Publication Search

This study evaluated drug inventory management in a primary hospital pharmacy using the Economic Order Quantity (EOQ) method. A descriptive quantitative design was applied with secondary data collected from January to December 2024, covering annual demand, purchase price, ordering cost, and holding cost. The analysis revealed inefficiencies, as several drugs simultaneously faced overstock and stock out risks, indicating weaknesses in procurement planning. EOQ calculations suggested lower and more frequent procurement, reducing storage costs and improving stock control. The findings demonstrated that applying EOQ could minimize financial risks, optimize resource allocation, and ensure continuous access to essential medicines. This research highlights the relevance of structured inventory models in supporting hospital efficiency and sustainable pharmaceutical logistics, particularly in resource-limited primary hospitals.

The construction industry faces major challenges in managing material inventory, particularly among start-up companies that still rely on paper-based manual records. Such practices often lead to data inconsistencies, delays in decision-making, and project inefficiencies. This study aims to analyze the implementation of a cloud-based and real-time inventory management system utilizing spreadsheets at PT X, a start-up contractor located in Karanganyar, Central Java. The research employs a qualitative case study approach, with data collected through direct observation, semi-structured interviews with finance staff, logistics administration staff, and the project manager, as well as documentation of material inflows and outflows. The findings reveal that the use of cloud-based spreadsheets enhances data transparency, facilitates real-time monitoring between field and office, and accelerates stock opname validation. The system also supports more responsive decision-making regarding material reordering and request postponements. Nevertheless, several obstacles remain, including limited digital literacy among staff, potential input errors, and reliance on internet connectivity. Theoretically, this research contributes to the literature on accounting information systems and inventory management in small-scale construction sectors. Practically, it demonstrates that low-cost cloud solutions can improve operational efficiency and serve as a foundation for developing more integrated systems in the future.

This study explores the intersection of Artificial Intelligence (AI), agriculture, and decolonial philosophy, emphasizing the role of local knowledge as the foundation for sustainable agricultural technology in Indonesia. The research investigates how AI can be developed not as a tool of technological domination but as a dialogical partner that recognizes the epistemic value of indigenous wisdom. Using a mixed-method approach, the study combines algorithmic experiments applying lightweight Convolutional Neural Networks (CNN) with Explainable AI (XAI) methods such as SHAP and LIME with participatory interviews involving farmers in Bima District. Empirical findings show that models integrated with localized visualization and community-based interpretability improved user trust by 84% and reduced computational energy by 28% without compromising accuracy. More importantly, the interaction between AI and farmers revealed a form of epistemic integration where algorithmic logic aligns with traditional indicators, such as soil texture, humidity, and seasonal signs known to local farmers. Philosophically, this research asserts that sustainable AI should emerge from ecological and cultural contexts rather than imposing external frameworks. In the decolonial sense, it positions local farmers not as passive users but as active epistemic agents shaping the meaning of technology. Thus, AI becomes not only a technical instrument but a site of ethical and epistemic liberation that reaffirms human responsibility toward knowledge, culture, and the earth.

Penelitian ini mengkaji dampak dari mandatory disclosure serta praktik Good Corporate Governance (GCG) terhadap kualtias audit pada perusahaan yang tergabung dalam indeks LQ-45 Bursa Efek Indonesia selama periode 2022-2024. Analisis menggunakan metode regresi logistik biner dengan sampel sebanyak 78 perusahaan yang secara konsisten tercatat dan menyajikan laporan keuangan lengkap. Hasil menunjukkan bahwa GCG memberikan pengaruh positif yang signifikan terhadap kaultias audit, yang tercermin dari meningkatnya kemungkinan perusahaan diaudit oleh Kantor Akuntan Publik (KAP) tipe Big Four yang dikenal memiliki standar audit berkualitas tinggi. Di sisi lain, pengungkapan wajib tidak menunjukkan dampak signifikan terhadap kualitas audit. Temuan ini menggarisbawahi pentingnya penarapan tata kelola perusahaan yang baik sebagai mekanisme untuk emngurangi potensi konflik antara pemegang saham dan manajemen, yang pada akhirnya meningkatkan mutu audit. Oleh sebab itu, disarankan agar kerangka GCG diperkuat guna mendukung peningkatan kualitas audit, serta regulasi fokus pada pengembangan tata kelola perusahaan yang efektif dan transaparan

