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All Journal Jurnal Teknik Industri Syntax Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnal Inspiration JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Cyberspace: Jurnal Pendidikan Teknologi Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) SNTE
Merinda Lestandy
Universitas Muhammadiyah Malang
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Public Health Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes Merinda Lestandy; Abdurrahim Abdurrahim; Lailis Syafa’ah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.645 KB) | DOI: 10.29207/resti.v5i4.3308

Abstract

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.
Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding Amrul Faruq; Merinda Lestandy; Adhi Nugraha; Abdurrahim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5641

Abstract

Depression, a prevalent mental condition worldwide, exerts a substantial influence on various aspects of human cognition, emotions, and behavior. The alarming increase in deaths attributable to depression in recent years demonstrates the imperative need to address this problem through prevention and treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to express their emotions and experiences openly. Reddit stands out among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles on depression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives to detect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings. The BiLSTM-BiGRU model analyzes information bidirectionally, detecting correlations in sequential data. It is suitable for tasks dependent on input order or for addressing data uncertainties. The Reddit dataset, which contains text concerning depression, achieved an accuracy score of 97.03% and an F1 score of 97.02%.
Co-Authors Abdurrahim Abdurrahim Abdurrahim Abdurrahim Achmad Nurdim Fikri Adhi Nugraha Adhi Nugraha Adhi Nugraha Adji, Bima Septian Alvian Widianto Amrul Faruq Amrul Faruq Amrul Faruq Aqmal, Muhammad ARC, La Febry Ashri Shabrina Afrah Bayanaka, Dzaky Darrell Catur Diah Rochmad Chatarina Umbul Wahyuni Damayanti, Alfina Fatma Ermanu Azizul Hakim Fauzi, Ahmad Ihsan Hamidah, Anggia Shifrila Ilham Pakaya Irfan, Muhammad Isnani Dzuhrina Khaeruddin Khaeruddin Khusnul Hidayat Lailis Syafa'ah Lailis Syafa’ah Lailis Syafa’ah Laksono, Pranadi Sigit Dwi Lukman Hakim M. Irfan M. Irfan M. Irfan M. Irfan M. Irfan M. Nasar Machmud Effendy Mochammad Arif Hidayatullah Mohammad Chasrun Hasani Muh. Akhirul Ramadhan Muhammad Aswin Muhammad Bara Al Farisyi Muhammad Ibnu Alfarizi Noor Cahyadi, Basri Novendra Setyawan Nur Alif Mardiyah, Nur Alif Nur Kasan, Nur Nurhadi Nurhadi Nurhadi, Nurhadi Putra, Dwiko Putri, Raaina Arleandy R, Dwi Nur Rachmawati, Dwi Nur Selfi Rizkianto Shanty Kusuma Dewi Soultoni, Fikri Hakim Sultan Maula Chamzah Suwondo, Juwita P. R. Syafaah, Lailis Syafaah, Lailis Syafa’ah, Lailis Syafa’ah, Lailis Yoga Nur Fimansyah Yoga Nur Firmansyah Zulfatman Zulfatman