Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost

Sukarsa, I Made; Pinata, Ngakan Nyoman Pandika; Rusjayanthi, Ni Kadek Dwi; Wisswani, Ni Wayan

doi:10.18421/tem101-17

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Five XGBoost models with various properties, including lag, rolling window, mean encoding and mix, were made in this study. According to the findings, the mixed feature model has an accuracy of 97.54%, an MAE of 0.063, and a MAPE of 2.64% [10].…”

Section: Introductionmentioning

confidence: 87%

The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims

Sukarsa,

Dwi Rusjayanthi,

Utami

et al. 2023

LKJITI

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Traffic accidents are still significant contributors to a fairly high death. Denpasar’s resort police record every traffic accident in the form of a daily report. The stored data can generate valuable information to improve policies and propagate better traffic practices. This research utilizes the classification technique with the XGBoost, random forest algorithm, and SMOTE method. The study shows that the SMOTE technique can increase the model's accuracy. Using the classification method with the two algorithms produces factors that affect the severity of traffic accident victims with feature importance. The feature importance obtained using the XGBoost model by counting the weight value for testing using the original dataset, the dataset for the type of two-wheeled vehicle, and the dataset of the kind of vehicle other than two-wheeled indicate that the variables influencing the severity of victims in road accidents are the time of accident between 00.00-06.00, the type of vehicle motorcycle, the type of opponent vehicle truck and pickup car, the age of the driver between 16-25, sub-district road status and front – side type of accident.

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Section: Introductionmentioning

confidence: 87%

The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims

Sukarsa,

Dwi Rusjayanthi,

Utami

et al. 2023

LKJITI

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show abstract

“…Abbasi et al [15] conducted research on demand forecasting on electrical load using XGBoost and the predictions made by XGBoost have an accuracy of 97.21%. Sukarsa et al [16] performed demand forecasting on gourami fish supplies using XGBoost and produce a model with an accuracy of 97.54%. Because the accuracy of XGBoost is higher than other methods, XGBoost was chosen as the algorithm to perform demand forecasting in this research.…”

Section: Demand Forecasting Using Machine Learningmentioning

confidence: 99%

Predictive business intelligence dashboard for food and beverage business

Wikamulia

Isa

2023

Bulletin EEI

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This research was conducted to provide an example of predictive business intelligence (BI) dashboard implementation for the food and beverage business (businesses that sell fast-expired goods). This research was conducted using data from a bakery's transactional database. The data are used to perform demand forecasting using extreme gradient boosting (XGBoost), and recency, frequency, and monetary value (RFM) analysis using mini batch k-means (MBKM). The data are processed and displayed in a BI dashboard created using Microsoft Power BI. The XGBoost model created resulted in a root mean square error (RMSE) value of 0.188 and an R2 score of 0.931. The MBKM model created resulted in a Dunn index value of 0.4264, a silhouette score value of 0.4421, and a Davies-Bouldin index value of 0.8327. After the BI dashboard is evaluated by the end user using a questionnaire, the BI dashboard gets a final score of 4.77 out of 5. From the BI dashboard evaluation, it was concluded that the predictive BI dashboard succeeded in helping the analysis process in the bakery business by: accelerating the decision-making process, implementing a data-driven decision-making system, and helping businesses discover new insights.

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“…TFIDF (Term Frequency Inverse Document Frequency) for feature extraction, ANOVA (Analysis of Variance) for feature selection, and PCA (Principal Component Analysis) for feature dimension reduction were other techniques applied in this work. The XGBoost method has been studied in several studies related to classification, including building a milk source classification model (dairy farming) [10], diabetes prediction [11], traffic accidents prediction [12], gourami supply estimation [13], and landslide hazard mapping [14]. The utilization of the XGBoost method for text mining has been carried out in several studies, including the hybrid model development for Ukrainian language sentiment analysis [15], integrated technology analysis of patent data [16], classification of injury rates based on accident narrative data [17], and classification of proactive personality in social media users [18].…”

Section: Introductionmentioning

confidence: 99%

Text Classification System Using Text Mining with XGBoost Method

Rusjayanthi,

Sudana,

Trisna

2023

JIM

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Large data nowadays can be used for analysis; thus, it can obtain important/valuable knowledge in various domains. Text analysis can be carried out by utilizing text mining using computational methods so that knowledge extraction can be carried out on large text data, including processing related to unstructured text data, which is written in natural language. Classification in text mining is a type of work with the searching process for a set of models or functions that describe and differentiate text data classes with the aim that the model can be used to predict the class of an object (text data) whose class is unknown. Text mining was carried out in this research to analyze text data through the Text Classification System using a classification method, namely the XGBoost (eXtreme Gradient Boosting) Method. A text classification system was developed to classify text in the form of articles. The highest accuracy obtained from the test is 77%.

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Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost

Cited by 4 publications

References 16 publications

The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims

The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims

Predictive business intelligence dashboard for food and beverage business

Text Classification System Using Text Mining with XGBoost Method

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