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This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Selecting the right supplier is a critical aspect of supply chain management, especially in a retail business like Parfume Corner, which relies on product quality, availability, and on-time delivery. This study aims to implement the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method as a multi-criteria decision-making approach to determine the best perfume supplier. The VIKOR method was chosen because of its ability to handle conflicts between criteria and produce optimal compromise solutions. The evaluation criteria used include product quality, price, on-time delivery, after-sales service, and flexibility in negotiations. Data were collected from five potential suppliers through observation, interviews, and historical transaction documents. The analysis results showed that one supplier obtained the lowest VIKOR index score, thus being determined as the best compromise solution. The implementation of the VIKOR method proved effective in providing objective and transparent recommendations, which can support Parfume Corner's strategic decisions in building long-term partnerships with reliable suppliers. This approach can also be adapted by similar businesses to improve procurement efficiency and quality. The test results obtained were that in the expert test a Good value was obtained, namely 80%, while in the system test a Very Good conclusion was obtained, namely 100%.

Presidential Instruction Number 1 of 2025 concerning State Expenditure Efficiency gave rise to a fiscal paradox at the beginning of the new administration, with a cut of Rp306.69 trillion facing a 17.9% APBN deficit for flagship programs such as Free Nutritious Meals, which triggered doubts about public trust, especially students at the University of Jambi affected by the BOPTN adjustment. This study analyzes the influence of student perceptions on the dimensions of effective-efficient (X1) and transparent-accountable (X2) policies on public trust (Y) as an indicator of government legitimacy. A quantitative survey approach was applied to 400 active students at the University of Jambi (proportional random sampling using the Slovin e=0.05 formula), with SPSS multiple linear regression analysis after classical assumption testing, validity (r> r-table), and reliability (Alpha Cronbach's 0.920 (x1); 0.949 (x2); 0.918 (y)). The results show a significant simultaneous effect (F=200.951; sig=0.000), partial X1 is dominant (t=7.116; β=0.162; sig=0.000) and X2 is significant (t=5.532; β=0.110; sig=0.000), with R²=0.503 explaining 50.3% of the variation in trust. The findings confirm the theory of Easton (1965) and Weber (1947) that efficiency performance evaluation shapes trust, so it is recommended that a real-time APBN dashboard, transparent communication to regional PTNs, and fiscal literacy strengthen the legitimacy of good governance.

This study aims to evaluate the public transportation service policy of Trans Jatim Bus Corridor 2, operating on the Mojokerto–Surabaya route, using the six evaluation indicators proposed by William N. Dunn: effectiveness, efficiency, adequacy, equity, responsiveness, and appropriateness. Employing a qualitative descriptive approach, data were collected through interviews, direct observations, and secondary data analysis. The findings reveal that the Trans Jatim Corridor 2 service has significantly contributed to improving accessibility and mobility for the people of East Java. In terms of effectiveness, the service meets transportation policy objectives by offering strategic routes, consistent schedules, and accessible bus stops. Efficiency is demonstrated through optimal utilization of limited fleets and operational costs, while still meeting high passenger demand. Regarding adequacy, the service is generally sufficient; however, overcrowding during peak hours indicates the need for capacity improvements. Equity is reflected in the widespread distribution of bus stops, although disparities remain in the availability of facilities and route information across several stops. The service shows high responsiveness through quick handling of passenger complaints via applications and social media. Additionally, service appropriateness is evident in its punctual operations supported by GPS-based monitoring and real-time information through the TRANSJATIM-AJAIB application. Overall, the evaluation shows that Trans Jatim Corridor 2 provides effective, efficient, and responsive public transport services, yet requires improvements in capacity and equitable distribution of facilities to achieve optimal service quality.

