C5.0 Algorithm and Synthetic Minority Oversampling Technique (SMOTE) for Rainfall Forecasting in Bandung Regency

Kurniawan, Erwin; Nhita, Fhira; Aditsania, Annisa; Saepudin, Deni

doi:10.1109/icoict.2019.8835324

Cited by 10 publications

(10 citation statements)

References 14 publications

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Section: Imbalanced Data and Smote Methodsmentioning

confidence: 99%

“…Classification problems arise when the class being represented has an unbalanced number, this problem is known as an Imbalanced Dataset [4]. There are several ways to overcome unbalanced data, one of which is to apply the SMOTE technique [4]. SMOTE or also known as the Synthetic Minority Over-Sampling Technique is one of the oversampling approaches used to overcome imbalanced data types.…”

Section: Imbalanced Data and Smote Methodsmentioning

confidence: 99%

“…To reduce the impact and damage, the community hopes for a system that can predict and analyze rainfall patterns based on historical data. One approach that can be used to analyze and predict is data mining [4]. However, the prediction results are not 100% accurate because each location, geography, and topography have different meteorological data.…”

Section: Introductionmentioning

confidence: 99%

“…Rainfall classification has been carried out by several researchers [4], [5], [6], and [7] using individual machine learning methods. In research [4] applying the C5.0 algorithm using k-fold cross-validation and obtaining the highest accuracy of 92% on imbalanced data, while applying the smote technique the accuracy increased by 99%. Research [5] uses the PCA algorithm Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting. The data used is Bandung City rainfall data for the years 2005 until 2021. The hybrid method is classified as an ensemble, which means combining several individual classification models to improve the performance of the built model. Voting algorithm has weaknesses in imbalanced data, while stacking does not. The results show that by combining five machine learning methods on an imbalanced dataset, the Stacking algorithm obtains an accuracy value of 99.60%. Meanwhile, with the addition of the SMOTE technique, the accuracy increases to 99.71%. This is supported by the performance of the Stacking method which is superior because it takes the best classification value for each individual model and can overcome the imbalance. Model evaluation does not only focus on accuracy, but also precision, recall, and f1-score. The contribution of this research is to provide information about the best Hybrid method between Voting and Stacking in obtaining model performance results on rainfall classification.

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Section: Imbalanced Data and Smote Methodsmentioning

confidence: 99%

Section: Imbalanced Data and Smote Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Imbalance data is the most common thing in sentiment analysis [32]. Data imbalance is a situation where the data ratio is not proportional or unbalanced, causing the performance of the model application to be ineffective [33]. Therefore, an oversampling technique is needed to balance the class distribution.…”

Section: Introductionmentioning

confidence: 99%

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Kristiyanti,

Sanjaya,

Tjokro

et al. 2024

IJ-AI

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<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

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Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

2020

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In recent years, due to the extensive use of the Internet, the number of networked computers has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude on computers by using not only known but also new attack-types, which are more sophisticated and harder to detect. To protect the computers from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don't contain up-to-date data. Additionally, they are imbalanced and cannot hold sufficient data for all types of attacks. These imbalanced and outdated datasets decrease the efficiency of current IDSs, especially for rarely encountered attack types. In this paper, we propose six machine-learning-based IDSs by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms. To implement a more realistic IDS, an up-to-date security dataset, CSE-CIC-IDS2018, is used instead of older and mostly worked datasets. The selected dataset is also imbalanced. Therefore, to increase the efficiency of the system depending on attack types and to decrease missed intrusions and false alarms, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling TEchnique (SMOTE). Data generation is performed for minor classes, and their numbers are increased to the average data size via this technique. Experimental results demonstrated that the proposed approach considerably increases the detection rate for rarely encountered intrusions.

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C5.0 Algorithm and Synthetic Minority Oversampling Technique (SMOTE) for Rainfall Forecasting in Bandung Regency

Cited by 10 publications

References 14 publications

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

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