High R-squared Values Research Articles

Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment

The transportation industry contributes significantly to climate change through carbon dioxide (CO2) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting CO2 emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government’s official open data portal, we explored the impact of various vehicle attributes on CO2 emissions. Our analysis reveals that not only do high-performance engines emit more pollutants, but fuel consumption under both city and highway conditions also contributes significantly to higher emissions. We identified skewed distributions in the number of vehicles produced by different manufacturers and trends in fuel consumption across fuel types. This study used deep learning techniques to construct a CO2 emission prediction model, specifically a light multilayer perceptron (MLP) architecture called CarbonMLP. The proposed model was optimized by hyperparameter tuning and achieved excellent performance metrics, such as a high R-squared value of 0.9938 and a low Mean Squared Error (MSE) of 0.0002. This study employs XAI approaches, particularly SHapley Additive exPlanations (SHAP), to improve the model interpretation ability and provide information about the importance of features. The findings of this study show that the proposed methodology accurately predicts CO2 emissions from vehicles. Additionally, the analysis suggests areas for further research, such as increasing the dataset, integrating additional pollutants, improving interpretability, and investigating real-world applications. Overall, this study contributes to the design of effective strategies for reducing vehicle CO2 emissions and promoting environmental sustainability.

Predictive analysis of fuel oil consumption in vessels: interpretable modelling with emphasis on load conditions

ABSTRACT Fuel oil consumption (FOC) in vessels is influenced by various factors, with vessel load conditions being a critical determinant. This research aimed to develop an interpretable regression-based machine learning model, specifically an XGBoost Regressor, to predict FOC, and leveraged the analysis with Explainable Artificial Intelligence (XAI) techniques to enhance transparency and understanding of the factors affecting FOC in maritime operations. Hyperparameter tuning optimized model parameters, achieving a high R-squared (R2) value of 0.99. XAI methods clarified how operational (e.g., speed, load, draft) and environmental factors (e.g., wind, wave, current, sea state) contribute to FOC increases. As a result, operational factors, notably average draft, exerted a substantial influence, with an average draft of 18.425 m resulting in a significant increase in predicted FOC to 1,729.40 kg/h during the laden condition, compared to the base value of 1,496.82 kg/h. Additionally, environmental factors, particularly Relative Wind Angle, significantly impacted FOC prediction, with a Mean Absolute SHAP Value of 11.97 kg/h, notably higher than in ballast (2.59 kg/h) and empty (2.85 kg/h) conditions. These findings emphasize the importance of tailored fuel efficiency strategies, as operational and environmental factors may vary in their impact across different loading conditions. ‘This article is a revised and expanded version of a paper entitled “Predictive Analysis of Fuel Oil Consumption in Vessels: Interpretable Modeling with Emphasis on Load Conditions” presented at The 11th International Conference on Logistics and Maritime Systems (LOGMS 2023) on 6 September 2023 at the Busan Port International Exhibition & Convention Center (BPEX)’

THE PREDICTION OF HYDROGEN EVOLUTION REACTION FROM DYNAMIC MAGNETIC FIELD ASSISTED WATER ELECTROLYSIS ARTIFICIAL NEURAL NETWORK

This study explores the prediction of Hydrogen Evolution Reaction (HER) performance in Dynamic Magnetic Field (DMF) assisted water electrolysis using Artificial Neural Networks (ANN). The integration of ANN models with experimental data from DMF-assisted electrolysis provides valuable insights into the complex interplay between magnetic fields and electrochemical processes. The results show significant enhancements in HER rates compared to conventional electrolysis, with static magnetic fields also contributing to performance improvements. The ANN models developed exhibit high accuracy in predicting HER performance under varying DMF rotational speeds, as evidenced by low Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and high R-squared values, demonstrating their strong predictive power and reliability. However, caution is advised regarding overfitting, and future research should focus on incorporating techniques like regularization and cross-validation to enhance model generalization. This study lays the foundation for further optimization of efficient hydrogen production technologies in the context of sustainable energy solutions.

Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques.