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

This study examines a critical perspective on the concept of rationality, which has long been regarded as the primary foundation of logical and objective decision-making. Using a qualitative approach through a literature study method, this research explores the integration of psychological, philosophical, and socio-cultural dimensions in understanding the limitations and complexities of human rationality. The findings reveal that rationality is not entirely neutral or free from bias. The psychological perspective highlights the influence of cognitive biases such as confirmation bias and anchoring effect, which often turn rationality into a justification for emotionally driven decisions. From a philosophical standpoint, David Hume’s argument that “reason is the slave of the passions” emphasizes the subordination of reason to emotion, while Horkheimer and Adorno reveal how modern rationality has evolved into an instrument of domination through ideologies of efficiency and control. In the socio-cultural dimension, rationality is shown to be non-universal, shaped by specific cultural and historical contexts, as exemplified by Max Weber’s concept of the “iron cage,” describing how individuals become trapped within bureaucratic systems of rationalization. The study concludes that classical rationality, characterized by its deterministic and calculative nature, is inherently bounded and insufficient to address the uncertainty and complexity of the modern world. Therefore, a new paradigm of rationality is needed—one that is integrative, reflective, and humanistic, combining logic with emotional, ethical, and social dimensions to produce decisions that are not only rational but also just and humane.

This quantitative study analyzes the influence of Ship Docking Schedule (X1), Port Availability (X2), and Loading and Unloading Productivity (X3) on Vessel Waiting Time (Y) at Tanjung Intan Cilacap Port. Data were collected through questionnaires administered to 86 vessel crew respondents and analyzed using Multiple Linear Regression (SPSS v.26). The t-test results indicate that X1, X2, and X3 individually exert a positive and significant influence on Y.1 The regression model demonstrates robust predictive power, explaining 55.2% of the variance in vessel waiting time (R^2{adj}=0.552). Based on the standardized beta coefficients, the Ship Docking Schedule (X1, \beta=0,385) is identified as the most dominant factor, followed by Loading and Unloading Productivity (X3, \beta=0,302) and Port Availability (X2, \beta=0,307).1 This finding confirms that optimizing maritime logistics performance, particularly in bulk terminals facing physical constraints, requires prioritizing procedural efficiency and scheduling accuracy.

CPU scheduling is a core function in modern operating systems that significantly impacts system performance and efficiency. Among various scheduling algorithms, Priority Scheduling is widely used and exists in two main variants: non-preemptive and preemptive. The non-preemptive mode allows a process to run to completion, while the preemptive mode can interrupt a running process for a higher-priority one. Understanding the behavioral differences between these modes is crucial but often challenging through manual calculations. To address this, an interactive web-based application was developed to simulate and visualize both preemptive and non-preemptive Priority Scheduling algorithms. The research method involved designing the system logic based on the core principles of each scheduling variant, followed by implementation using standard web technologies: HTML, Tailwind CSS, and JavaScript. The application allows users to input custom process data or load predefined case studies, select the scheduling mode, and instantly receive a comprehensive analysis. The results include a dynamically generated Gantt chart, a detailed performance metrics table (including turnaround time and waiting time), and a step-by-step execution log. Through a comparative analysis of a specific case study, the application is proven to be an effective educational tool. It accurately simulates both modes and visually demonstrates the impact of preemption on execution order, resource utilization, and key performance metrics, thereby simplifying the learning process for students and educators.

