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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Simetris Jurnal Pengembangan Kota Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Krea-TIF: Jurnal Teknik Informatika Jurnal Riset Informatika Progresif: Jurnal Ilmiah Komputer Jurnal Informatika: Jurnal Pengembangan IT
Arif Dwi Laksito
Universitas Amikom Yogyakata
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Jurnal Riset Informatika

 1 
Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning Ahmad Rais Dwijaya; Arif Dwi Laksito
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.505

Abstract

The COVID-19 pandemic that hit the world at the end of early 2020 caused many losses. The Indonesian government has established various ways to reduce the path of the COVID-19 pandemic by launching the PeduliLindungi application to reduce the spread of COVID-19. Various layers of society responded to the launch of the application with various opinions. This research mainly analyzes public opinion sentiment toward the PeduliLindungi application, as determined by 10,000 reviews on the Google Play Store. This study aims to compare the performance of deep learning and machine learning models in sentiment analysis. The stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word TF-IDF, which includes the Nave Bayes algorithm, Decision Tree, Random Forest, K-Nearest Neighbour, and SVM. As for the deep learning model with the fastText word embedding word representation technique using the LSTM algorithm, an evaluation is carried out using the confusion matrix. The results of this study state that deep learning models perform better than machine learning models.
Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning Ahmad Rais Dwijaya; Arif Dwi Laksito
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.505

Abstract

The COVID-19 pandemic that hit the world at the end of early 2020 caused many losses. The Indonesian government has established various ways to reduce the path of the COVID-19 pandemic by launching the PeduliLindungi application to reduce the spread of COVID-19. Various layers of society responded to the launch of the application with various opinions. This research mainly analyzes public opinion sentiment toward the PeduliLindungi application, as determined by 10,000 reviews on the Google Play Store. This study aims to compare the performance of deep learning and machine learning models in sentiment analysis. The stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word TF-IDF, which includes the Nave Bayes algorithm, Decision Tree, Random Forest, K-Nearest Neighbour, and SVM. As for the deep learning model with the fastText word embedding word representation technique using the LSTM algorithm, an evaluation is carried out using the confusion matrix. The results of this study state that deep learning models perform better than machine learning models.
Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification Mukhamad Rizal Ilham; Arif Dwi Laksito
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.507

Abstract

A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically. From part-of-speech (POS) parsing and tagging to machine translation and dialogue systems, NLP enables computers to carry out various natural language-related activities at all levels. In this research, we compared word embedding techniques FastText and GloVe, which are used for text representation. This study aims to evaluate and compare the effectiveness of word embedding in text classification using LSTM (Long Short-Term Memory). The research stages start with dataset collection, pre-processing, word embedding, split data, and the last is deep learning techniques. According to the results of the experiments, when compared to the glove technique it seems that FastText is superior, the accuracy obtained reaches 90%. The number of epochs did not significantly improve the accuracy of the LSTM model with GloVe and FastText. It can be concluded that the FastText word embedding technique is superior to the GloVe technique.
Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification Mukhamad Rizal Ilham; Arif Dwi Laksito
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.507

Abstract

A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically. From part-of-speech (POS) parsing and tagging to machine translation and dialogue systems, NLP enables computers to carry out various natural language-related activities at all levels. In this research, we compared word embedding techniques FastText and GloVe, which are used for text representation. This study aims to evaluate and compare the effectiveness of word embedding in text classification using LSTM (Long Short-Term Memory). The research stages start with dataset collection, pre-processing, word embedding, split data, and the last is deep learning techniques. According to the results of the experiments, when compared to the glove technique it seems that FastText is superior, the accuracy obtained reaches 90%. The number of epochs did not significantly improve the accuracy of the LSTM model with GloVe and FastText. It can be concluded that the FastText word embedding technique is superior to the GloVe technique.
Co-Authors Acihmah Sidauruk Adninda, Gardyas Bidari Anas Fikri Hanif Burhanudin, Nazil Ilham Darmawan, Briga Eli Pujastuti, Eli Ervan Triyanto Hafizh Fauzi Fathurahman Hayaty, Mardhiya Heri Sismoro Humam, Choirul Kartikakirana, Renindya Azizza Mahanggara, Andika Mahanggara, Andika Muhammad Darwis Arifin Muhammad Resa Arif Yudianto Muhammad Royyan Saputra Nur ‘Azah Nurcholis, Muhammad Tofa Rahmi, Alfie Nur Sasongko, Theopilus Bayu Sumarni Adi Syauqani Juliansyah