Under-5 mortality remains a critical social indicator of a country's development and economic sustainability, particularly in developing nations like Bangladesh. This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. By leveraging these models, the study aims to provide actionable insights for policymakers and health professionals to address persistent challenges. Data from the 1993-94 to 2017-18 Bangladesh Demographic and Health Survey (BDHS) was analyzed using advanced machine learning algorithms. Key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and Mean Absolute Percentage Error (MAPE), were employed to evaluate model performance. Additionally, k-fold cross-validation was conducted to ensure robust model evaluation. This study confirms a significant decline in under-5 mortality in Bangladesh over the study period, with machine learning models providing accurate predictions of future trends. Among the models, Linear Regression emerged as the most accurate, achieving the lowest MAE (4.05), RMSE (4.56), and MAPE (6.64%), along with the highest R-squared value (0.98). Projections indicate further reductions in under-5 mortality to 29.87 per 1,000 live births by 2030 and 26.21 by 2035. From 1994 to 2018, under-5 mortality in Bangladesh decreased by 76.72%. While the Linear Regression model demonstrated exceptional accuracy in forecasting trends, long-term predictions should be interpreted cautiously due to inherent uncertainties in socio-economic conditions. The forecasted rates fall short of the Sustainable Development Goal (SDG) target of 25 deaths per 1,000 live births by 2030, underscoring the need for intensified interventions in healthcare access and maternal health to achieve this target.

Data-driven approach to define mathematical model of a permanent-magnet synchronous motor used in small class electric vehicles

he development of cutting-edge motor technologies for electric vehicles (EVs) has been fueled by the global movement towards sustainable mobility. Because of its great efficiency, power density, and flexibility to operate at various speeds, Permanent-Magnet Synchronous Motors (PMSMs) have become the go-to option for small-class EVs. However, the accuracy and applicability of standard mathematical modeling techniques are limited because they frequently miss real-world nonlinearities and parameter fluctuations. In this paper, a data-driven methodology for PMSM modeling that is tailored for small-class EV applications is presented. The suggested method improves the precision, flexibility, and computational efficiency of PMSM models by utilizing real-world operational data. The efficiency map and peak torque characteristics of the WULING MACARON EV's electric drive were defined using regression analysis, which produced low residual errors and high R-squared values of 0.9647 and 0.9789, respectively. The outcomes show that the model can optimize energy use, forecast performance under various circumstances, and assist real-time control applications.

TAX REVENUE AND ITS IMPACT ON NIGERIA'S ECONOMIC GROWTH

This research examines the relationship between tax revenue components and economic growth in Nigeria, using quarterly data from 1994 to 2022. The research investigates three primary tax components: Petroleum Profit Tax (PPT), Value Added Tax (VAT), and Other Tax Revenue (OTH), employing an Autoregressive Distributed Lag (ARDL) model to analyze both short-run and long-run relationships. Data was sourced from Central Bank of Nigeria’s bulletins, National Bureau of Statistics, etc. The findings reveal a significant positive relationship between overall tax revenue and economic growth, with a 1% increase in total tax revenue leading to a 0.234% increase in GDP growth. Among the tax components, VAT demonstrates the strongest positive impact (0.289%), followed by Other Tax Revenue (0.178%) and PPT (0.156%). The error correction term (-0.567) indicates that approximately 56.7% of any disequilibrium is corrected within one year, suggesting efficient adjustment mechanisms. The study's diagnostic tests confirm the models' statistical validity, with high R-squared values and no significant issues of serial correlation or heteroskedasticity. Based on these findings, the study recommends enhancing VAT administration, expanding the tax base, modernizing tax collection systems, and implementing strategic reforms to diversify revenue sources beyond petroleum. These results have important implications for tax policy formulation in Nigeria, particularly in the context of economic diversification efforts and sustainable development goals.

Modelling and Forecasting Unemployment Trends in Kenya Using Advanced Machine Learning Techniques

This study conducts a comprehensive analysis exploring the relationship between key macroeconomic indicators and the unemployment rate, alongside evaluating the predictive accuracy of modern regression models. The correlation analysis examines the association between unemployment rate percentages and five macroeconomic variables: real Gross Domestic Product (GDP) growth, gross public debt as a percentage of GDP, population size, government revenue as a percentage of GDP, and government expenditure as a percentage of GDP. The results highlight significant correlations, particularly the strong positive relationship between unemployment rates and gross public debt (% of GDP) (0.8417), while real GDP growth shows a weak correlation (0.0783), indicating that debt levels may be a more crucial determinant of unemployment variations in this context. Additionally, a comparison of modern regression models, namely Support Vector Regression (SVR), Neural Network Regression, and Bayesian Regression, is conducted based on their performance metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Among the models, Support Vector Regression outperforms the others, with the lowest MAE (0.0823), RMSE (0.0878), and the highest R-Squared value (0.9915), along with notably favourable AIC (-100.2072) and BIC (-69.4268) scores. Neural Network Regression also delivers competitive performance with a slightly higher MAE and RMSE but a similarly strong R-Squared (0.9887). In contrast, Bayesian Regression exhibits weaker predictive power with higher error metrics (MAE = 0.2579, RMSE = 0.3109) and a significantly lower R-Squared (0.8806), AIC (28.0408), and BIC (36.8352). These findings underscore the efficacy of SVR in predictive modelling for macroeconomic datasets, suggesting its suitability for unemployment rate forecasting.