Employee performance is a crucial element in determining the success of the company's operations, especially in the logistics sector which demands efficiency and speed of service. This study aims to analyze the factors that affect the performance of employees of PT. Universal Tunggal Logistics which operates at the Port of Tanjung Emas Semarang. The research method used is a quantitative approach with data collection techniques through questionnaires distributed to employees of the operational and administrative departments. The variables tested included work motivation, work environment, leadership style, and employee competence. Data analysis was conducted to identify the dominant factors that contribute to the performance improvement. The results of the research are expected to make an empirical contribution to the development of more effective managerial strategies in improving productivity and service quality. The novelty of this research lies in its focus on the EMKL (Sea Cargo Expedition) industry, which has unique operational characteristics compared to other service sectors, such as high dependence on cross-agency coordination, port dynamics, and tight time demands. The findings of this study are expected to be a reference for similar logistics companies in designing adaptive and evidence-based human resource policies, as well as strengthening competitiveness amid the challenges of globalization and digitalization of the logistics sector.

This study analyzes the influence of internal operational variables—Process, Service Quality, and Facilities—on the Export Performance of PT. Panah Perdana Logisindo, a freight forwarding company in Semarang. Maritime logistics plays a crucial role in national competitiveness, but company performance often faces challenges amidst the complexity of global supply chains . Employing a quantitative approach with a sample of 100 export service users, multiple linear regression analysis indicates that all three independent variables positively and significantly affect Export Performance (Y). The results reveal that Process has the most dominant influence (\beta=0.309), followed closely by Facilities (\beta=0.302) and Service Quality (\beta=0.255). The model explains 53.0% of the variance in the company's Export Performance (Raja Ferry, 2025 2). These findings confirm the necessity of strengthening micro-operational aspects (efficient procedures, robust networks, and staff professionalism) as a core strategy for enhancing profitability and customer loyalty amidst macro-logistical challenges in Indonesia.

Mathematics learning plays an essential role in developing students’ logical, systematic, and analytical thinking skills. One of the higher-order thinking abilities that serves as a main focus in mathematics education is mathematical reasoning. This ability is closely related to the process of mathematical proof, which functions to justify the truth of a statement logically. This study aims to examine the relationship between the understanding of proof concepts and the development of mathematical reasoning among junior high school students through a library research approach. Data were obtained from various sources such as textbooks, journal articles, research findings, and relevant educational documents. The analysis was conducted using the content analysis method to review both conceptual and empirical findings from the literature. The results indicate that understanding the concept of proof not only helps students comprehend the logical structure of mathematics but also contributes to the development of deductive and reflective reasoning skills. Engaging in proof activities enables students to shift from merely imitative reasoning toward independent logical construction (creative reasoning). However, teaching proof at the junior high school level still faces several challenges, such as students’ limited understanding of basic logic, time constraints, and the lack of teacher training in teaching proof effectively. Therefore, learning strategies that emphasize students’ thought processes, provide opportunities for exploration, and integrate proof activities into meaningful learning contexts are needed.

This research aims to examine the impact of integrating cleaner production practices with green supply chain technologies as a comprehensive approach to achieving environmental sustainability. The study highlights that cleaner production and green supply chain management represent advanced, innovative strategies that have emerged as a response to the growing environmental challenges caused by the rapid expansion and diversification of industrial activities. These technologies are not only environmentally oriented but also carry significant economic implications for organizations. The findings emphasize that adopting cleaner production involves minimizing waste generation, improving production efficiency, and ensuring that processes are designed to have minimal adverse effects on the environment. On the other hand, green supply chain technologies focus on integrating environmental thinking into every stage of the supply chain—ranging from product design, material sourcing, and manufacturing processes to logistics, product delivery, and end-of-life management. The study concludes that the synergy between these two approaches provides multiple benefits. From an environmental perspective, they contribute to reducing carbon emissions, particularly from fuel-powered machinery and transportation systems. They also promote the rational use of resources, including energy, water, and raw materials, thereby helping to preserve natural resources for future generations. From an economic perspective, their implementation leads to reduced operational costs by enhancing efficiency, decreasing waste disposal expenses, and optimizing resource usage. Furthermore, the integration of cleaner production and green supply chain technologies supports compliance with environmental regulations and enhances the corporate image of economic units, enabling them to gain competitive advantages in increasingly eco-conscious markets. Overall, the research affirms that these practices are essential tools for confronting and mitigating the environmental pollution challenges of modern industries, while simultaneously fostering sustainable economic growth and long-term environmental protection.

