Text mining based on tax comments as big data analysis using SVM and feature selection

Mihuandayani,; Utami, Ema; Luthfi, Emha Taufiq

doi:10.1109/icoiact.2018.8350743

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Menurut (Cahyaningrum et al, 2020) Twitter merupakan media sosial yang menjadi wadah penggunanya untuk saling berbagi respons atas suatu peristiwa melalui cuitan, pada penelitian nya cuitan tersebut diklasifikasikan menjadi sentimen positif, negatif dan netral. Penelitian tentang text mining yang bersumber dari komentar yang ditulis pada media social twitter dan facebook (Mihuandayani et al, 2018) pada penelitian tersebut performa diukur menggunakan parameter precision, recall, dan F-measure. Pada penelitian ini, twitter akan menjadi sumber data yang akan diproses untuk analisis sentimen.…”

Section: Pendahuluanunclassified

Analisis Sentimen Omnibus Law Pada Twitter Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization

Augustia

Taufan

Alkhalifi

et al. 2021

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Section: Pendahuluanunclassified

Analisis Sentimen Omnibus Law Pada Twitter Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization

Augustia

Taufan

Alkhalifi

et al. 2021

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“…In this research work, Information Gain (IG) was employed as a feature selection because it can reduce noisy features [15], [16]. IG can detect relevant features for classifiers.…”

Section: Information Gainmentioning

confidence: 99%

“…Information Gain (IG) is one of the most commonly used filter-based feature selection methods in machine learning. IG is a feature selection method to select relevant features and reduce noisy features [14]- [16]. Therefore, in this study, Information Gain (IG) is employed as a feature selection to improve the performance of the Naïve Bayes algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Improving Classification Performance on Credit Approval

Prastyo

Prasetyo

Arti

2021

IJID

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Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.

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State of Mental Health and Social Media: Analysis, Challenges, Advancements

Patil

Jain

Mohapatra

et al. 2022

Cognitive Intelligence and Big Data in Healthcare

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Text mining based on tax comments as big data analysis using SVM and feature selection

Cited by 9 publications

References 8 publications

Analisis Sentimen Omnibus Law Pada Twitter Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization

Analisis Sentimen Omnibus Law Pada Twitter Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization

A Machine Learning Framework for Improving Classification Performance on Credit Approval

State of Mental Health and Social Media: Analysis, Challenges, Advancements

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