Machine Learning-Driven Optimization of Micro-Textured Surfaces for Enhanced Tribological Performance: A Comparative Analysis of Predictive Models

Micro-textured surfaces show promise in improving tribological properties, but predicting their performance remains challenging due to complex relationships between surface features and frictional behavior. This study evaluates five algorithms—linear regression, decision tree, gradient boosting, support vector machine, and neural network—for their ability to predict load-carrying capacity and friction force based on texture parameters including depth, side length, surface ratio, and shape. The neural network model demonstrated superior performance, achieving the lowest MAE (24.01) and highest R-squared value (0.99) for friction force prediction. The results highlight the potential of machine learning techniques to enhance the understanding and prediction of friction-reducing micro-textures, contributing to the development of more efficient and durable tribological systems in industrial applications.

Effective thermal conductivity of composites using homogenization

Composite materials are extensively used in engineering applications due to their customizable properties and high performance. Determining the equivalent homogenized properties of composites, such as thermal conductivity, is crucial for their effective use. Various theoretical, analytical, and experimental methods have been developed to assess these properties. This study investigates the effective thermal conductivity of composites using a deterministically based procedure for thermal analysis. This procedure accounts for the combined influences of the inclusions’ volume fractions, shapes, orientations, and locations within the matrix to determine the effective thermal conductivity of composites. The specific composite analyzed consists of a cubical PLA matrix with a single spherical or elliptical void inclusion with perfect interfaces. For that purpose, an analytical approach was developed, and MATLAB® code was created to calculate the effective thermal conductivity tensor. To benchmark the analytical results, comparisons were made against numerical finite element modeling (FEM) results conducted using ANSYS®; in addition, to previously reported analytical models from the literature. Corroboration was also obtained by comparing the results against experimental data from the literature. The accuracy of the proposed homogenization scheme was demonstrated by achieving a low mean absolute percentage error (MAPE) compared to FEM (2.88%) and to the experimental results (2.72% for void inclusion and 6.99% for filled inclusion). Additionally, a high R-squared (R2) value of 0.986 was achieved compared to FEM, and values of 0.97 and 0.998 were achieved compared to the experimental results for void and filled inclusions, respectively.

Predicting Wall Pressure Fluctuations on Aerospace Launchers Through Machine Learning Approaches

Artificial intelligence (AI) can be used to optimize the prediction of pressure fluctuations over the external surfaces of aerospace launchers and minimize the number of wind tunnel tests. In the present research, various machine learning (ML) techniques capable of predicting the acoustic load were tested and validated. The methods included decision trees, Gaussian Process Regression (GPR), Support Vector Machines (SVMs), artificial neural networks (ANNs), linear regression, and ensemble methods such as bagged and boosted trees. These algorithms were trained using experimental data from an extensive wind tunnel test campaign conducted to support the design of a VEGA (Advanced Generation European Vehicle) launcher vehicle and provide wall pressure fluctuations in many configurations. The main objective of this study was to identify, among several algorithms, the most suitable method able to process such complex databases efficiently and to provide reliable predictions. Different statistical indices, including the root mean square error (RMSE), the mean square error (MSE), and a correlation coefficient (R-squared), were employed to evaluate the performance of the ML methods. Among all the methods, the bagged tree algorithm outperformed the others, providing the most accurate predictions, with low RMSE and high R-squared values across all test cases. Other methods, such as the ANNs and GPR, exhibited higher errors, indicating their reduced suitability for this dataset. The results demonstrate that ensemble decision tree methods are highly effective in predicting acoustic loads, offering reliable predictions, even for configurations outside the training database. These findings support the application of ML-based models to optimize experimental campaigns and enhance the design of aerospace launch vehicles.

Development of petroleum-derived polymeric additive to enhance the bituminous properties with the use of a machine-learning model

Study revealed the synthesis of petroleum-derived polymeric additive PS-co-PAO. The synthesis of the additives was confirmed by FT-IR and NMR spectroscopy. The synthesized polymeric additives was blended with VG 10 base bitumen to modifiy its physicochemical and rheological properties. Six modified bitumens SOMB1,SOMB2, SOMB3, SOMB4, SOMB5, and SOMB6 were prepared using different concentrations of polymeric additives at 150°C and 170°C. These modified bitumens were further analyzed for the physicochemical and rheological properties which revealed that the SOMB-6 modified bitumen was found most suitable. Additionally, a Random Forest regression model was developed to predict the rutting resistance based on the percentage of polymeric additive and temperature. The model demonstrated a high R-squared value of 0.986, indicating that it can effectively predict rutting resistance, enhancing the design and testing of polymer-modified bitumen. A study on multifunctional additives also revealed that the prepared modified bitumens marginally meet the properties of higher-grade VG and modified bitumen as per IS and IRC specifications.

