Coefficient Of Determination Research Articles

Machine learning prediction of microhardness and corrosion current density of Ni-Co alloys produced by scanning jet electrodeposition

ABSTRACT Scanning jet electrodeposition (SJE) is a promising method for electrodeposition. Integrated with automation and machine learning, intelligent electrodeposition can be achieved. In this study, a machine learning method was used to develop the relationship between the process parameters and properties of Ni-Co alloys produced by SJE. To evaluate the various models, the coefficient of determination (R 2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated as performance metrics. The multivariate nonlinear regression (MNR) model shows a higher R 2, lower MAPE and RMSE values, and better performance in both the training and testing sets, indicating superior approximation and prediction accuracies for microhardness. A back-propagation artificial neural network (BP-ANN) shows stable R 2 value of closest to 1, and the lowest MAPE and RMSE for corrosion current density and presents an exceptional generalisation ability. Finally, under the present experimental conditions, the highest microhardness and corresponding process parameters were determined using the MNR model, and the lowest corrosion current density was optimised using the BP-ANN model.

Peran Fear of Negative Evaluation Terhadap Body Dissatisfaction pada Perempuan Emerging Adulthood

The aim of this study is to determine the role of fear of negative evaluation on body dissatisfaction in emerging adulthood women. Cluster sampling was used as a sampling technique with a total of 411 women aged 18 to 25 years in West Sumatra. The measurement instrument for fear of negative evaluation and body dissatisfaction used in this study is a questionnaire. The data from the questionnaire results were analyzed using a simple regression test. The results of a simple regression analysis showed a significance value of 0.000 (p < 0.05) which means that fear of negative evaluation plays a role in body dissatisfaction in the group of women in the emerging adulthood stage with a determination coefficient (R square) value of 0.230. This value means that fear of negative evaluation plays a role in 23% of body dissatisfaction in emerging adulthood women, while the remaining 77% is influenced by other factors

Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network

This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN) model. It was applied to 272 RAC samples under different conditions and compositions focusing on key parameters for CS prediction: water-to-cement ratio (WCR), water absorption (WA), recycled coarse aggregate (RCA) density, fine aggregate (FA) density, naturally occurring coarse aggregate (NCA) density and water-to-total material ratio (WTMR). These parameters were used to develop the ESGA-CFNN model which was then evaluated for its performance. To compare the ESGA-CFNN model, two other models were developed and compared: particle swarm optimization-based CFNN (PSO-CFNN) and artificial bee colony-based CFNN (ABC-CFNN). K-fold cross-validation was used during model development to prevent overfitting. Results showed that ESGA-CFNN model performed better with an RMSE (root-mean-squared error) of 1.144, R2 (determination coefficient) of 0.991 and a10-index of 1.000. ABC-CFNN model had an RMSE of 1.434, R2 of 0.987 and a10-index of 0.982 while PSO-CFNN had an RMSE of 1.561, R2 of 0.984 and a10-index of 0.982. Practical validation with 6 RAC samples confirmed the real world applicability of these models. The findings of this study showed that the proposed ESGA-CFNN model is important for quality control in RAC production and optimizing mix designs to achieve required compressive strength to meet standards and reduce cost and increase sustainability in concrete construction. This study introduces a novel hybrid approach combining ESGA-CFNN, PSO-CFNN, and ABC-CFNN algorithms for accurately predicting the compressive strength of RAC. These models outperform traditional methodologies by offering enhanced predictive accuracy and generalization capability, especially in complex, real-world datasets.

Predicting an opaque bubble layer during small-incision lenticule extraction surgery based on deep learning

