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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics Sewagati
Kelly Rossa Sungkono
Departemen Teknik Informatika, Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study Haryono, Agus Tri; Sarno, Riyanarto; Sungkono, Kelly Rossa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp861-869

Abstract

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.
Improving cyberbullying detection through multi-level machine learning Salsabila, Salsabila; Sarno, Riyanarto; Ghozali, Imam; Sungkono, Kelly Rossa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1779-1787

Abstract

Cyberbullying is a known risk factor for mental health issues, demanding immediate attention. This study aims to detect cyberbullying on social media in alignment with the third sustainable development goal (SDG) for health and well-being. Many previous studies employ single-level classification, but this research introduces a multi-class multi-level (MCML) algorithm for a more detailed approach. The MCML approach incorporates two levels of classification: level one for cyberbullying or not cyberbullying, and level two for classifying cyberbullying by type. This study used a dataset of 47,000 tweets from Twitter with six class labels and employed an 80:20 training and testing data split. By integrating bidirectional encoder representations from transformers (BERT) and MCML at level two, we achieved a remarkable 99% accuracy, surpassing BERT-based single-level classification at 94%. In conclusion, the combination of MCML and BERT offers enhanced cyberbullying classification accuracy, contributing to the broader goal of promoting mental health and well-being.
Co-Authors ABDUL MUNIF Adhatus Solichah Ahmadiyah, Adhatus Solichah Agus Tri Haryono, Agus Tri Ahmadiyah, Adhatus Solichah Alifiansyah Arrizqy Hidayat Anggraini, Ratih Nur Esti Anita Hakim Nasution Anny Yuniarti Arifiani, Siska Azzam Jihad Ulhaq Azzam Jihad Ulhaq Berlian Rahmy Lidiawaty Bilqis Amaliah Dini Adni Navastra Hidayat, Husnul Hidayati, Shintami Chusnul Imam Ghozali Isnaini Nurul Kurnia Sari Isnaini Nurul KurniaSari Kartini Kartini Khairun Nisa Muhammad Riduwan Muhammad Taufiqulsa’di Muhammad Taufiqulsa’di Navinda Meutia Navinda Meutia Nurul Fajrin Ariyani Ratih Nur Esti Anggraeni Ratih Nur Esti Anggraini, Ratih Nur Esti Riyanarto Sarno Salsabila, Salsabila Shintami Chusnul Hidayati Widyasari Ayu Wibowo Wijayanti Nurul Khotimah Yutika Amelia Zahrul Zizki Dinanto Zahrul Zizki Dinanto