The development of digital technology has transformed how institutions communicate with the public, including the Maritime and Logistics Education Corporation of Indonesia (PT PMLI). This study is motivated by the strategic role of Cyber Public Relations in building institutional image in the digital era. The research focuses on how PT PMLI implements Cyber PR strategies to establish its institutional image. The objective is to identify the strategies applied and how the image is built in digital spaces. This study employs Ronald D. Smith’s Cyber PR Strategy Theory and Veljkovic’s Institutional Image Theory, using a qualitative descriptive method through in-depth interviews with five internal and external informants. The findings show that PT PMLI conducts situation analysis and audits using a SWOT approach, with strengths in digital integration and proactive approaches, and weaknesses in limited human resources and state-owned enterprise regulations. Opportunities come from digital innovation and external collaboration, while threats include the spread of hoaxes and sensitive data exposure. The strategic objectives focus on increasing audience engagement through interactive and educational content, as well as maintaining online reputation through consistent and responsive communication. PT PMLI utilizes its website and social media platforms Instagram, TikTok, and YouTube and develops trend-based content evaluated through media plans and engagement indicators.

Sustainability reporting has been increasingly popular in recent years as businesses become more aware of environmental challenges. 96% of the top businesses in the world have implemented sustainability reporting practices, according to a KPMG survey. The effect of sustainability reporting practices on environmental performance is examined in this study. Sustainability reports, the implementation of GRI standards, and external assurance are used to gauge sustainability reporting procedures; firm size is used as a control variable. In this study, 305 observational data from manufacturing firm over a five-year period (2018-2022) were analysed quantitatively using binary logistic regression. The findings indicate that while the use of GRI standards has a positive and significant effect on environmental performance, sustainability reports and external assurance have no significant effect. These findings show that the implementation in GRI standards encourages business commitment to sustainable practices and transparency, both of which have a significant impact on environmental performance. In the meanwhile, external assurance and sustainability reports tend to be mostly symbolic and do not demonstrate a real commitment to environmental improvement. Environmental performance is positively impacted by the control variable, firm size. These findings suggest that a company's environmental performance is correlated with its size. As a control variable, firm size contributes to maintaining and clarifying the relationship between environmental performance and sustainability practices while ensuring objective and valid study findings. This study emphasizes the importance of strengthening the quality of sustainability reporting and expanding the application of external standards and assurance to improve the credibility and accountability of corporate environmental performance in Indonesia.

Auditor switching is very important for companies because it maintains the independence of auditors and has objectivity in assessing the fairness of the company's financial statements and maintaining public trust.  This study aims to determine the role of company growth in moderating the relationship between audit report lag and public ownership to auditor switching. This research was conducted using quantitative methods and the data source used, namely secondary data taken from the Indonesia Stock Exchange in the form of annual financial statements. The population in this study is 70 companies in the infrastructure sector in 2019 – 2023. The sampling technique used is purposive sampling. The sample in this study is 21 companies with a total of 105 sample data. The data analysis technique used in this study is logistics regression using the Eviews version 12 application. The results of this study show that simultaneously Audit Report Lag and Public Ownership have an effect on Auditor Switching. While partially Audit Report Lag has no effect on Auditor Switching, Public Ownership has no influence on Auditor Switching. The role of Company Growth is able to moderate the relationship between Audit Report Lag and Auditor Switching. The role of Company Growth is not able to moderate the relationship between Public Ownership and Auditor Switching.  