AimThis study aimed to predict the formation of OBL during femtosecond laser SMILE surgery by employing deep learning technology.MethodsThis was a cross-sectional, retrospective study conducted at a university hospital. Surgical videos were randomly divided into a training (3,271 patches, 73.64%), validation (704 patches, 15.85%), and internal verification set (467 patches, 10.51%). An artificial intelligence (AI) model was developed using a SENet-based residual regression deep neural network. Model performance was assessed using the mean absolute error (EMA), Pearson’s correlation coefficient (r), and determination coefficient (R2).ResultsFour distinct types of deep neural network models were established. The modified deep residual neural network prediction model with channel attention built on the PyTorch framework demonstrated the best predictive performance. The predicted OBL area values correlated well with the Photoshop-based measurements (EMA = 0.253, r = 0.831, R2 = 0.676). The ResNet (EMA = 0.259, r = 0.798, R2 = 0.631) and Vgg19 models (EMA = 0.31, r = 0.758, R2 = 0.559) both displayed satisfactory predictive performance, while the U-net model (EMA = 0.605, r = 0.331, R2 = 0.171) performed poorest.ConclusionWe used a panoramic corneal image obtained before the SMILE laser scan to create a unique deep residual neural network prediction model to predict OBL formation during SMILE surgery. This model demonstrated exceptional predictive power, suggesting its clinical applicability across a broad field.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Analysis of natural and man-made factors of landslide development in the Carpathian region using GIS

Purpose. Determination of the dependencies of spatial relationships between landslides in the Carpathian region of Ukraine and the factors causing them, taking into account the differences in geomorphological, geological, climatic conditions and natural and geographical zoning according to the landscape principle. Methodology. The morphometric analysis of the territory was carried out using QGIS tools to determine the slope angles and exposure; linear characteristics were calculated: distances to the river, road, and houses. Based on the image interpretation data, we calculated the total annual rainfall and average annual air temperature. The impact of the factors considered on landslide formation was assessed using multivariate statistical analysis: factor analysis and linear multiple regression. Findings. Studies on the impact of natural and topographical conditions and anthropogenic activity on the development of landslide processes have been conducted. Orographic and climatic factors play the most important role. Construction of business facilities results in a decrease in the slope stability in 14–19 % of cases. The analysed independed factors showed good consistency between the distribution of existing landslides and the parameters under consideration: the share of the total variance of the factors under consideration is 71–76 %, the coefficient of determination of the regression model is 0.7. Based on the results of geoinformation and statistical analyses, it is proved that differences in geomorphological conditions and human activity (construction of roads, economic facilities) are dominant in the formation of landslides in different landscape zones. Originality. It is established that there is a dynamic coherence between the spatial change in natural conditions and human settlement and activity, which interact with each other to create prerequisites for the development of landslides. Practical value. This study is important from the point of view of understanding and mitigating the cause-and-effect relationship of landslide development for certain territories, and the results obtained can be useful in developing land use planning strategies, infrastructure development, and planning new construction works.

A goodness-of-fit measure for logistic regression under separation

Logistic regression models have a severe problem called separation. The maximum likelihood estimator does not exist in logistic regression models for data structures under separation. Under separation, the forcibly estimated maximum likelihood estimate may have an extremely large value. Separation often occurs when the size of dataset is small. Consequently, goodness-of-fit measures based on the likelihood ratio and those based on covariance functions using the maximum likelihood estimate indicate that the model is excessively good regardless of the cause of the separation. The Firth and exact logistic regression methods are valid estimation methods for separation problems. Therefore, we propose methods to reasonably evaluate the goodness-of-fit measures of statistical models under separation with dataset of a small sample size with the abovementioned methods. The goodness-of-fit measures based on covariance functions which are a generalization of the multiple correlation coefficient, referred to as the regression correlation coefficient and the entropy coefficient of determination are then used combined with the abovementioned methods for the separation data. In addition, we conducted a data analysis using the definition of the non separation ratio based on the regression depth.

Optimization of Starch-Based Bioplastic from Sweet Potato using Box-Behnken Design with Plasticizer and Filler for Reduced Water Absorption

