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All Journal Jupiter Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JSiI (Jurnal Sistem Informasi) JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informasi dan Komunikasi IJCOSIN : Indonesian Journal of Community Service and Innovation
Rona Nisa Sofia Amriza
Universitas Telkom
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Jupiter

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Komparasi Metode Machine Learning dan Deep Learning untuk Deteksi Emosi pada Text di Sosial Media Rona Nisa Sofia Amriza; Didi Supriyadi
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 2 (2021): JUPITER Edisi Oktober 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/3603.jupiter.2021.10

Abstract

Emotion Detection is the process of human emotions recognition, it extracting emotions such as happy, sad, and angry, which are obtained from human natural language. Linguistic Style has a wide range, emotional representations occur to millions of people and makes it difficult to infer a person's emotion in a concrete way. Multilabel datasets are also a challenge to deal in emotion detection. Therefore, an in-depth study of the appropriate method for emotional detection is needed. This study performs a comparative analysis between machine learning methods and deep learning methods. The machine learning methods used are Naïve Bayes, Random Forest, SVM, Gradient Boosting and Logistic Regression. The deep learning methods used in this study include LSTM, CNN, MLP, GRU and RNN. This research discovered that Deep learning has a better performance than machine learning, it seen from the accuracy values ​​of LSTM, CNN, MLP, GRU and RNN which exceed the accuracy values ​​of Naïve Bayes, SVM, Logistic Regression, Gradient Boosting and Random Forest.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Achmad Rizky Fambudianto Ahmad Mukhlis Jundulloh Artika Arista Citra Wiguna Darmansah Dedy Agung Prabowo Didi Supriyadi Dwi Januarita Dwi Januarita A.K Dwi Januarita Ardianing Kusuma Fadhilla, Cut Alna Farhanah, Titi Fitriana, Rian Gunawan, Chichi Rizka Jonatan Maruli Butarbutar Jundulloh, Ahmad Mukhlis Khairun Nisa Meiah Ngafidin Logiandani Logiandani M Yoka Fathoni Maulana Wildan Rifaldi Mega Aprilia Fajriati Muhamad Nur Gunawan Muhamad Nur Gunawan Nafdik Zaydan Raushanfikar Nurlaila, Ayu Annisa Nurul Fitriani Raihan Zidane Ramadhan Ratnasari, Wiwit Raushanfikar, Nafdik Zaydan Safhira Nanda Rahmadhani Sarah Astiti S.Kom., M.MT Sisilia Thya Safitri Siti Nur Amalia Siti Ummi Masruroh Titi Farhanah Tsania Maulidia Wijiasih Yogo Dwi Prasetyo