This research seeks to examine the innovative governance of police logistics to enhance the operational service efficiency at the Logistics Bureau of East Java Regional Police. The research context arises from the need to reform police logistics management to meet technological innovations and demands for public accountability. This research utilized a qualitative method featuring a case study approach, incorporating extensive interviews with logistics officers, operational personnel, and third-party providers, along with firsthand observations of digital logistics management systems. The results indicate that the adoption of the Integrated Logistic Management System (ILMS) and barcode tracking has improved transparency, distribution speed, and efficiency in operational budgets. Nonetheless, difficulties persist in human resource capability and interdepartmental collaboration. The dialogue suggests that these advancements correspond with adaptive governance theory, stressing bureaucratic adaptability in response to organizational shifts, and public service motivation, which underscores the importance of motivation and professionalism in enhancing the quality of public services. The research finds that digital innovation and performance-oriented assessment enhance the efficiency of police operational services. It suggests enhancing human resource capabilities, performance assessment systems, and collaboration among units to attain transparent, effective, and sustainable logistics management.

The DC motor serves as the main drive of the vessel and is equipped with a rotary encoder that functions to regulate the movement of the sensor in measuring sediment levels. This rotary encoder is also used to monitor and represent the rotational speed of the DC motor. System testing was carried out by implementing a Fuzzy Logic Controller (FLC) algorithm to control the DC motor speed in moving the vessel, ensuring stable motion. This fuzzy logic–based approach is expected to improve accuracy and efficiency in sediment volume calculations, while also reducing potential errors that commonly occur in manual methods. Simulating motor speed control using the fuzzy logic algorithm in MATLAB, the best test results were achieved over several trials, with a rise time of 376.310 ms and an overshoot of 83.33%. Motor speed measurements using both a tachometer and Arduino produced consistent results, with an average relative error of 0.18%.

This research seeks to evaluate how asset and logistics management innovation enhances operational efficiency in the Household Division of the East Java Provincial Secretariat. The study focuses on the bureaucratic reform initiative aimed at attaining transparent, accountable, and effective asset management via digital transformation. The emphasis lies on how digital inventory systems, purchasing methods, and asset allocation aid in minimizing budget waste and improving efficiency. This research utilized a qualitative descriptive strategy through a case study technique. Data were gathered via extensive interviews, direct observation, and analysis of internal documents, subsequently undergoing data reduction and triangulation to confirm validity. The results indicate that the adoption of the e-inventory system has enhanced administrative efficiency by as much as 30%, but issues persist with data integration and employee digital skills. Additionally, the lack of a real-time public monitoring system restricts transparency in logistics management. The research advises enhancing employee training, creating cohesive SOPs, and adopting technology-driven assessment systems. These actions are anticipated to promote effective, efficient, and sustainable asset management in the East Java Provincial Secretariat.

This study investigates the algebraic reasoning processes of university students when expanding mathematical expressions in the context of nonroutine problem-solving. The research adopts a qualitative approach to explore how students interpret algebraic structures, apply symbolic transformations, and construct logical explanations while working through unfamiliar tasks. Data were collected through written tests, task-based interviews, and detailed analysis of students’ solution strategies. The findings reveal significant variation in students’ ability to generalize patterns, recognize structural relationships, and justify algebraic procedures. Students with strong conceptual understanding demonstrated flexible reasoning, coherent explanations, and appropriate use of algebraic properties. In contrast, students who relied heavily on procedural rules often struggled with symbolic manipulation, produced fragmented reasoning, and exhibited misconceptions related to variables and distributive operations. These results highlight the importance of fostering conceptual understanding, metacognitive awareness, and reasoning-oriented instruction in university mathematics. The study provides insights for educators seeking to design learning environments that promote deeper algebraic thinking and enhance students’ ability to solve complex, nonroutine problems.

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.