Plastic is a ubiquitous material used in a wide range of applications, but its disposal has become a significant environmental concern. Bioplastics, which offer several advantages over conventional plastics, including a smaller carbon footprint, greater biodegradability, and adaptability, are being explored as a potential alternative. Among the bioplastics, starch-based polymers have gained significant attention due to their natural abundance, biocompatibility, low cost, and renewability. In this study, a starch-based bioplastic was developed using glycerol and sorbitol as plasticizers and wood dust as a filler through the casting method. Starch was extracted from sweet potato (Ipomoea batatas). The optimal conditions for preparing the bioplastic with minimum water adsorption were determined using a three-level Box-Behnken design. The amount of starch, plasticizer, and filler were the key parameters for this study. The percentage of water absorption was determined by immersing the prepared bioplastic in distilled water for 24 hours and weighing it. The result of the Fourier Transform Infrared (FTIR) spectroscopy of extracted starch showed a similar observation peak pattern when compared to the FTIR spectrum of commercial starch. The linear response model produced a good coefficient of determination (R2 = 0.8237), indicating that the chosen parameters implicitly affect the percentage of water absorption. The prediction percentage of water absorption after optimization is 20.069 with a desirability value of 0.806. This study shows that the response surface approach helps in determining the optimum conditions for the minimum percentage of water adsorption from starch-based bioplastic.

Kontribusi Social Support terhadap Problematic Internet Use pada Mahasiswa Universitas Negeri Padang

The development of information and communication technology, particularly the internet, has significantly benefited Indonesian society, with internet users reaching 95% by 2024. Although the internet facilitates communication and access to information, excessive use can lead to mental health issues, especially among students, with 15% experiencing problematic internet use. Students often rely on the internet for social interaction and emotional management, which can increase the risk of addiction. This study aims to investigate the contribution of social support to problematic internet use among students at Universitas Negeri Padang. The research adopts a quantitative approach with statistical analysis. The population measured in this study consists of students at Universitas Negeri Padang. The sample was taken using a proportionate stratified random sampling technique, appropriate for populations with non-homogeneous and stratified members. A total of 395 students participated in the study. The instruments used were the GPIUS2 scale and the MSPSS scale, developed using the Likert model. The data obtained were analyzed using multiple regression analysis. The results show a coefficient of determination (R²) of 0.220, indicating that the social support variable contributes 22% to the problematic internet use variable.

Energy Consumption and Economic Growth: Evidence from SADC Countries

This article examined the nexus between energy consumption and economic growth in selected Southern African Development Community countries for the period 1980 to 2015. The study used both simple and multiple linear regression models to examine the relationship between the variables. The statistical results of the simple linear regression model between energy use and economic growth show a correlation coefficient of 0.060. This represents a weak positive correlation between the model variables. A coefficient of determination of 0.00476 depicts that energy use explains only 0.476% of the variability in the gross domestic product of countries investigated. The multiple linear regression model results show a multiple R of 0.3684, and an R2 of 0.1358. This also depicts a weak positive correlation between the model variables. A coefficient of determination of 0.1358 means that energy use and electric power consumption accounts for only 13.58% of the values of the gross domestic product of selected Southern African Development Community countries and the balance is explained by the terms that are not specified in the model. This article is of value to policy makers in the region as well as the academia as it strives to close the gap in knowledge.

The Effect of Cooperative Learning Model Type Think Pair Share (TPS) on Student Learning Outcomes

This study aims to examine the effect of the Think Pair Share (TPS) Cooperative Learning Model on the learning outcomes of Class X students in the Accounting Expertise program at SMK Negeri 1 Gowa. The research employs an experimental design with a posttest-only control group, involving two groups: an experimental group utilizing the TPS model and a control group receiving conventional instruction. The sample consists of 68 students, divided equally between the two groups, selected through purposive sampling. Data collection techniques include observation, documentation, and tests, while data analysis is performed using SPSS 27.0 for Windows, encompassing descriptive statistics, validity tests, and hypothesis testing. The findings indicate that the TPS Cooperative Learning Model was implemented effectively, with a high average score of 91.2% in the experimental group. Descriptive analysis reveals that the learning outcomes in the experimental group averaged 86.24, significantly higher than the control group’s average of 61.76. Regression analysis shows a significant positive effect of the TPS model on learning outcomes, with a regression equation of Y' = 40.312 + 0.499X and a determination coefficient of 44.1%, indicating that the TPS model accounts for 44.1% of the variance in learning outcomes. The t-test results confirm that the TPS model significantly enhances learning outcomes (p < 0.05). These results align with previous studies and support the theoretical framework that cooperative learning models, particularly TPS, enhance student engagement and academic performance.

