Mean Absolute Percentage Error Research Articles

Novel state of charge estimation method of containerized Lithium–Ion battery energy storage system based on deep learning

State of charge (SOC) is a critical indicator for lithium–ion battery energy storage system. However, model-driven SOC estimation is challenging due to the coupling of internal charging and discharging processes, ion diffusion, and chemical reactions in the electrode materials. These factors result in frequent changes in their internal characteristics. Therefore, we devise a novel deep learning SOC estimation method based on the attention mechanism (A) and convolutional neural networks-long short term memory (CNN–LSTM) network model. The CNN layers are employed to extract the critical features of the battery dataset. The LSTM layers are utilized to capture the long-term dependencies in the time series data. The attention mechanism can assign attention weights to different features within the output of the convolution layer. The A–CNN–LSTM method not only amplifies the positive influence of critical knowledge but also provides a global reference. The comparative experiments of three datasets and ablation study are carried out. The root mean square error, mean absolute error, and mean absolute percentage error obtained from the A–CNN–LSTM model decreased by 32.75 %, 45.88 %, and 53.36 %, respectively, compared with the best results obtained from the existing CNN, LSTM, gate recurrent unit (GRU), CNN–LSTM, and CNN–GRU models. The proposed model demonstrates greater stability and consistency than other models, underscoring its efficacy in SOC estimation.

Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNETICS™ validation study

BackgroundAquaculture, traditionally a form of biotechnology, has evolved to integrate innovative biotechnological applications, such as advanced feed formulations, aimed at improving the growth performance and health of farmed fish species. In the present study, the effects of feeding rainbow trout with novel feed formulations were investigated. Fish growth, gut and liver morphology, the concentration of fatty acids in the fillet, and volatile fatty acids in the gut were assessed. The study also validated scenarios from in vivo experiments using a nutrient-based model called FEEDNETICS™. This globally used model serves as a tool for data interpretation and decision support in the context of precision fish farming.MethodsAlternative protein and oil sources, including poultry by-product meal (PBM) and natural algae oil, were explored as sustainable replacements for fishmeal (FM) and fish oil (FO). A 90-day feeding trial was conducted using rainbow trout, comparing two isoproteic, isolipidic and isoenergetic diets. The control diet contained 15% FM, 5% PBM, and 8% FO, while the test diet replaced FM with 15% PBM and 5% feather meal hydrolysate (FMH), and fully substituted FO with VeraMaris® natural algae oil and rapeseed oil.ResultsPBM successfully replaced FM protein without negatively affecting feed intake, growth performance or feed utilization in trout. The combination of PBM and natural algae oil was well tolerated by the trout and showed no negative effects on gut health. A detailed analysis of fatty acids in the fillet revealed that PUFAs of the n3 and n6 series were significantly higher in the PBM group than in the FM group. Values of fatty acid-related health indexes, including atherogenicity index, and thrombogenicity index, confirmed the high nutritional value of trout filet, thus representing a healthy product for human. In addition, the predictions using the FEEDNETICS™ indicated that the tested novel alternative formulations are economically viable. The validation of the model for fish growth resulted in a Mean Absolute Percentage Error (MAPE) of 8%.ConclusionsThe FEEDNETICS™ application enhances our ability to optimize feeding strategies and improve production efficiency in the aquaculture industry. VeraMaris® algae oil and PBM could serve as viable and sustainable raw materials for fish feed, promoting environmentally friendly aquaculture practices.

Artificial Neural Network Modeling of the Removal of Methylene Blue Dye Using Magnetic Clays: An Environmentally Friendly Approach

