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All Journal International Journal of Electrical and Computer Engineering Jurnal Masyarakat Informatika MATEMATIKA Jurnal INKOM Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran Sinergi International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Journal of Information System IT JOURNAL RESEARCH AND DEVELOPMENT
Retno Kusumaningrum
Universitas Diponegoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : International Journal of Advances in Intelligent Informatics

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Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning Retno Kusumaningrum; Iffa Zainan Nisa; Rizka Putri Nawangsari; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.737

Abstract

Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.
Temperament detection based on Twitter data: classical machine learning versus deep learning Annisa Ulizulfa; Retno Kusumaningrum; Khadijah Khadijah; Rismiyati Rismiyati
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.692

Abstract

Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007.
Co-Authors Adi Wibowo Aniati Murni Arymurthy Annisa Apriliani Annisa Ulizulfa Apriliyanto, Andi Bambang Subeno Helmie Arif Wibawa Hisar Maruli Manurung Iffa Zainan Nisa Juwanda, Farikhin Khadijah, Khadijah Manurung, Hisar Maruli Prajanto Wahyu Adi Prastya, Dwiyan Panji Priyo Sidik Sasongko Rismiyati Rismiyati Rismiyati Rismiyati Rizka Putri Nawangsari Roihan Auliya Ulfattah Salman Dziyaul Azmi Satriyo Adhy Sri Mulyono Sukmawati Nur Endah Sukmawati Nur Endah Suryono Suryono Wahyul Amien Syafei Widiyanto Yeny Rochmawati, Yeny Yudo Prasetyo