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Optimization problems in computer science often arise when a system must select the best combination from several alternatives under limited resources such as capacity, time, or cost. One commonly used optimization model is the Knapsack Problem, which involves selecting a number of items with specific weights and values to obtain the maximum profit without exceeding the available capacity. This study aims to analyze and compare the performance of the Greedy algorithm and Dynamic Programming in solving the 0–1 Knapsack Problem. The research employs a quantitative experimental approach by implementing both algorithms in a computer program and testing them on several datasets with different sizes. The evaluation parameters include the maximum value obtained and the algorithm execution time. The results show that the Greedy algorithm has faster execution time and more efficient memory usage, but it does not always produce an optimal solution. In contrast, the Dynamic Programming algorithm consistently produces an optimal solution, although it requires greater computational time. Therefore, the choice of algorithm should be adjusted to system requirements, whether prioritizing computational efficiency or optimal solution quality.

Sea Surface Temperature (SST) is an important parameter in oceanographic studies because it influences climate dynamics, ocean circulation, and marine ecosystems. Continuous monitoring of SST in open sea areas requires a reliable system capable of operating autonomously. This study develops a solar-powered ocean buoy designed to measure sea surface temperature while simultaneously evaluating the performance of a solar panel as the main energy source. The system uses a DS18B20 sensor to measure SST and an INA219 sensor to monitor the voltage, current, and power of the solar panel, while an ESP32 microcontroller functions as the central data processing unit. The results show that sea surface temperature tends to remain relatively stable with small daily variations, whereas the temperature and performance of the solar panel exhibit larger fluctuations due to direct exposure to solar radiation and changing weather conditions. Solar panel performance also shows significant variations in current and power depending on the intensity of sunlight. To analyze the influence of SST variations on solar panel performance, a statistical analysis using Analysis of Variance (ANOVA) was conducted. The ANOVA results, based on the calculated F-value and the significance value (p-value) at a confidence level of α = 0.05, indicate that SST variations have a significant effect on solar panel performance, demonstrating that the proposed solar-powered buoy system can operate autonomously and has potential for long-term SST monitoring in offshore areas.

Coastal areas are highly dynamic and increasingly exposed to physical pressures such as coastal erosion, shoreline change, inundation, and sea-level rise. In Indonesia, most coastal vulnerability studies remain focused on physical mapping and have not been systematically integrated with spatial planning evaluation. This study aims to analyze physical coastal vulnerability using the Coastal Vulnerability Index (CVI) and integrate the results with the Regional Spatial Plan (RTRW) in the coastal areas of Mangarabombang and Laikang Sub-districts, Takalar Regency. A quantitative spatial approach was applied using eight parameters, which were reclassified into vulnerability scores, transformed into CVI values, and classified using quartile methods. The results show that high and very high vulnerability classes dominate the coastal area. Spatial integration reveals that several development zones intersect with high vulnerability levels, indicating potential spatial mismatch. This study confirms that CVI can be operationalized as a spatial evaluation tool to support adaptive and risk-based coastal planning.

This research aims to analyze the influence of thermal conditions; specifically the temperature difference between the test object and the environment; on the characteristics of air flow and heat transfer around it. The object of this study is a test piece subjected to free air flow under various temperature conditions; focusing on the convection heat transfer phenomenon. The main problem addressed is how temperature variations affect the convection heat transfer coefficient; heat transfer rate; and heat flux; as well as changes in air velocity and pressure profiles. Therefore; the objective of this research is to quantitatively compare and assess these thermal and fluid parameters through an experimental study approach and Computational Fluid Dynamics (CFD) simulation. The methodology involves direct measurement of temperature and pressure parameters under low and high-temperature conditions; which are then processed to determine the convection coefficient (); heat transfer rate (); and heat flux (). The main findings indicate that at low-temperature conditions; the heat transfer coefficient () was found to be 53.26 ; the heat transfer rate () was 24.99 W; and the heat flux () was 537.87 ; with a pressure drop of 0.86 Pa. In conclusion; thermal conditions play a crucial role in determining the dynamics of air flow and the efficiency of heat transfer; the greater the temperature difference (); the higher the potential heat transfer rate; establishing a strong correlation between thermal conditions and the convection phenomenon.