The Information System registration module for the Regional Taxpayer Identification Number (NPWPD) was developed using the Laravel framework and implemented by the Taxpayer Identification Agency (BPPKAD) at Kediri City. The system was designed to digitize the NPWPD registration process, which was previously done manually. This traditional approach often led to long queues, extended processing times, and, at times, errors in data entry. The new system offers several key advantages, including an online registration form that allows taxpayers to upload required documents such as photos of ID cards, business locations, and other necessary paperwork. Data validation is performed by officers to ensure accuracy, and automatic notifications are sent to taxpayers, informing them of the status of their applications. The implementation of this system has had several positive impacts, such as significantly improving the efficiency of administrative processes, reducing the manual workload for officers, and increasing transparency and accountability in public services. Moreover, it has improved customer satisfaction by providing faster, more accurate, and more responsive services. This system supports the creation of a streamlined, user-friendly, and effective method for taxpayers to register for NPWPD online, enhancing the overall quality of public sector service delivery.

The development of the digital era has triggered a significant transformation in consumer shopping patterns, which have now shifted from conventional retail to Quick Commerce (Q-Commerce). This article analyzes the phenomenon of changing consumer behavior driven by preferences for speed, practicality, and time efficiency, with the COVID-19 pandemic as the main catalyst. The study highlights that the success of Q-Commerce is highly dependent on Logistics Service Quality (LSQ), particularly in terms of timeliness, courier interaction quality, and order condition. Despite offering convenience that disrupts physical retail, this business model faces serious sustainability challenges, including high last-mile operational costs, difficulty achieving profitability leading to the closure of several market players, and intense competition from hybrid retail models. In addition, traffic safety issues and increased carbon emissions are highlighted as social and environmental impacts. This study concludes that while Q-Commerce holds great potential, its sustainability requires strategic innovations that balance service speed with cost efficiency and ecological responsibility.vThe development of the digital era has triggered a significant transformation in consumer shopping patterns, which have now shifted from conventional retail to Quick Commerce (Q-Commerce). This article analyzes the phenomenon of changing consumer behavior driven by preferences for speed, practicality, and time efficiency, with the COVID-19 pandemic as the main catalyst. The study highlights that the success of Q-Commerce is highly dependent on Logistics Service Quality (LSQ), particularly in terms of timeliness, courier interaction quality, and order condition. Despite offering convenience that disrupts physical retail, this business model faces serious sustainability challenges, including high last-mile operational costs, difficulty achieving profitability leading to the closure of several market players, and intense competition from hybrid retail models. In addition, traffic safety issues and increased carbon emissions are highlighted as social and environmental impacts. This study concludes that while Q-Commerce holds great potential, its sustainability requires strategic innovations that balance service speed with cost efficiency and ecological responsibility.

This study analyzes the effectiveness of using BNI Bank’s QRIS in improving transaction convenience and efficiency at the canteen of Universitas 17 Agustus 1945 (UNTAG) Surabaya. The research was conducted by a team led by Ida Ayu Nuh Kartini, S.E., M.M., together with students of the Management Study Program, during November–December 2025. The rapid development of digital banking has encouraged the adoption of QRIS as a standardized non-cash payment system regulated by Bank Indonesia. This study aims to examine the level of QRIS BNI utilization, assess ease of use (application access and QR code scanning), and evaluate transaction efficiency, including time savings, queue reduction, and error minimization. A descriptive quantitative approach was employed using a survey method with a five-point Likert scale questionnaire distributed to 36 respondents consisting of students, lecturers, and administrative staff who had used QRIS at the canteen. Primary data were analyzed descriptively using averages and percentages, supported by relevant literature. The results show that QRIS is perceived as highly effective, with scores above 90% for ease of use (95%), transaction speed (92.78%), reduction of change-related issues (97.78%), support for digitalization (97.22%), and security (91.11%). The main limitation identified is dependence on internet connectivity (85%). Overall, respondents strongly support the full implementation of non-cash payment systems at the campus canteen.

