PT. Matahari Department Store Tbk for the 2023–2024 period using financial ratio analysis, particularly profitability and liquidity ratios. The study applies a descriptive quantitative approach, utilizing secondary data from the company’s financial reports. Profitability ratios such as Net Profit Margin, Return on Assets (ROA), and Return on Equity (ROE), along with liquidity ratios including Current Ratio, Quick Ratio, and Net Working Capital Ratio, were used as indicators. The results show a significant increase in profitability ratios, indicating improved operational efficiency and asset utilization. Meanwhile, the liquidity ratios also improved but remained below the optimal level, suggesting that the company still faces challenges in meeting its short-term obligations. In conclusion, PT. Matahari has demonstrated enhanced profitability but needs to strengthen its liquidity position to ensure financial stability.

The Hungarian method is a mathematical optimization algorithm used to solve assignment problems by finding the optimal allocation of resources to tasks. This research examines the application of the Hungarian method for both balanced and unbalanced maximization assignment problems. The balanced assignment problem involves an equal number of workers and jobs, while the unbalanced problem deals with unequal numbers. The study aims to analyze the effectiveness of the Hungarian method in solving maximization problems through mathematical modeling and algorithmic implementation. The research methodology includes literature review, mathematical analysis, and computational testing using various case scenarios. Results demonstrate that the Hungarian method can effectively solve both balanced and unbalanced maximization assignment problems by converting them into minimization problems through matrix transformation. The balanced cases show direct application of the classical Hungarian algorithm, while unbalanced cases require the addition of dummy variables to achieve matrix balance. The method proves to be efficient with polynomial time complexity O(n³), making it suitable for real-world applications. The research concludes that the Hungarian method provides optimal solutions for resource allocation problems in various organizational contexts, contributing to improved operational efficiency and cost-effectiveness in decision-making processes.  

The implementation of Hospital Management Information Systems (SIMRS) has become a strategic necessity to improve healthcare service quality and operational efficiency. This study focuses on evaluating the performance of SIMRS in the outpatient registration unit of Nur Hidayah Hospital Bantul, where system disruptions, inconsistent SOP application, and limited user training were identified as core issues. The research aims to assess how well the SIMRS aligns with the Human, Organization, and Technology components through the HOT-Fit model framework. A descriptive qualitative method was employed using purposive sampling to select five informants directly involved in SIMRS operations, including registration officers, IT staff, and the head of the medical records unit. Data were collected through in-depth interviews, direct observation, and documentation, and analyzed thematically based on the HOT-Fit model. The findings show that SIMRS contributes positively to workflow efficiency and data management; however, human factors such as inconsistent training and input errors remain prevalent. Organizational support is visible but lacks structured evaluation routines, and although the technological infrastructure is generally stable, peripheral device and network issues persist. The study synthesizes that effective SIMRS performance requires not only technological readiness but also consistent organizational commitment and continuous human resource development. It concludes that strengthening user competence, formalizing SOP usage, and improving infrastructure are essential to optimize the system's functionality and alignment with the HOT-Fit model.

This study aims to analyze the use of Online Integrated Banking Sharia System (OLIBSS) in improving the accuracy and efficiency of wadiah savings transaction processes at BPRS HIK Parahyangan. This study uses a qualitative approach with primary data collection methods through interviews and secondary data obtained from relevant internal documents. The results of the study indicate that the implementation of OLIBSS makes a significant contribution to improving the accuracy of recording wadiah savings transactions, minimizing human error, and accelerating operational processes that were previously carried out manually. In addition, this system also supports efficiency in reporting and internal supervision. Thus, the use of OLIBSS has proven effective in supporting a reliable accounting information system that is in accordance with sharia principles.