Stock market volatility represents a key indicator of financial market uncertainty, particularly in emerging economies where market structures are still evolving and are highly sensitive to global shocks. This study aims to analyze and compare the volatility dynamics of stock markets in four Asian emerging economies: Indonesia, India, Malaysia, and Thailand. The research employs a quantitative approach using daily stock index data from January 2011 to January 2026 obtained from Yahoo Finance. Stock returns are calculated using logarithmic transformation and analyzed using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH(1,1)) model. Prior to model estimation, stationarity and ARCH effect tests are conducted to ensure the validity of volatility modeling. The empirical findings indicate that all return series exhibit non-normal distribution, strong volatility clustering, and significant ARCH effects. The estimation results show that both ARCH and GARCH parameters are statistically significant, with persistence levels close to unity across all markets, implying that volatility shocks tend to persist over a long period. These findings suggest that emerging stock markets in Asia are highly sensitive to external shocks and exhibit long-memory volatility behavior. The results provide important implications for investors and policymakers in designing effective risk management and market stabilization strategies.

separating and shredding organic and inorganic waste. This study aims to analyze the process of shredding household waste using the Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) and determine the optimal operational parameters based on variations in rotor speed. The research method uses numerical simulation with SolidWorks 2024 software for geometric modeling and Ansys Rocky 2023 R1 for CFD-DEM simulation. The rotor speed variations used are 1000 RPM, 2500 RPM, and 4000 RPM with a mass flow rate of 4 tons/hour and a simulation duration of 2 seconds. The parameters analyzed included particle mass flow rate, shredding characteristics, and power consumption. The simulation results showed that a speed of 1000 RPM produced the most optimal performance with a maximum capacity of ±4 tons/hour and a stable shredding response compared to other variations. At 2500 RPM, there were high fluctuations with low capacity (±0.6 tons/hour), while at 4000 RPM, the capacity was moderate (±1.1 tons/hour) but still did not exceed the performance of 1000 RPM. Based on these results, it can be concluded that a rotor speed parameter of 1000 RPM is the most effective condition for improving the grinding efficiency and production capacity of a hammer mill-type Depackaging machine based on CFD-DEM simulation.

Coffee husk is an agro-industrial waste with significant potential to be utilized as a renewable energy source through the fast pyrolysis process. This study aims to analyze and optimize gas production from the fast pyrolysis of coffee husk biomass using a screw reactor through single-particle-based Computational Fluid Dynamics (CFD) simulations. The simulations were conducted by varying the operating temperature at 500°C, 600°C, and 700°C to examine pressure distribution, heat transfer, particle temperature, and the formation of pyrolysis products, namely bio-oil, biogas, and biochar. The modeling was performed using COMSOL Multiphysics 6.2 with a numerical approach to represent thermal phenomena and biomass decomposition reactions during the pyrolysis process. The simulation results indicate that increasing temperature significantly affects the rate of heat transfer and the temperature distribution of coffee husk particles. At 600°C, heat transfer and temperature distribution are more uniform compared to 500°C, although heating at the particle core is not yet fully optimal. The pressure distribution shows a stable flow of pyrolysis gas from the bottom to the top of the reactor. In terms of products, increasing temperature leads to a reduction in biochar and bio-oil formation due to the occurrence of secondary reactions, while biogas production increases. The highest gas production is achieved at 700°C, indicating the most optimal condition for maximizing gas yield from fast pyrolysis. Therefore, single-particle-based CFD simulation can be used as an effective tool to understand pyrolysis mechanisms and optimize process parameters in a screw reactor.