The Effect of Tourist Attractions on the Progress of the Sipinsur Geosite Tourism Object in Pearung Tourism Village, Humbang Hasundutan Regency in 2024

This study aims to determine the positive and significant influence of tourist attractions (x) on the progress of tourist attractions (y) Geosite Sipinsur in Pearung Tourism Village, Humbang Hasundutan Regency in 2024. This research uses a quantitative approach by distributing questionnaires to 96 respondents by accidential sampling. To determine the answer score on the questionnaire which will be tested for validity and reliability using a Likert scale. The data analysis technique used is descriptive statistical analysis, Pearson Product Moment correlation, determination coefficient test and simple linear regression analysis. Based on the results of descriptive statistical analysis, the influence of tourist attractions on the progress of tourist attractions is in a good category, with an average of 4.5449 at the interval of the category very advanced. The test results of the correlation coefficient (r) = 0.586 with a sufficient category and compared with r table = 0.200, where r count> r table (0.586> 0.200) it can be said to be significant and the hypothesis is accepted.

Advancements in renewable energy: Achieving milder reaction conditions in biodiesel synthesis from green seed canola oil with pristine ZIF-8

This study explores the enhancement of reaction conditions for biodiesel production from green seed canola oil through the methanolysis reaction using pristine ZIF-8 as a catalyst. The research focused on the effects of varying the methanol-to-oil molar ratio, temperature, and reaction time as well as their combined effects on biodiesel yield. To analyze these factors, Response Surface Methodology (RSM) paired with Central Composite Design (CCD) was employed. The quadratic model was identified as the best fit for the experimental data, evidenced by a high determination coefficient (R²) of 0.99, with all model parameters proving significant. A comprehensive characterization of the catalyst was conducted using various techniques. The surface and pore properties were examined using N2 adsorption-desorption measurements. X-ray diffractometry (XRD) was employed for structural analysis, while X-ray photoelectron spectroscopy (XPS) presented the binding information of the sample. Fourier transform infrared spectroscopy (FT-IR) provided insights into the functional groups present. Thermal gravimetric analysis (TGA) assessed the thermal stability of the catalyst. Results revealed that ZIF-8 significantly improved biodiesel production, with optimal conditions identified at a reaction temperature of 160 °C, a duration of 2.5 h, and methanol to oil (M/O) molar ratio of 26, resulting in a biodiesel yield of 85 %. This yield is close to the predicted value, demonstrating the reliability of the developed model. The kinetic analysis was performed under optimal reaction conditions. From this, the activation energy (Ea) and the pre-exponential factor (A) were obtained to be 14.5 kJ/mol and 3.4×10−7 min−1, respectively. The use of pristine ZIF-8 in biodiesel production from a low-quality and cheap oil offers a more efficient and milder reaction conditions, with potential for significant reductions in production costs and environmental impact.

Pengaruh Tenaga Kerja dan Investasi terhadap Pertumbuhan Ekonomi di Provinsi Jambi

The aims of the study were to determine: (1) the effect of labor on economic growth in Jambi Province; (2) the effect of investment on economic growth in Jambi Province, and (3)tThe effect of labor and investment on economic growth in Jambi Province. The type of data used in this study is published secondary data, namely in the form of time series for 14 years in Jambi Province from 2010-2023. The source of the data obtained is from the Central Bureau of Statistics (BPS) Jambi Province. This study uses multiple linier regression analysis. Data analysis prerequisite test used normality test, multicollinearity test, autocorrelation test and heteroscedasticity test. The hypothesis test used is the coefficient of determination R square, the F test and the t test. The results of this study indicate that partially: (1) labor has a significant positive effect on economic growth in Jambi Province; (2) investment has a significant positive effect on economic growth in Jambi Province; and (3) simultaneously labor and investment have a significant effect on economic growth in Jambi Province.

