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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Manajemen Informatika dan Sistem Informasi Community Development Journal: Jurnal Pengabdian Masyarakat
Amrul Faruq
Universitas Muhammadiyah Malang
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health Other

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Journal : telkomnika telecommunication computing electronics and control

 1 
Optimization of an Intelligent Controller for an Unmanned Underwater Vehicle Amrul Faruq; Shahrum Shah Bin Abdullah; M. Fauzi Nor Shah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 2: August 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v9i2.695

Abstract

 Underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain it position at a certain depth in order to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an unmanned underwater vehicle is described in this paper. The control algorithm is simulated by using the marine guidance navigation and control simulator. The project shows a radial basis function metamodel can be used to tune the scaling factors of a fuzzy logic controller. By using offline optimization approach, a comparison between genetic algorithm and metamodeling has been done to minimize the integral square error between the set point and the measured depth of the underwater vehicle. The results showed that it is possible to obtain a reasonably good error using metamodeling approach in much a shorter time compared to the genetic algorithm approach.
A comparative analysis of transfer learning models on suicide and non-suicide textual data Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; Muhammad Irfan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.25654

Abstract

The rise of social media has allowed individuals to express themselves freely, increasing the visibility of mental health concerns, including suicidal tendencies. This issue is particularly significant, as suicide is one of the leading causes of death globally. The objective of this study is to develop a model capable of accurately detecting suicide-related textual data using advanced natural language processing techniques. To achieve this, we applied transfer learning models, including bidirectional encoder representations from transformers (BERT), robustly optimized bidirectional encoder representations from transformers (RoBERT), a lite BERT (ALBERT), and decoding-enhanced BERT with disentangled attention (DeBERTa). the dataset used in this research includes 232,074 posts from Reddit, categorized into suicide and non-suicide labels. Preprocessing steps such as removing HTML tags, special characters, and punctuation were applied, followed by stopword removal and lemmatization. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models tested, DeBERTa demonstrated superior performance, achieving an accuracy of 98.70% and an F1-score of 98.70%. These findings suggest that transfer learning models, particularly DeBERTa, are effective in identifying suicidal ideation in textual data.
Co-Authors Abdurrahim Abdurrahim Achmad Fauzan Hery Afirda Desember Riawati Agus Heriyanto Akhmad Hakam Sudrajat Aminaton Marto Aminaton Marto Amir Parnianifard Angger Abdul Razak Basri Nor Cahyadi Basuki, Setio Chasrun Hasani Che Munira Che Razali Che Munira Che Razali Diding Suhardi Ermanu Azizul Hakim Erwin Nurdiansyah Fitri Yakub Guntur Alam Hasani, Chasrun Ilham Pakaya Indah Sulistiyowati Irfan, Muhammad Izzaty, Nadia Karima Khaeruddin Khaeruddin Khaeruddin Khusnul Hidayat Kuye, Abidemi Tolulope M. Fauzi Nor Shah M. Irfan Machmud Effendy Machmud Effendy Machmud Effendy Mauridhi Hery Purnomo Merinda Lestandy Mochammad Arif Hidayatullah Mohd Anuar Abu Bakar Mohd Fauzi Nor Shah MOHD HUSSEIN, SHAMSUL FAISAL Mohd Sabre, Mohamad Syafiq Muhammad Ilham Perdana Muhammad Irfan Muhammad Irfan Muhammad Nasar Nisrina Nurhafizhah Noor Cahyadi, Basri Novendra Setyawan Nugroho, Herry Prasetyo Nur Alif Nur Alif Mardiyah Nur Alif Mardiyah, Nur Alif Nur Kasan, Nur Nuralif Nuralif Nurdiansyah, Erwin Octavio, Abi Mufid Putri Nayla Sabri Qory Hidayati Shahrum Shah Abdullah Shahrum Shah Abdullah Shahrum Shah Abdullah Shahrum Shah Abdullah Shahrum Shah Bin Abdullah Shamsul Faisal Mohd Hussein Sudrajat, Akhmad Hakam Sumadi, Fauzi Dwi Setiawan Syafaah, Lailis Syafaah, Lailis Vhirdausia, Nuri Wijaya, Frenischa Yincenia Zakia Azzahro Zulfatman Zulfatman