Publication Search

Deli Serdang Regency located in North Sumatra Province has significant economic potential, supported by the agriculture, industry, trade, and service sectors. In recent years, Deli Serdang Regency has experienced fluctuations in its economic growth. This study analyzes the economic growth of Deli Serdang Regency using the Solow-Swan model. Secondary data from the Central Statistics Agency (BPS) in 2019-2020 accessed through the website https://www.bps.go.id/id analyzed to estimate model parameters. The results show that: (1) the level of savings and investment has a significant effect on economic growth, (2) technology and labor growth play an important role in increasing productivity, and (3) fiscal and monetary policies need to be optimized to increase economic growth. The Solow-Swan model effectively explains the dynamics of economic growth in Deli Serdang Regency.

This paper presents an IoT-enabled dual-axis solar tracking system that integrates  a Kalman filter and a Proportional-Integral-Derivative (PID) controller to enhance tracking accuracy, energy efficiency, and operational stability. Addressing the ongoing challenge of maxi- mizing photovoltaic (PV) panel output, the proposed system leverages an ESP32 microcontroller and the Blynk platform to provide real-time monitoring, remote parameter adjustments, and flexible connectivity. Light Dependent Resistor (LDR) sensors measure sunlight intensity from multiple directions, while MG90S servo motors dynamically adjust the panel’s azimuth and elevation. The Kalman filter refines noisy sensor data to yield precise sun position estimates, enabling the PID controller to respond quickly and accurately to deviations in panel orientation. Through extensive testing conducted over several days, including both clear and partially cloudy conditions, the system achieved an average Root Mean Square Error (RMSE) as low as 1.2° under clear skies and maintained RMSE below 2.0° even under partial shading. Compared to a fixed-panel baseline, daily energy harvesting improved by approximately 43%. These results confirm that advanced estimation and control algorithms, when combined with IoT functionali- ties, significantly outperform simpler tracking methods and static installations. Furthermore, the low-cost, compact design and user-friendly interface facilitate practical deployment in a range of scenarios, including small-scale and off-grid installations. By ensuring continuous alignment of the PV panel with the sun, the system not only increases overall energy capture but also reduces maintenance requirements through remote oversight. This research thus offers a robust, scalable approach to improving solar energy utilization in diverse and evolving environmental conditions.

The Cuckoo Search Algorithm (CSA), while effective in solving complex optimization problems, faces limitations in random population initialization and reliance on fixed parameters. Random initialization of the population often results in clustered solutions, resulting in uneven exploration of the search space and hindering effective global optimization. Furthermore, the use of fixed values for discovery rate and step size creates a trade-off between solution accuracy and convergence speed. To address these limitations, an Enhanced Cuckoo Search Algorithm (ECSA) is proposed. This algorithm utilizes the Sobol Sequence to generate a more uniformly distributed initial population and incorporates Cosine Annealing with Warm Restarts to dynamically adjust the parameters. The performance of the algorithms was evaluated on 13 benchmark functions (7 unimodal, 6 multimodal). Statistical analyses were conducted to determine the significance and consistency of the results. The ECSA outperforms the CSA in 11 out of 13 benchmark functions with a mean fitness improvement of 30% across all functions, achieving 35% for unimodal functions and 24% for multimodal functions. The enhanced algorithm demonstrated increased convergence efficiency, indicating its superiority to the CSA in solving a variety of optimization problems. The ECSA is subsequently applied to optimize earthquake evacuation space allocation in Intramuros, Manila.