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

Time series forecasting of rain-induced attenuation using coupled ARIMA-SARIMA models for radio propagation applications over subtropical locations

This present research aimed to identify the optimum model for predicting the rain-induced attenuation along the terrestrial and satellite communication networks and employed the time series forecasting models of Autoregressive integrated moving average (ARIMA) and Seasonal Autoregressive integrated moving average (SARIMA) to examine and contrast several techniques for predicting rain-induced attenuation across a sub-tropical area of South Africa. In this research, the ARIMA was developed using the Box and Jenkins technique to predict long-term rain-induced attenuation in the following South African provinces: Kwazulu-Natal, Eastern Cape, Gauteng, and Northern Cape. The datasets used for the research were obtained from the South African Weather Station from 1994–2023 were used to build and check the model after generating rain-induced attenuation using synthetic storm techniques (SST). The forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model and that of autoregressive integrated moving average (ARIMA) were compared with four forecast performance measures: - Mean Squared Error (MSE), Mean Absolute Error (MAE), R- squared or the coefficient of determination (R2) and Root Mean Squared Error (RMSE). The results of the study showed that due to its lower forecast performance error indicators, SARIMA performed better than ARIMA in forecasting rain-induced attenuation in South Africa. There is no statistically significant difference between the two projected values, according to the results of a Ljung-box test for significant difference. The authors draw the inference that the two approaches can effectively be employed in their proper locations. Rain-attenuation prediction data indicates that this long-term projection can help decision makers to improve the performance of microwave networks in the face of random fluctuations and avoiding unexpected signal loss with efficient installation of communications infrastructure. It also has applications in prediction of floods, urban planning, and crop management.

New multivariate models for the estimation of earth’s albedo and its variation with meteorological parameters over Kano, Nigeria

The study of what happens to sunlight as it goes through the atmosphere is vital to many fields of science because over time, reflection, scattering, and absorption in the atmosphere have changed the amount of solar radiation that reaches the Earth's surface. This study examined the variation in albedo over Kano, situated in the Sahelian climatic zone of Nigeria. The study's data, which covered the years 1984–2022, totaled thirty-nine, was obtained from the National Aeronautics and Space Administration (NASA). Utilizing monthly average daily measured meteorological data on global solar radiation, wind speed, mean temperature, surface pressure, and relative humidity, eleven models of three categories based on two, three, and four variable regression models were developed. Five validation indicators were used to verify the regression models: the t-test, coefficient of determination (R2), mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE). The developed models that were considered most appropriate for estimating surface albedo were ranked; the three-variable regression model ALB7, which included the wind speed, relative humidity, and mean temperature, outperformed the previously employed generalized method from literature in estimating surface albedo over Kano. The variation of measured albedo with meteorological parameters depicts a direct relationship for clearness index and wind speed; an inverse relationship was observed for surface pressure. Mean temperature and surface pressure exhibit an inverse relationship during the wet season and vice versa for the dry season.

Comparative Analysis of Machine Learning Models for Weather Forecasting: A Heathrow Case Study

Accurate weather forecasting, especially temperature prediction, is fundamental for various segments within the UK, including agriculture, energy, and policy planning, as the nation adapts to the effects of climate change. This study addresses the limitations of conventional linear models in capturing the complex, non-linear relationships within meteorological data by comparing the effectiveness of different Machine Learning (ML) strategies. This study evaluates the performance of baseline ML models, such as Linear Regression (LR), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN). It also examines advanced ensemble and boosting models such as Decision Tree (DT), Random Forest (RF), XGBoost (XGB), LightGBM (LGBM), and CatBoost, using a comprehensive dataset from Heathrow Airport. Detailed preprocessing, model training, and optimization through cross-validation were conducted, with performance assessed using Mean Squared Error (MSE) and Coefficient of Determination (R²) metrics. The results demonstrate that ensemble methods, particularly XGB and LGBM, offer superior predictive accuracy for weather forecasting tasks, highlighting their potential to enhance predictive models in meteorological applications.

Pengaruh Konformitas terhadap Perilaku Konsumtif pada Mahasiswa Konsumen Coffee Shop di Kota Padang

This study aims to see the influence of conformity on consumer behavior among student coffee shop consumers in Padang City. This research uses quantitative methods. The population in this study were students as coffee shop consumers in Padang City with a sampling technique using purposive sampling. The sample in this study amounted to 105 people. The instruments used in this study were the conformity scale and the consumer behavior scale. The results of the analysis using simple linear regression with a significance of p = 0.018 (p <0.05), correlation (R) of 0.231 and the coefficient of determination (R Square) of 0.053. This means that conformity has a significant positive influence on consumer behavior with an influence of 5.3%.