ENTREPRENEURIAL ORIENTATION AND SME SUCCESS: UNVEILING THE MEDIATING IMPACT OF AMBIDEXTERITY

This research seeks to assess how ambidexterity in exploration and exploitation mediates the relationship between entrepreneurial orientation and the performance of small and medium-sized enterprises (SMEs). Specifically, this study investigates the indirect influence of entrepreneurial orientation on business performance through the mediating roles of ambidexterity exploration and exploitation. A quantitative approach using structural equation modeling (SEM) is employed to examine the relationships among these variables. The research sample comprises 200 SMEs operating primarily in East Java, Indonesia. The findings demonstrate a significant mediating effect (p<0.05) of ambidexterity exploration and exploitation, which enhances the influence of entrepreneurial orientation on business performance. This indirect effect proves to be stronger than the direct impact of entrepreneurial orientation itself. The structural model shows a high R-square value of 68.6%, indicating a strong predictive capability. Additionally, the goodness-of-fit index reflects high model suitability (0.543 > 0.36), and the predictive power of the PLS model is considered medium. The study’s results underscore the importance of fostering both ambidexterity in exploration and exploitation alongside entrepreneurial orientation to optimize SME performance.

Microprudential Regulations and Bank Lending in Nigeria

Objectives: The current study aimed at investigating the relationship between microprudential regulations and bank lending in Nigeria. Theoretical framework: The theoretical underpinning of the study is drawn on the theory of risk-return tradeoff which maintains that potential return rises with an increase in risk. Method: Financial data of 13 banks over a period of 15 years were sourced from the repository of Central Bank of Nigeria, and were analyzed using the Panel (Least Squares) Multiple regression technique. Results and Discussions: The result of analyses revealed a high R-squared value of 0.75, an indication that microprudential regulation accounted for 75% variation in profitability in the Nigeria banking institution. Also, the F-statistic of 101.31 and a p-value of 0.000, indicated that the Ordinary Least Square regression model is statistically significant at the 1% level. Overall, microprudential regulation impact on lending and profitability in Nigeria. Research Implications: More so, the study’s relevance is evident in its applications to the principles and practice of lending, fund mobilization and other finance related policies and decisions in the banking sector. Originality/ Value: The outcome of the current study is valid and can be used for statistical inference. The conceptual framework of the study is a novel model that demonstrates how the variables in the study are interconnected. Furthermore, the model specification presents a viable template for financial speculations and calculations.

Optimizing prediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods

In machine learning, predicting the mechanical properties of stainless steel, such as Yield Strength (YS), Ultimate Tensile Strength (UTS), and Elongation (EL), requires many input variables, such as chemical composition, type of heat treatment, heating duration, and cooling method. However, the complexity and number of these variables can increase processing time and reduce model accuracy. This study aims to explore the impact of selecting the most influential input variables to improve prediction accuracy. We compared two feature selection techniques: Recursive Feature Elimination (RFE), which systematically removes less important features, and Information Gain (IG), which measures the contribution of each variable to the target prediction. Both techniques were implemented using the random forest algorithm, chosen for its robustness in handling large datasets and its ability to capture complex interactions between variables. Parameter optimization was performed using a grid search. The analysis showed that the RFE-based model outperformed both the IG-based model and the model without feature selection. In predicting YS, RFE identified 13 out of 21 influential variables, achieving a Mean Absolute Error (MAE) of 9.91, Root Mean Square Error (RMSE) of 14.20, and R-squared value of 0.89. For UTS, RFE identified 8 out of 21 variables, with an MAE of 12.89, RMSE of 16.97, and R-squared of 0.97. In predicting EL, RFE identified 14 out of 21 variables, with an MAE of 3.82, RMSE of 6.10, and an R-squared value of 0.85. The high R-squared values (0.85) across all properties indicate the model’s strong predictive capabilities, making it suitable for practical applications in predicting the mechanical properties of stainless steel.