Government expenditure is a great reason in economic stability and its impact on the balance of payments is dire. In this light, this paper seeks to use the time series analysis method and the ARDL model to investigate the association between the balance of payments of Iraq and the public spending within the 2004-2023 period. The Eviews 13 software was used to analyse it. The findings show that there is a positive association between spending by the people and balance of payment especially at the short run. The latter findings indicate that the efficiency of government expenditure reform is a necessary tool to accomplish the expansion and close the balance of payments deficit. This study highlights the importance of strategic fiscal policies and government spending in achieving a balanced economy and sustainable growth. Additionally, it emphasizes the need for continuous monitoring and adjustment of public spending to ensure its alignment with national economic objectives. The findings contribute to the understanding of fiscal policy implications in developing economies, especially in the context of Iraq’s economic challenges.  

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.

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.

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

This community service activity was conducted at Grind Now Coffee Bar, an MSME in Batam City’s food and beverage sector, to address challenges in managing raw material inventory, which often led to inaccurate stock records, shortages, and overstocking, affecting daily operations and efficiency. The program aimed to improve inventory management by applying the Economic Order Quantity (EOQ) and Reorder Point (ROP) models, providing a systematic and measurable approach to procurement. Activities included detailed observation of operational practices, interviews with staff to understand current challenges, collection of data on raw material usage and purchasing patterns, and the implementation of a website-based inventory control system to streamline record-keeping and monitoring. The results showed that EOQ and ROP models helped determine optimal order quantities and appropriate reorder timing, while the inventory system improved organization, reduced errors, and enabled real-time stock monitoring. Overall, this intervention enhanced decision-making, promoted operational efficiency, and supported the sustainability and competitiveness of Grind Now Coffee Bar.

The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in formative assessments, emphasizing the importance of considering cultural diversity, institutional culture, and educators' beliefs. AI technologies, while powerful in automating grading and providing personalized assessments, often face limitations in addressing complex student responses that require human judgment. Furthermore, cultural factors, such as students' prior exposure to technology and different cultural attitudes towards AI, play a critical role in the acceptance and effectiveness of these tools. Organizational factors, including leadership support, digital literacy, and the readiness of institutions to adopt AI, are also key determinants in the successful implementation of AI systems in education. Teachers’ beliefs about assessment influence their acceptance and use of AI tools, highlighting the need for professional development and training to ensure that AI enhances pedagogical goals rather than replacing human expertise. The study concludes that the alignment of technology, culture, and assessment beliefs is essential for the effective use of AI driven formative assessments in educational settings. Recommendations for educational institutions include adopting a socio technical approach to AI integration, with a focus on providing resources, training, and fostering a culture of innovation. Future research directions should focus on expanding studies to diverse educational contexts, conducting longitudinal research on AI’s impact on learning outcomes, and exploring additional socio technical frameworks to guide AI adoption in education.

The integration of artificial intelligence (AI) in forensic investigation has significantly transformed the analysis and authentication of digital evidence. This paper explores the role of AI technologies, specifically machine learning and deep learning algorithms, in examining digital evidence from various sources, including computers, mobile devices, and network systems. We provide an in-depth analysis of current AI-based forensic tools, their efficiency in evidence authentication, and the challenges they face regarding legal admissibility. Our findings indicate that AI-powered forensic systems can detect digital evidence tampering with 94.7% accuracy, drastically reducing analysis time from weeks to hours. However, challenges remain, particularly in areas such as algorithmic transparency, bias prevention, and ensuring the integrity of the chain of custody. This research offers a framework for incorporating AI in forensic laboratories, while also addressing crucial legal and ethical concerns to ensure the admissibility of AI-analyzed evidence in court. These considerations are essential for the widespread acceptance and use of AI in forensic investigations.