In this study, the effectiveness of Fe3O4-based clay as a cost-effective material for removing methylene blue (MB) dye from aqueous solutions was evaluated. The structural properties of the clay and Fe3O4-based clay were analyzed using SEM, XRF, BET, XRD, FTIR, and TGA techniques. In this research, the effects of various aspects, such as adsorbent amount, contact time, solution pH, adsorption temperature, and initial dye concentration, on the adsorption of Fe3O4-based clay are investigated. The experiments aimed at understanding the adsorption mechanism of Fe3O4-based clay have shown that the adsorption kinetics are accurately described by the pseudo-second order kinetic model, while the equilibrium data are well represented by the Langmuir isotherm model. The maximum adsorption capacity (qm) was calculated as 52.63 mg/g at 25 °C, 53.48 mg/g at 30 °C, and 54.64 mg/g at 35 °C. All variables affecting the MB adsorption process were systematically optimized in a controlled experimental framework. The effectiveness of the artificial neural network (ANN) model was refined by modifying variables such as the quantity of neurons in the latent layer, the number of inputs, and the learning rate. The model’s accuracy was assessed using the mean absolute percentage error (MAPE) for the removal and adsorption percentage output parameters. The coefficient of determination (R2) values for the dyestuff training, validation, and test sets were found to be 99.40%, 92.25%, and 96.30%, respectively. The ANN model demonstrated a mean squared error (MSE) of 0.614565 for the training data. For the validation dataset, the model recorded MSE values of 0.99406 for the training data, 0.92255 for the validation set, and 0.96302 for the test data. In conclusion, the examined Fe3O4-based clays offer potential as effective and cost-efficient adsorbents for purifying water containing MB dye in various industrial settings.

Accurate real-time modeling for multiple-blow forging

Numerical simulations are crucial for predicting outcomes in forging processes but often neglect dynamic interactions within forming tools and presses. This study proposes an approach for achieving accurate real-time prediction of forging outcomes. Initially, a simulation-based surrogate model is developed to replicate key process characteristics related to the billet, enabling prediction of geometry, deformation field, and forging load after an upsetting operation. Subsequently, this model is integrated with a mass-spring-damper model representing the behavior of forging machine and tools. This integration enables the prediction of blow efficiency and energy distribution after each blow, including plastic, elastic, damping, and frictional energy of the upsetting operation. The approach is validated by comparing predictions with experimental results. The coupled model outperformed Finite Element Method (FEM) predictions, exhibiting mean absolute errors (MAE) below 0.1 mm and mean absolute percentage errors (MAPE) below 1% in geometry predictions. Deformation field predictions showed errors below 0.05 mm/mm, and load-displacement curves closely matched experimental data. Blow efficiency predictions aligned well with experimental results, demonstrating a mean absolute error below 1.1%. The observed energy distribution correlated with literature findings, underscoring the model’s fidelity. The proposed methodology presents a promising approach for accurate real-time prediction of forging outcomes.

Radiometric calibration of UAV multispectral images under changing illumination conditions with a downwelling light sensor

AbstractUnmanned aerial vehicle (UAV)‐acquired multispectral images commonly suffer from radiometric inaccuracies due to changing illumination produced by intermittent cloud cover. This study addressed cloud shadow effects by integrating direct irradiance measurements from a downwelling light sensor into reflectance calculations. Two reflectance calibration methods were proposed as follows: (1) use of a single calibration reference panel for all images (Method 1) and (2) employment of a minimum distance classifier to assign color‐graded in‐field reflectance calibration targets to individual images based on their proximity in irradiance levels (Method 2). Images were acquired from 30 and 75 m flight altitudes with fixed‐exposure and auto‐exposure settings. Conventional photogrammetric calibration of UAV multispectral images inadequately addressed cloud shadows, resulting in orthomosaics with poor to moderate spatial uniformity (R2 = 0.70–0.92) and high mean absolute percentage error (MAPE = 13.52%–49.18%). The proposed calibration methods improved radiometric accuracy, yielding average R2 = 0.99 and 0.98 at 30 and 75 m images, respectively. Method 1 showed superior performance on the red‐edge and near infrared bands, and Method 2 worked best in the blue, green, and red bands. Post‐processing empirical line calibration of orthomosaics from Methods 1 and 2 reduced MAPE in reflectance estimation by at least 50% compared to conventional calibration. The clear sky reference histograms had lower Jensen–Shannon distance, higher Pearson's correlation, and improved intersection ratios with the histograms from the proposed methods, implying high similarity. Vegetation indices calculated with the proposed methods closely matched those from the reference orthomosaic, exhibiting significantly lower MAPE than conventionally calculated vegetation indices.

AI-enabled workflow for automated classification and analysis of feto-placental Doppler images.

Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors. To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurate analysis across varied healthcare settings. The classification of Doppler views was approached through three distinct blocks: (i) a Doppler velocity amplitude-based model with an accuracy of 94%, (ii) two Convolutional Neural Networks (CNN) with accuracies of 89.2% and 67.3%, and (iii) Doppler view- and dataset-dependent confidence models to detect misclassifications with an accuracy higher than 85%. The extraction of Doppler indices utilized Doppler-view dependent CNNs coupled with post-processing techniques. Results yielded a mean absolute percentage error of 6.1 ± 4.9% (n = 682), 1.8 ± 1.5% (n = 1,480), 4.7 ± 4.0% (n = 717), 3.5 ± 3.1% (n = 1,318) for the magnitude location of the systolic peak in LVIO, UA, AoI and MCA views, respectively. The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks

Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN). This model predicts spatial user demand for bikes between stations by constructing a spatial demand graph, accounting for geographical influences. For precision, ST-BDP integrates an attention-based graph convolutional network for station demand graph’s temporal-spatial features, and a sequential convolutional network for multi-source data (e.g., weather, time). In real dataset, experimental results show that ST-BDP has excellent performance with mean absolute error (MAE) = 1.62, mean absolute percentage error (MAPE) = 15.82%, symmetric mean absolute percentage error (SMAPE) = 16.14%, and root mean square error (RMSE) = 2.36, outperforming the baseline techniques. This highlights its predictive accuracy and potential to guide future bike-sharing policies.

Comparison of Holt Winter's and SARIMA Methods on the data of the Number of Foreign Tourist Visits in Bali Province

The tourism sector is a critical component of a country’s economy, including in Indonesia, where its impact is felt both nationally and regionally, such as in provinces and cities. This research focuses on Bali Province and aims to conduct a comparative analysis of the Holt-Winter’s and Seasonal Auto-regressive Integrated Moving Average (SARIMA) methods for forecasting foreign tourist arrivals. The analysis centers on two primary entry points: Ngurah-Rai Airport and the seaport. The primary objective is to forecast the number of foreign tourist arrivals from February 2024 to January 2025. The results indicate that the Holt-Winter’s model has a Mean Absolute Percentage Error (MAPE) of 5.2631%, which is lower than the MAPE of 5.8920% for the SARIMA model. Additionally, the Mean Absolute Error (MAE) for the Holt-Winter’s model is 19,149.18, compared to 20,883.20 for the SARIMA model. Consequently, this study concludes that the Holt-Winter’s model provides more accurate predictions and is closer to the actual values than the SARIMA model. Bali, Holt-Winter’s, forecasting, SARIMA, tourism.

Application of Data Mining to Measure the Level of Satisfaction with Public Facilities and Services at STMIK Kaputama Binjai Using Linear Regression Method

This study aims to analyze the level of satisfaction of STMIK Kaputama Binjai students with physical facilities (classrooms, laboratories, prayer rooms, wifi) and general services (administration, academic guidance, library, security, campus cleanliness) using multiple linear regression methods. Data were collected through questionnaires from students in the 2022/2023 academic year. The results showed that both variables have a significant effect on student satisfaction, with a regression coefficient of physical facilities of 0.40 and general services of 0.59, indicating that general services have a greater impact. Prediction of student satisfaction reached an accuracy level of 98% with a Mean Absolute Percentage Error (MAPE) value of 2%. Laboratory facilities and internet access (wifi) are the dominant factors affecting satisfaction. Based on these findings, improvements in both aspects are recommended to increase student satisfaction and institutional competitiveness.

Leveraging wearable sensors and machine learning for posture-based detection of carpal tunnel syndrome

Abstract Carpal tunnel is associated with long-term use of the wrist and hand for various activities such as typing, welding, or poor working postures. Carpal Tunnel Syndrome (CTS) may cause severe pain and discomfort in the hand and wrist, and in some circumstances, surgery becomes inevitable. The objective of this study is to prevent typing postures, which can be ascertained as predisposing subjects to CTS development. The data used in this study is an array of wrist wearable sensors to capture flexion, extension, and bending of fingers while using a keyboard or mouse. Machine learning is employed on the data in order to identify risk factors indicative of a high probability of CTS. The analyzed models are linear regression, Support Vector Machine, Random Forest, Multilayer Perceptron, Convolution Neural Network, and Long Short Term Memory. The conditions for assessing the performance of the data models include RMS error, coefficients of determination, and mean absolute percentage error. In this research, I conducted an exploratory data analysis (EDA) to gain an initial understanding of the dataset. Following the exploratory phase, I applied feature extraction techniques, specifically Principal Component Analysis (PCA). As put forward for the proposed research, the strategies to prevent risky occupations have broad potential at the present time, especially in the case of CTS when preventing repetitive wrist movements.

