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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization eProceedings of Engineering
Erwin Budi Setiawan
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics

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Journal : Jurnal Teknik Informatika (JUTIF)

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FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE Wida Sofiya; Erwin Budi Setiawan
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.855

Abstract

Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets.
Co-Authors Aditya Mahendra Zakaria Agung Toto Wibowo Ahmad Zahri Ruhban Adam Aji Reksanegara Alvi Rahmy Royyan Anang Furkon RIfai Ananta Ihza Ramadhan Anindika Riska Intan Fauzy Annisa Aditsania Annisa Aditsania Annisa Cahya Anggraeni Annisa Cahya Anggraeni Annisa Rahmaniar Dwi Pratiwi Arie Ardiyanti Arki Rifazka Athirah Rifdha Aryani Azrina Fazira Ansshory Bagas Teguh Imani Billy Anthony Christian Martani Brenda Irena Brigita Tenggehi Crisanadenta Wintang Kencana Damarsari Cahyo Wilogo Daniar Dwi Pratiwi Daniar Dwi Pratiwi Dede Tarwidi Dedy Handriyadi Dery Anjas Ramadhan Dhinta Darmantoro Diaz Tiyasya Putra Dion Pratama Putra Diyas Puspandari Faidh Ilzam Nur Haq Fathurahman Alhikmah Fathurahman Alhikmah Febiana Anistya Feby Ali Dzuhri Fhina Nhita Fhina Nhita Fida Nurmala Nugraha Ghani Kamil Ghina Dwi Salsabila Gita Safitri Grace Yohana Grace Yohana Hanif Reangga Alhakiem Helmi Sunjaya Ramadhan Hildan Fawwaz Naufal Hilman Bayu Aji Husnul Khotimah Farid I Gusti Ayu Putu Sintha Deviya Yuliani I Kadek Candradinata Ilyana Fadhilah Iqbal, Bayu Muhammad Irma Palupi Isabella Vichita Kacaribu Isep Mumu Mubaroq Isman Kurniawan Kartika Prameswari Kemas Muslim Lhaksmana, Kemas Muslim Kevin Usmayadhy Wijaya Luthfi Firmansah M. Arif Bijaksana Mahmud Imrona Mansel Lorenzo Nugraha Marissa Aflah Syahran Marissa Aflah Syahran Maulina Gustiani Tambunan Mela Mai Anggraini Moh Adi Ikfini M Muhammad Afif Raihan Muhammad Faiq Ardyanto Putro Muhammad Khiyarus Syiam Muhammad Kiko Aulia Reiki Muhammad Noer Ibnu Sina Muhammad Nur Ilyas Muhammad Shiba Kabul Muhammad Tsaqif Muhadzdzib Ramadhan Nabilla Kamil Naufal Adi Nugroho Nisa Maulia Azahra Nur Ihsan Putra Munggaran Rafi Anandita Wicaksono Raisa Sianipar Rakhmat Rifaldy Rayhan Rahmanda Refka Muhammad Furqon Rendo Zenico Ridho Maulana Cahyudi Rizki Annas Sholehat Rizki Tri Setiawan Roji Ellandi Sari Ernawati Saut Sihol Ritonga Septian Nugraha Kudrat Septian Nugraha Kudrat Shakina Rizkia Siti Inayah Putri Sri Suryani Sri Suryani Sukmawati Dwi Lestari Wida Sofiya Windy Ramadhanti Yoan Maria Vianny Yuliant Sibaroni ZK Abdurahman Baizal