The A* Algorithm is a path-finding algorithm that primarily uses weighted graphs and focuses on the heuristic values of nodes. However, while effective in generating a near-optimal path in a static environment, the traditional algorithm faces limitations in navigating dynamic environments, often resulting in collisions due to its inability to recognize dynamic and moving obstacles. This limitation makes it inefficient especially in complex environments with real-world scenarios. To address these limitations, an Enhanced A* Algorithm is proposed. This algorithm utilizes Navigation Mesh data structure to generate a more optimal route with local path planning and to dynamically adjust the parameters in two-dimensional non-grid environments. The performance of the algorithms was evaluated using 12 benchmarks, each corresponding to a distinct test case and levels of complexity. Then, in terms of dynamic obstacle avoidance, a comparison between the Enhanced A* Algorithm and the traditional algorithm was conducted. Statistical analyses were also performed to assess the consistency and validity of the findings. The results demonstrated that the Enhanced A* Algorithm successfully avoided all dynamic obstacles and moving objects encountered along the path in all distinct test cases. In contrast to the traditional algorithm, which achieved an average obstacle avoidance rate of 8.33%, the enhanced algorithm consistently demonstrated a 100% average obstacle avoidance rate. The enhanced algorithm outperformed the traditional A* algorithm in generating a path in a complex environment by exhibiting optimal dynamic obstacle recognition and avoidance. The Enhanced A* Algorithm is subsequently applied to autonomous vehicle parking, following standard parking restriction laws.

This research examines quantitative approaches in flower dissection, with a focus on measuring symmetry and flower structure using mathematical formulas. This research aims to develop an objective method for analyzing flower morphology to support taxonomic, evolutionary and ecological studies of plants. Data was collected through morphometric measurements on various flower species with radial and bilateral symmetry characters. Parameters measured include petal length, corolla width, spreading angle, and number of reproductive parts. The analysis was carried out using geometric formulas to calculate the symmetry index and proportions of the flower structure. The research results show that flowers with radial symmetry have a higher symmetry index value than flowers with bilateral symmetry. Additionally, variations in flower structure were found to correlate with ecological adaptations, such as pollination strategies by insects or wind. This approach successfully revealed consistent mathematical patterns in floral design, providing new insights into the relationship between structure and biological function. This study shows that quantitative approaches can increase precision in the analysis of flower morphology, while providing a new tool for exploring the evolutionary dynamics of plants. This method can be applied to cross-disciplinary research involving morphology, genetics and plant ecology.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.

Cyclodextrin inclusion complexes have been widely used in the pharmaceutical industry to improve drug solubility and bioavailability. Analysis of these complexes using HPLC requires the development of appropriate and validated methods. To analyze and compare various HPLC methods used for the analysis of cyclodextrin inclusion complexes, and to identify challenges and trends in method development. A literature review was conducted of articles published in the last 10 years, focusing on HPLC methods for the analysis of cyclodextrin inclusion complexes. Parameters compared included column type, mobile phase composition, flow rate, and detection method. The majority of studies used C18 columns with varying lengths and particle sizes. The composition of the mobile phase varied, reflecting the diversity of properties of the complexes analyzed. The flow rate was generally 1.0 mL/min, with the exception of the HPLC/MS method. Detection methods included UV-Vis, PDA, fluorescence, and mass spectrometry. The main challenges included the dynamic equilibrium of the complexes and stability during analysis HPLC method development for the analysis of cyclodextrin inclusion complexes requires optimization of various parameters to overcome specific challenges. Future trends are towards the use of advanced technologies such as UHPLC and high-resolution mass detection.

The spread of infectious diseases has become a critical issue in public health, requiring effective mathematical models to understand and control their dynamics. This study aims to develop a mathematical model based on differential equations to analyze the transmission patterns of infectious diseases. By dividing the population into distinct compartments—susceptible, infected, and recovered—this model provides a framework to study disease progression. The methodology involves formulating a system of ordinary differential equations to represent interactions among these compartments, followed by numerical simulations to explore key parameters influencing disease spread. The findings reveal significant insights into the role of infection rate, recovery rate, and basic reproduction number in determining the outbreak's intensity and duration. These results highlight potential strategies for intervention, including vaccination and quarantine measures, to mitigate the impact of infectious diseases. The proposed model serves as a valuable tool for researchers and policymakers to predict and manage disease outbreaks, offering practical implications for public health planning.

This article discusses the importance of understanding heat convection in fluids in the context of physics and engineering. Using first order Simple Differential Equations (PDS), we can analyze the temperature distribution in a fluid over time and space in high detail. PDS allows modeling heat convection by considering parameters such as temperature differences and fluid flow velocity. Numerical methods are used to complete the PDS computationally, while data collection techniques through literature studies provide an in-depth understanding of relevant theories and previous findings. With the application of PDS and numerical methods, we can better understand and predict heat transfer in fluids, which has wide applications in engineering, biology, and physics. In conclusion, this article provides a comprehensive insight into the use of PDS in the analysis of heat convection in static and dynamic fluids, with a focus on mathematical and computational approaches to better understand this phenomenon.    