Estimation of Bullying Incidence Using Linear Regression Algorithm

Bullying is a violent act intended to cause harm or humiliation to another person. Bullying can take many different forms, including verbal and nonverbal, and it frequently targets people who are thought to be weaker or different. Nevertheless, because many bullying incidents go unreported, it is challenging to gather reliable statistics on the prevalence of bullying. In this study, the number of bullying cases in the field of education is estimated using a linear regression approach. This algorithm is used because it may estimate based on pertinent data, like the gender-based type of bullying and data on the quantity of bullying events that occurred in the preceding year. According to the study's findings, 2,250 bullying incidences are predicted for the upcoming year 2024, with a MAPE (Mean Absolute Percentage Error) of 0,07% or an accuracy level of 99,3%, categorized as highly accurate forecasting results.

Wavelet CNN‐LSTM time series forecasting of electricity power generation considering biomass thermal systems

AbstractThe use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

PREDICTION OF TIN EXPORTS, POPULATION, POVERTY, AND LABOR FORCE IN THE PROVINCE OF BANGKA BELITUNG ISLANDS

The COVID-19 virus has also caused shocks to the Bangka Belitung Islands Province in various sectors, especially the economy. To overcome this problem, of course the government has prepared responsive policies, both fiscal and monetary policies to prevent post-COVID-19 risks, especially in the economic recession. To prevent a post-COVID-19 economic recession, a prediction or time series forecast is needed on four variables that influence the economic recession, namely the number of tin exports, population, poverty and labor force in the Bangka Belitung Islands Province so that economic growth is maintained. This research aims to predict the four research variables by comparing the Moving Average and Exponential Smoothing methods. This research also uses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as indicators of model accuracy. Based on the results of the accuracy indicators of this model, it was found that the Exponential Smoothing method was better than the Moving Average method. The predicted results for the value of tin exports in 2024 are -3.3645811 with The RMSE value is 42293770, MAE is 29558091, and MAPE is 84.46131. The negative value in the tin export prediction means that the decline in the value of tin exports in 2024 will not have a significant effect because it is still within a reasonable figure. The total labor force in 2024 will be 11057.23 with RMSE value is 16536.48, MAE value is 14194.02, and MAPE is 112.8078. Then for population the predicted result is 21241.92 with RMSE is 19537.82, MAE is 11548.41, and MAPE is 37.51894. Then for the predicted results the number of poverty is 70.22749 with RMSE, MAE, and MAPE respectively of 3992.146, 3205.528, and 139.1129. The alpha value used is 0.0183.

A Case Study: Comparison of LSTM and GRU Methods for Forecasting Oil, Non-Oil, and Gas Export Values in Indonesia

This study explores the forecasting of Indonesia’s oil, non-oil, and gas export values, highlighting its critical role in supporting national economic growth. Given the inherent volatility in export values, accurate forecasting is vital for informed economic decision-making. The research employs Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, both well-regarded for their ability to handle sequential data and complex temporal patterns. Model performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The findings indicate that although both models produced nearly identical MAPE values of 99.99% across the oil, non-oil, and gas sectors, the GRU model outperformed the LSTM model with RMSE values of 0.0655 for oil and gas exports and 0.0697 for non-oil and gas exports. Moreover, the GRU model’s forecasts align closely with data from the Central Bureau of Statistics (BPS), which reported an 11.33% decline in Indonesia’s export values by the end of 2023. These results suggest that the GRU model not only offers greater accuracy but is also applicable to other economic forecasting contexts, such as exchange rate and inflation predictions, thereby enhancing economic policy-making.

ADDITIVE HOLT-WINTERS METHOD FOR FORECASTING GROSS REGIONAL DOMESTIC PRODUCT AT CONSTANT PRICES OF EXPENDITURE OF WEST SUMATRA