The construction sector is an industry with a high level of work accident risk due to its dynamic and complex work characteristics. This study aims to examine the application of the Occupational Health and Safety (OHS) System and evaluate its effectiveness in mitigating risks at the Health Facility Building Construction Project (Project X) in Fakfak Regency. The research method used is a descriptive-analytical quantitative approach with purposive sampling of 25 respondents, including executors, supervisors, and field workers. Risk analysis was conducted using the Failure Modes and Effects Analysis (FMEA) method by measuring Severity, Occurrence, and Detection parameters to produce a Risk Priority Number (RPN). The results show that RPN values range from 52.35 to 452.30. The highest risk was found in the variable of limited safety signage in hazardous locations (RPN 452.30), which falls into the very high category. Additionally, 10 high-risk variables and 9 medium-risk variables were identified, dominated by technical, operational, and management factors, such as the use of heavy equipment by uncertified operators and weak implementation of Standard Operating Procedures (SOP) and OHS audits. This study concludes that the application of OHS in Project X is still reactive and requires strengthening risk-based safety management systems as well as improving workforce competence to achieve zero accident conditions.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Gas turbine generators play an important role in providing electrical energy, especially in the maritime sector, but they are vulnerable to disturbances such as overcurrent and undervoltage, which can cause equipment damage. This study aims to design and test an automatic protection system based on the ESP32 microcontroller with the INA219 sensor to detect current and voltage, as well as a relay as a circuit breaker. The method used is an experimental approach including static and dynamic testing, both with and without a 5W AC lamp load as a simulation of real loading conditions. Test results show that the average sensor reading error is 2.65% for current and 1.76% for voltage, which is still within the ±3% tolerance limit. The system is able to disconnect the load when any parameter exceeds the protection threshold, although there are slight inconsistencies in the relay response due to sensor reading fluctuations. In conclusion, this automatic protection system is proven to be 85% accurate and responsive in maintaining the operational reliability of the gas turbine generator, making it applicable as a preventive solution against electrical disturbances in marine environments.

This study aims to analyze the temperature distribution in an LPG-fueled chili drying machine using Computational Fluid Dynamics (CFD) simulation. The simulation was performed using SolidWorks Flow Simulation 2022 to investigate the effect of inlet air temperature and velocity on temperature uniformity inside the drying chamber. Three inlet temperature variations were applied: 60°C, 70°C, and 80°C, combined with two air velocities of 10 m/s and 11 m/s. The results showed that these parameters significantly influence temperature distribution. The optimum condition was achieved at 70°C and 10 m/s with a temperature uniformity efficiency (

This study aims to analyze the temperature distribution in an LPG-fueled chili drying machine using Computational Fluid Dynamics (CFD) simulation. The simulation was performed using SolidWorks Flow Simulation 2022 to investigate the effect of inlet air temperature and velocity on temperature uniformity inside the drying chamber. Three inlet temperature variations were applied: 60°C, 70°C, and 80°C, combined with two air velocities of 10 m/s and 11 m/s. The results showed that these parameters significantly influence temperature distribution. The optimum condition was achieved at 70°C and 10 m/s with a temperature uniformity efficiency (

The relatively high volatility of Robusta coffee prices creates uncertainty for farmers, business actors, and policymakers in making economic decisions. This study aims to analyze the price movement patterns of Robusta coffee, determine the most appropriate Autoregressive Integrated Moving Average (ARIMA) model, and conduct short- to medium-term price forecasting for Robusta coffee. The data used consist of monthly Robusta coffee price data from January 2023 to September 2025, sourced from the World Bank Commodity Price Data. The analytical method employed is ARIMA using EViews software, beginning with stationarity testing using the Augmented Dickey-Fuller (ADF) test, model identification through ACF and PACF, parameter estimation, and residual diagnostic testing. The results show that Robusta coffee price data are non-stationary at the level but become stationary at the first difference, indicating integration of order one I(1). Based on model identification and diagnostic testing, the ARIMA (0,1,0) model is found to be the most appropriate and satisfies the white noise assumption. Forecasting results indicate that Robusta coffee prices are projected to remain relatively stable with a moderate upward trend through December 2026. These findings are expected to serve as a reference for decision-making by farmers, business actors, and the government in responding to Robusta coffee price dynamics.

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.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Natural fibers continue to attract the interest of researchers to develop them as composite reinforcements in automotive and non-construction building interior applications. Basically, natural fiber-reinforced polymer composites are not suitable for applications exposed to heat. Investigating changes in mechanical properties due to temperature increases, this research is very important to conduct. The addition of filler (CaCO3) is known to improve the performance of natural fiber-reinforced polymer composites. This study investigates the physical, mechanical, and thermal properties of polyester composites reinforced with waru fibers with CaCO3 powder filler. The composites were fabricated using the hand lay-up method with a volume fraction of 30% waru fibers and CaCO3 powder with a volume fraction of 0-10%. The density of the polyester composite increased from 1.42 to 1.68 and 1.87 g/cm3 as the volume fraction CaCO3 0-10%. The results of dynamic mechanical analysis (DMA) testing of the polyester composite showed that parameters such as loss modulus, storage modulus, and tan delta also increased with increasing CaCO3 content. Thermogravimetric analysis (TGA) testing also showed increased thermal resistance after the addition of 5% (wt) (STL) and 10% (wt) (ZMB) with a residual combustion of 6.54% and 7.89% for each STL and ZMB composite, respectively. Compared to the TKO composite, it had the lowest combustion residue of 3.61%. Tensile strength and elastic modulus showed the same trend, namely an increase, while the elongation of the composite decreased with the addition of CaCO3 powder. The overall test results showed that polyester composites reinforced with CWf fibers and CaCO3 fillers were suitable for automotive and building interior applications.