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

Eduwisata village is a form of development of a village that combines elements of tourism with education and learning. The establishment of eduwisata village aims to make people aware of the importance of culture and the environment. In addition, eduwisata village is used as a means of empowering the community's economy. In eduwisata village,there are communities that play a role in increasing the value of the production sector, UMKM, and the economy as well as the economic potential of the village itself. In the process of developing the eduwisata Village program, there is cooperation with various agencies such as local governments, educational institutions, local communities, and the private sector. This study uses the theory of Smelser and Swedberg (2005) suggests that economic sociology focuses more on sociological analysis of economic processes, such as the formation of prices (agreements) between actors with economic actors, the interaction between the economy and other institutions in society, and institutional dynamics and cultural parameters that become the foundation.  

This research aims to analyze ambient air quality in the city of Surabaya, with a focus on four main locations: industry, transportation, offices and residential areas during two different periods, the dry season and the rainy season in 2023. This analysis is important for assessing the influence of urbanization and industrial activity. to the environment and public health. The method used is descriptive and comparative observational, where measurements are made of main pollutant parameters such as SO2, CO, NO2, O3, Pb, NMHC, PM10, PM2.5, and TSP. Sampling was carried out twice at each location, in accordance with SNI 19-7119.6-2005 and PPRI No. 22 of 2021 standards. The research results showed that the concentrations of all pollutants were below the specified threshold, indicating the effectiveness of existing air control policies. However, there are differences in pollutant concentrations between locations and significant seasonal changes, requiring more dynamic and tailored pollution management strategies. The study's conclusions suggest the importance of continuous monitoring, increased green space, public education about pollution, as well as stricter policies to control pollutant emissions. This research provides valuable empirical data for policy makers to develop effective strategies for managing air quality and reducing health risks in Surabaya..

The stochastic SEIR model offers an innovative approach to understanding the spread of infectious diseases, particularly tuberculosis, in limited populations. This study adopts a stochastic model to capture random variability in individual interactions, often overlooked in deterministic models. The population is divided into four main categories: Susceptible (S), Exposed (E), Infected (I), and Recovered (R), with transitions between categories determined by probabilities based on epidemiological parameters. Through simulations, the model demonstrates its capability to depict more realistic patterns of disease spread, including fluctuations in case numbers and epidemic duration. The findings indicate that stochastic variability plays a crucial role in understanding the dynamics of tuberculosis transmission, especially in small populations or when the number of individual contacts is limited. The stochastic SEIR model can serve as an effective tool for policymakers to evaluate various intervention strategies, such as vaccination, transmission control, and treatment, as well as to design public health policies that are data-driven and adaptive to epidemiological uncertainties.

The digital transformation within the public sector has shifted from an optional advancement to a primary parameter for local government success in managing dynamic metropolitan areas. This research examines the acceleration of Smart Governance in Surabaya through the implementation of the mandatory non-cash parking policy on public roads. Using a qualitative approach with a descriptive-analytical design, the study explores how this transition redefine the relationship between the government, parking attendants, and citizens. Findings indicate that the policy effectively minimizes budget leakage and enhances fiscal transparency. The integration of digital payment systems has transformed traditional parking management into a data-driven service, fostering public trust through accountable financial tracking. Furthermore, the shift from cash to digital transactions has successfully professionalized the role of parking attendants within the urban ecosystem. However, success relies heavily on consistent infrastructure readiness and public literacy. The study concludes that Surabaya's non-cash parking model serves as a vital instrument for strengthening Regional Original Revenue (PAD) while modernizing urban governance. These implications suggest that digital integration is not merely a technical change but a fundamental shift in bureaucratic culture. This model provides a strategic framework for other Indonesian metropolitan cities aiming to implement similar digital-based public service innovations and sustainable smart city governance.