Regional disparities often pose significant challenges, with some areas experiencing rapid economic growth while others lag behind. An essential macro benchmark to gauge the success of development initiatives is economic growth, as indicated by changes in a region's Gross Regional Domestic Product (GRDP). Using GRDP at constant prices (GRDP CP) helps eliminate the impact of price fluctuations, focusing on real increases in production activities. Expenditure GRDP reflects the value of goods and services produced within a region and consumed by the community. In the case of West Sumatra, the GRDP CP expenditure data reveals simultaneous seasonal and trend elements. The seasonal pattern, occurring quarterly each year, exhibits an additive seasonal effect. The Additive Holt-Winters method has been proven effective for data containing seasonal patterns with constant seasonal variation (additive) and linear trends, where the level, trend, and seasonal pattern can change. The data used is secondary data of GRDP CP of expenditure quarterly of West Sumatra in 2010 - 2022 obtained from the official website of the Indonesian Central Bureau of Statistics. According to research findings, the GRDP CP expenditure with a Mean Absolute Percentage Error (MAPE) value of 1.03% for quarter I to quarter IV in 2023 are Rp46,284,010.59, Rp46,472,223.99, Rp47,512,197.79, and Rp48,445,184.94, respectively. This suggests that the model equation performs exceptionally well predicting future economic trends.

PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.

Nutritional composition analysis in food images: an innovative Swin Transformer approach.

Accurate recognition of nutritional components in food is crucial for dietary management and health monitoring. Current methods often rely on traditional chemical analysis techniques, which are time-consuming, require destructive sampling, and are not suitable for large-scale or real-time applications. Therefore, there is a pressing need for efficient, non-destructive, and accurate methods to identify and quantify nutrients in food. In this study, we propose a novel deep learning model that integrates EfficientNet, Swin Transformer, and Feature Pyramid Network (FPN) to enhance the accuracy and efficiency of food nutrient recognition. Our model combines the strengths of EfficientNet for feature extraction, Swin Transformer for capturing long-range dependencies, and FPN for multi-scale feature fusion. Experimental results demonstrate that our model significantly outperforms existing methods. On the Nutrition5k dataset, it achieves a Top-1 accuracy of 79.50% and a Mean Absolute Percentage Error (MAPE) for calorie prediction of 14.72%. On the ChinaMartFood109 dataset, the model achieves a Top-1 accuracy of 80.25% and a calorie MAPE of 15.21%. These results highlight the model's robustness and adaptability across diverse food images, providing a reliable and efficient tool for rapid, non-destructive nutrient detection. This advancement supports better dietary management and enhances the understanding of food nutrition, potentially leading to more effective health monitoring applications.

Optimizing sheep growth curves using a meta-heuristic algorithm.

Sheep were among the first animals domesticated by humans, and to this day, small ruminants are primarily raised for their meat, milk, and wool. This study evaluated the goodness of fit for growth curve models using observed age and weight data from crossbred lambs of various breeds based on the mean values between paired breeds. We employed a hybrid metaheuristic algorithm, combining a simulated annealing (SA) algorithm and a genetic algorithm (GA) called SAGAC, to determine the optimal parameter values for growth models, ensuring the best alignment between simulated and observed curves. The goodness of fit and model accuracy was assessed using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Errors were measured by comparing the criteria differences between simulated and observed data. Thirty crossbreed combinations were simulated, considering the average weight. Analysis of the observed and simulated growth curves indicated that specific crossbreeding scenarios produced promising results. This simulation approach is believed to assist geneticists in predicting potential crossbreeding outcomes, thereby saving time and financial resources in field research.

ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION

Climate change is a complex process that has far-reaching consequences for daily living. Temperature is one of the climatic features. Knowing its future value through a forecasting model is critical, as it aids in earlier strategic decision-making. Without considering spatial factors, this study investigates an Air Temperature variable forecasting. Weighted Fuzzy Time Series (WFTS) is one of the forecasting techniques. Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. Therefore, this research investigates the interval length and the Order length of the WFTS through the Particle Swarm Optimization (PSO) approach. The variable used is the air temperature in Malang, Indonesia. The dataset is taken from BMKG-Indonesia. The forecasting performance of classical WFTS is enhanced by setting an appropriate order level and employing Particle Swarm Optimization (PSO) to determine the optimal interval fuzzy length. As indicated by the Evaluation matrices in the result section, the proposed optimization overtaken the classical WFTS in term of accuracy. The evaluation indicates a Mean Absolute Percentage Error (MAPE) value of 1.25 and a Root Mean Square Error (RMSE) of 0.32 for the Proposed model. In contrast, the classical WFTS demonstrates a MAPE of 2.26 and RMSE of 0.58. The implementation of the PSO provides solid insights for Air temperature forecasting accuracy.