Analysis of Stemming Influence on Indonesian Tweet Classification

Hidayatullah, Ahmad Fathan; Ratnasari, Chanifah Indah; Wisnugroho, Satrio

doi:10.12928/telkomnika.v14i2.3113

Cited by 30 publications

(22 citation statements)

References 10 publications

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“…Complaints from users will be automatically classified into predefined categories, which are "Akademik (Academic)," "Kegiatan (Activity)," "Fasilitas (Facility)," "BEM (Student Board)," and "Lainnya (Other)," using Naive Bayes Classifier algorithm. Naïve Bayes itself is a probabilistic learning method to classify data, which is popularly used in machine learning and data mining researches [17,18]. The testing stage is performed using the USE Questionnaire with a seven-level Likert scale to measure the usability variables of the application.…”

Section: Methodsmentioning

confidence: 99%

UMN E-Complaint Application with Automatic Classification Feature

Ferdina,

Hansun,

Kristanda

2019

IJEAT

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In university, students’ complaints about the services provided are essential things to note because it could lead to higher student transfer if not appropriately handled. In Universitas Multimedia Nusantara (UMN), students can express their complaints through Dewan Keluarga Besar Mahasiswa (DKBM) UMN. By technology development, an online complaint submission system can be applied at UMN. A method that can be used in supporting efficient complaints processing is by using automated classification system since it can save both time and human resources. UMN e-complaints application with automated complaints classification feature was built using the CodeIgniter framework. The evaluation of the e-complaint application was conducted by using the USE Questionnaire. The results show that both DKBM UMN and students agree that the application is useful, easy to use, easy to learn, and satisfying.

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Section: Methodsmentioning

confidence: 99%

UMN E-Complaint Application with Automatic Classification Feature

Ferdina,

Hansun,

Kristanda

2019

IJEAT

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“…Besides Arabic, other studies have studied stemmer impact in another language and dataset. Research by [24] conducted an Indonesian Tweet Classification using 2000 tweets divided into three datasets, i.e., a first dataset with 1500 tweets, second dataset with 1750 tweets, and third dataset with 2000 tweets. They classified the tweets into 2 classes, namely positive and negative tweets.…”

Section: Related Workmentioning

confidence: 99%

Stemming Impact Analysis on Indonesian Quran Translation and Their Tafsir Classification for Ontology Instances

Utomo

Suryana

Azmi

2020

IIUMEJ

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The current gap which appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and their Tafsir to develop ontology instances. The existing studies of stemming effect analysis performed in various languages, dataset, stemming method, cases, and classifier. However, there is a lack of literature that studies about stemming influence on instances classification for Quran ontology with different dataset, classifier, Quran translation, and their Tafsir on Indonesian. Based on this problem, our study aims to investigate and analyze the stemming impact on instances classification results using Indonesian Quran translation and their Tafsir as datasets with multiple supervised classifiers. Our classification framework consists of text pre-processing, feature extraction, and text classification stage. Sastrawi stemmer was used to perform stemming operation in text pre-processing stage. Based on our experiment results, it was found that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation owns the best classification performance, i.e., 70.75% for accuracy and 71.55% for precision in Indonesian Quran translation dataset on 20% test data size. While in 30% test data size, SVM and TF-IDF with stemming process own the best classification performance, i.e., 67.30% for accuracy and 68.10% for precision in Ministry of Religious Affairs Indonesia dataset. Furthermore, in this study, it was also discovered that the Backpropagation Neural Network has the most precision and accuracy reduction due to the negative impact of stemming operations.

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“…Preprocessing can also be used to reduce computational processes and feature space which can improve performance accuracy and classification. In the case of text classification, many preprocessing techniques can be used [12] [13]. Preprocessing techniques used in this study are as follows.…”

Section: Preprocessingmentioning

confidence: 99%

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Pradana¹,

Hayaty²

2019

KINETIK

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Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents.

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Analysis of Stemming Influence on Indonesian Tweet Classification

Abstract: Abstract

Cited by 30 publications

References 10 publications

UMN E-Complaint Application with Automatic Classification Feature

UMN E-Complaint Application with Automatic Classification Feature

Stemming Impact Analysis on Indonesian Quran Translation and Their Tafsir Classification for Ontology Instances

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

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