Development of Predictive Models for Energy Demand and CO2 Emission Trends for Households in Makurdi Metropolis

This study aimed at developing predictive models for energy demand and CO2 emission trends for households in Makurdi metropolis. As part of the study, households were randomly selected to represent a reasonable spread. Four major emission sources: electricity; transportation; solid waste and cooking fuels were considered, and data on the sources were obtained using records from relevant departments as well as questionnaires, surveys, and interviews of the occupants. This data was analyzed and used to calculate the CO2 emissions of the households using IPCC standard guidelines and formulae. The CO2 emission and energy demand predictive models were also developed using Design Expert 13. The ANOVA for the CO2 emission and energy demand shows a significant model (Significance F value of ≤ 0.0001) and a high R-squared value of 0.9981 and 0.9802 respectively. This indicates the adequacy of the models, and they can therefore be used to predict the CO2 and energy demand of households in Makurdi metropolis. The limitation remains the imbalance in living standards across the respective areas of the metropolis which definitely impacts on the demand and use of energy. It will be useful for policy makers and implementers in ensuring sustainable energy demand planning and CO2 emissions mitigation.

Oman’s Green Horizon: Steering Towards Sustainability Through Decarbonization and Energy Transition

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman’s efforts towards sustainable development and decarbonization.

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

Image-based assessment of seismic damage in RC exterior beam-column joints

Seismic damage to reinforced concrete (RC) beam-column joints in reinforced concrete (RC) structures significantly impacts their stability and safety after an earthquake. Cracks caused by earthquakes weaken these joints, reducing their ductility, strength, and stiffness. Therefore, a systematic method for assessing damage in RC beam-column joints is crucial. This study introduces a new method that uses image analysis, along with ranking algorithms and machine learning, to assess seismic damage in RC exterior beam-column joints. The study identified three key indicators of severe damage in joints: drift ratio, strength, and stiffness. The method uses image analysis to extract features from preprocessed, binary images of cracks. These features capture both the texture and geometry of the cracks. The chosen features, including crack area, aspect ratio, and distribution of line widths, along with structural parameters like geometry and bond index, were selected using F-test and MRMR algorithms. Seismic damage assessments were conducted on 115 images from 37 specimens using four machine learning models (Regression Trees, Support Vector Machine, Gaussian Process Regression, and Gradient Boosting). The study achieved a high R-squared value of 0.80, indicating strong accuracy, for assessing seismic damage based on drift ratio using the image-based method. This demonstrates that image analysis techniques can effectively link surface crack patterns to quantifiable image features, leading to a more precise assessment of seismic damage.

PENERAPAN REGRESI LINEAR UNTUK PREDIKSI SUHU BADAN SAPI MENGGUNAKAN DATA SMART TERNAK DARI PT TELKOM INDONESIA: STUDI KASUS DI DESA PENGENGAT

Abstract West Nusa Tenggara (NTB) is one of the regions in Indonesia with significant potential in the field of livestock farming. In the context of the agricultural industry, livestock is considered a valuable asset, and monitoring their health is crucial for improving livestock productivity. This research aims to determine the relationship between the body temperature of cattle (Y) and environmental factors, namely air humidity (), environmental temperature (), and barometric pressure (), using smart livestock data provided by PT Telkom. The study was conducted in Pengengat Village, NTB, involving correlation and regression analysis. The analysis results indicate a significant positive correlation between the body temperature of cattle and environmental temperature () and a negative correlation with humidity (). Additionally, there is a strong positive correlation between the body temperature of cattle and barometric pressure (). The multiple linear regression equation obtained is as follows: = –26.816842418191094 + -0,0014707 + 0.39772325 + 0.04927779. Model testing shows excellent prediction quality with low Mean Squared Error (MSE) values (0.3849 for training and 0.4623 for testing). Furthermore, the high R-squared (R^2) values (0.7283 for training and 0.7110 for testing) indicate that the linear regression model can predict the body temperature of cattle based on the analyzed environmental factors. Keywords: Cattle Body Temperature, Smart Livestock Data, Prediction, Machine Learning, Linear Regression

Improve Metode Lightgbm untuk Prediksi Harga Mobil Bekas Menggunakan Hyper-Parameter Tuning

This study aims to predict used car prices using the LightGBM method and hyperparameter tuning techniques in the context of data science. The analysis process includes collecting historical data on used cars, preprocessing the data to clean and encode variables, and splitting the data into training and testing sets. The LightGBM model was trained and optimized through hyperparameter tuning using GridSearchCV to improve model performance. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The results indicate that the well-optimized LightGBM model can accurately predict used car prices with high accuracy. The low MSE value (35207938112.028404) and high R-squared value (0.9462871489515565) demonstrate the model's excellent predictive quality. This research provides deeper insights into the factors influencing used car prices and contributes to the development of effective and reliable predictive models.