Model Hibrid Genetic-Xgboost Dan Principal Component Analysis Pada Segmentasi Dan Peramalan Pasar

Siringoringo, Rimbun; Perangin-angin, Resianta; Jamaluddin, Jamaluddin

doi:10.46880/jmika.vol5no2.pp97-103

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Extreme Gradient Boosting or XGBoost is a decision tree or regression tree based enhancement algorithm [4]. Figure 1 shows an overview of the regression tree-based boosting algorithm.…”

Section: Literature Review Xgboostmentioning

confidence: 99%

Application of Classification Algorithm for Sales Prediction

Permana

Rosadi²,

Nikki³

2022

teknokom

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Increasing sales results is a desired target for all companies both at home and abroad. The company has a wide variety of products to offer. This paper (to fulfill a Business Intelligence course assignment) is the result of an experiment from data (keaggle) about consumer demand for products during the 2013-2015 period, then based on this data we try to predict to classify product sales, in order to make it easier for companies to classification for sales predictions. To find out the sales of the best-selling products, data mining classification techniques are used, namely XGBoost, Decision Tree, Random Forest, Linear Regression, and Nave Bayes. Based on the test results of the five classification techniques, the XGBoost model is the best with the data training value producing an RMSE value of 0.68% and data testing of 0.79%. This method is also better than the results of previous studies.

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“…Extreme Gradient Boosting or XGBoost is a decision tree or regression tree based enhancement algorithm [4]. Figure 1 shows an overview of the regression tree-based boosting algorithm.…”

Section: Literature Review Xgboostmentioning

confidence: 99%

Application of Classification Algorithm for Sales Prediction

Permana

Rosadi²,

Nikki³

2022

teknokom

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“…Dari langkah tersebut, nilai error dapat direduksi dengan efektif. Berikut adalah stuktur dari XGBoost [10].…”

Section: Xgboostunclassified

Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search

Ubaidillah

Muliadi

Nugrahadi

et al. 2022

mib

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Liver disease is a disorder of liver function caused by infection with viruses, bacteria or other toxic substances so that the liver cannot function properly. This liver disease needs to be diagnosed early using a classification algorithm. By using the Indian liver patient dataset, predictions can be made using a classification algorithm to determine whether or not patients have liver disease. However, this dataset has a problem where there is an imbalance of data between patients with liver disease and those without, so it can reduce the performance of the prediction model because it tends to produce non-specific predictions. In this study, classification uses the XGBoost method which is then added with SMOTE to overcome class imbalances in the dataset and/or combined with Bayesian search hyperparameter tuning so that the resulting model performance is better. From the research, the results obtained from the XGBoost model get an AUC value of 0.618, for the XGBoost model with Bayesian search the AUC value is 0.658, then for the XGBoost SMOTE model the AUC value is 0.716, then for the XGBoost SMOTE model with Bayesian search the AUC value is 0.767. From the comparison of the four models, XGBoost SMOTE with Bayesian search obtained the highest AUC results and has an AUC difference of 0.149 compared to the XGBoost model without SMOTE and Bayesian search.

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An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

Deng,

Zhu,

et al. 2024

Expert Systems with Applications

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Model Hibrid Genetic-Xgboost Dan Principal Component Analysis Pada Segmentasi Dan Peramalan Pasar

Cited by 4 publications

References 8 publications

Application of Classification Algorithm for Sales Prediction

Application of Classification Algorithm for Sales Prediction

Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

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