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.

This study aims to analyze the effect of the combination of oil viscosity and shock load on the compression characteristics of telescopic shock absorbers on motorcycles, which include depth, time, and compression speed parameters. The experiment was carried out using two levels of oil viscosity, namely 10 cSt and 15 cSt (equivalent to SAE 10W and SAE 15W), as well as two shock load variations, namely 40 kg and 50 kg, which were dropped vertically from a height of 30 cm. Compression depth measurements were carried out using a slow-motion camera at a speed of 240 fps and the results were validated using Kinovea software. Meanwhile, the compression time measurement is carried out with a stopwatch, and the compression speed is calculated from the results of the comparison between the compression depth and the compression time. The experimental data were analyzed using Minitab software with the Two-Way ANOVA method to determine the influence of each variable on response. The results of the analysis showed that oil viscosity and shock load had a significant influence on the compression depth, while the viscosity of the oil also significantly affected the compression speed. However, neither the viscosity of the oil nor the shock load exerted a significant influence on the compression time. To support the results of the experiment, a mass-spring-damper system theory model approach was used, which represents the dynamic behavior of shock absorbers. This model helps to explain the compression response to changes in fluid and load parameters theoretically. Based on the analysis of experimental results and model simulations, the optimal combination that provides the best performance is found in the use of oil viscosity of 10 cSt (SAE 10W) with a shock load of 50 kg.

Enhancing the efficiency of renewable energy on ships is crucial for reducing dependency on fossil fuels. This research employs the Research and Development (R&D) method, aiming to design and implement a solar panel optimization system for battery charging, with a focus on increasing power efficiency and providing real-time performance monitoring. The system is designed using Maximum Power Point Tracking (MPPT) technology to maximize the solar panel's power output. A 200Wp solar panel with dimensions of 1290 x 760 x 30 mm was utilized. Static testing results show that the deployed sensors possess a high degree of accuracy, with an average error of 0.71% for the temperature sensor and only 1.81% for the light sensor used to monitor environmental conditions. Dynamic and system integration tests prove that the MPPT implementation significantly increases power output efficiency by 30.83% compared to a system without MPPT. Furthermore, the system with MPPT charges the battery approximately 27% faster. Additionally, the developed Modbus protocol-based monitoring system enables comprehensive and remote monitoring of key parameters such as voltage, current, temperature, and light intensity via a cloud database. Data communication reliability tests confirmed the system's capability to transmit entire data packets to a Google Sheets database at a periodic interval of 15 seconds without failure. Based on these results, the developed solar panel optimization system is feasible for implementation in maritime environments to enhance the utilization efficiency of renewable energy and the operational reliability of onboard systems.

This report presents a comprehensive overview of the crucial role of Proportional-Integral-Derivative (PID) control systems in maintaining an optimal environment for premature neonates in modern infant incubators. The main focus is directed on the effectiveness of the PID system in regulating vital physiological parameters such as temperature, humidity, and stability of the baby's body temperature, which greatly determine the survival and development of the neonate. The PID control system is proven to be able to provide a fast and accurate response to changes in environmental conditions, thus maintaining parameter stability in real-time. The integration of PID technology with modern incubator devices enables precise automatic setup, supports energy efficiency, and improves patient safety and comfort. The report also discusses various PID tuning methods, such as Ziegler-Nichols and adaptive methods, which are used to improve the performance of the system in the face of environmental dynamics and individual characteristics of infants. Implementation challenges identified include system complexity, need for periodic calibration, and limited technical resources in healthcare facilities. However, continuous innovations in control design and algorithms have driven the evolution of incubator devices to be more intelligent and responsive. Thus, the PID control system plays a central role in supporting neonatal life-support technologies, while representing significant advances in biomedical engineering and intensive care of premature infants.