The use of artificial intelligence technology in elementary education is becoming increasingly relevant as teachers demand to develop creative and efficient teaching materials. Problems faced by fifth-grade elementary school teachers include limited time, a variety of ideas, and reliance on conventional methods in developing teaching materials, resulting in suboptimal learning creativity. This study aims to describe the use of ChatGPT by fifth-grade elementary school teachers in developing creative teaching materials and to identify the benefits and constraints of its use in the learning process. The research method used is a descriptive qualitative approach with data collection techniques through literature studies and online questionnaires based on Google Forms. The research respondents were fifth-grade elementary school teachers who were familiar with and used ChatGPT in lesson planning. The data obtained were analyzed using qualitative descriptive analysis techniques through the stages of data reduction, data presentation, and drawing conclusions. The results of the study are expected to provide a comprehensive picture of the role of ChatGPT as a digital assistant for teachers in increasing the efficiency and creativity of teaching material development in elementary schools.

Traffic performance on urban road segments is strongly affected by vehicle volume and travel time, particularly during peak periods. This study analyzes the relationship between travel duration and the total number of vehicles passing along Otto Iskandar Road as an illustration of urban traffic conditions. Data were collected through field surveys, focusing on two main variables: average vehicle travel time and total traffic volume. Statistical analysis was performed using IBM SPSS Statistics, including normality testing and the Wilcoxon Signed Rank Test to identify potential differences between the observed variables. The results show a difference in average values between travel duration and vehicle volume; however, this difference is not statistically significant at the 95% confidence level (p = 0.180). These findings indicate that increases in traffic volume do not always lead to proportional increases in travel time, although they can still influence the stability and efficiency of traffic flow. The results are consistent with previous studies, such as Halim (2021), who reported that U-turn movements affect speed and traffic performance, and Handayani et al. (2024), who found that parking activities and vehicle maneuvers reduce road capacity. Other studies also highlight the impact of side friction and traffic flow variations on speed and saturation levels. Overall, this study emphasizes the importance of managing vehicle flow and monitoring travel time in urban transportation planning and traffic management.

The attendance recording system at PLN ULP Tanjungbalai still relies on manual, paper-based methods, resulting in delays in data recap, reduced efficiency, and a high potential for recording errors. This condition affects the accuracy of employee attendance information, which is essential for administrative activities and managerial decision-making. Based on these issues, this practical work aims to design and develop a web-based e-attendance application as a solution to enhance efficiency, processing speed, and the accuracy of attendance recapitulation. The system was developed using PHP as the programming language and MySQL as the database management system, following several stages including requirement analysis, system design using UML, and implementation of a web-based user interface. The application provides essential features such as user login, daily attendance recording, employee data management, attendance notes (permission, sickness, etc.), and automatic attendance report generation. The system is designed for two types of users—Admin and Employees—each with specific access rights. The implementation results indicate that the e-attendance application significantly improves the efficiency of attendance administration at PLN ULP Tanjungbalai. Data collection and recapitulation become faster, more structured, and less prone to errors, while also enabling administrators to monitor employee attendance in real time. Therefore, this web-based e-attendance application serves as an effective solution to support operational activities and enhance the quality of employee attendance management.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

The discharge of surfactant-laden wastewater from the rapidly expanding laundry industry poses significant environmental risks, especially in densely populated urban areas. While constructed wetlands (CWs) and Eco-Enzyme technology have shown promise for surfactant remediation, their standalone application requires long hydraulic retention times (HRTs), limiting practical implementation. This study evaluated the efficacy of a novel integrated system combining a subsurface constructed wetland (SSFCW) with fruit peel-derived Eco-Enzyme to treat synthetic laundry wastewater. Over a 6-day treatment period, the combined system achieved a remarkable surfactant removal efficiency of 99.63%, reducing the concentration from 225 mg/L to 0.835 mg/L—well below the regulatory threshold of 3 mg/L. The synergistic degradation mechanism involves enzymatic hydrolysis via Eco-Enzyme lipase and protease activity, complemented by microbial mineralization in the wetland rhizosphere. This system maintains optimal environmental conditions, with a stable pH of 6.85-7.32 and a temperature of 30.9-35.2°C, supporting robust biological activity. These findings demonstrate that the integrated Eco-Enzyme/SSFCW system overcomes the limitations of conventional HRT approaches, offering a highly efficient, sustainable, and practical decentralized wastewater treatment solution for the laundry industry.