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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Dinamika Informatika Jurnal Ilmiah Kursor Journal of Engineering and Technological Sciences Jurnal Edukasi dan Penelitian Informatika (JEPIN) jurnal Teknologi Informasi Magister Proceeding of the Electrical Engineering Computer Science and Informatics Edu Komputika Journal Communications in Science and Technology Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Otomasi Kontrol dan Instrumentasi IJID (International Journal on Informatics for Development) Jurnal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Teknomatika: Jurnal Informatika dan Komputer
Teguh Bharata Adji
Departemen Teknik Elektro Dan Teknologi Informasi Fakultas Teknik, Universitas Gadjah Mada
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Journal : Communications in Science and Technology

 1 
Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering Agastya, I Made Artha; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.46

Abstract

Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.
Improving multi-class EEG-motor imagery classification using two-stage detection on one-versus-one approach Wijaya, Adi; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 5 No 2 (2020)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.5.2.2020.216

Abstract

The multi-class motor imagery based on Electroencephalogram (EEG) signals in Brain-Computer Interface (BCI) systems still face challenges, such as inconsistent accuracy and low classification performance due to inter-subject dependent. Therefore, this study aims to improve multi-class EEG-motor imagery using two-stage detection and voting scheme on one-versus-one approach. The EEG signal used to carry out this research was extracted through a statistical measure of narrow window sliding. Furthermore, inter and cross-subject schemes were investigated on BCI competition IV-Dataset 2a to evaluate the effectiveness of the proposed method. The experimental results showed that the proposed method produced enhanced inter and cross-subject kappa coefficient values of 0.78 and 0.68, respectively, with a low standard deviation of 0.1 for both schemes. These results further indicated that the proposed method has an ability to address inter-subject dependent for promising and reliable BCI systems.
Active-Reflective Learning Style Detection Using EEG and Abrupt Change Detection Primartha, Rifkie; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1737

Abstract

Recognizing the varying learning styles of students is vital to creating customized educational approaches and maximizing academic success. While commonly used, conventional evaluation methods such as self-report surveys are frequently characterized by subjective biases and inconsistent accuracy. To address this limitation, this present study proposes an EEG-driven approach for learning style classification, specifically targeting the Active and Reflective dimensions of the Felder-Silverman Learning Style Model (FSLSM). Data was acquired from 14 participants using an 8-channel OpenBCI headset, with cognitive engagement stimulated through Raven’s Advanced Progressive Matrices (RAPM). Initially, the raw EEG data underwent bandpass filtering process purposely to remove noise. Subsequently, the data was divided into consecutive 1-second segments. For feature extraction, the CUSUM algorithm was employed, with an aim to effectively capture significant signal variations. These features were then fed into an LDA classifier for style discrimination. The performance evaluation revealed impressive results—98.26% accuracy in standard Train-Test validation, and an even higher 99.29% under LOOCV testing. Notably, our approach consistently outperformed existing techniques including 1-DCNN and TSMG across all metrics. Notably, computational efficiency and reliability were improved, with the "Odd-only" subset yielding peak accuracy (99.24%). These findings demonstrate that integrating EEG signals with conventional machine learning enables real-time, high-precision learning style detection. Additionally, this work addresses the computational constraints and dataset limitations observed in recent studies, providing a robust foundation for adaptive learning systems. It is recommended that future research explore larger, more diverse datasets and additional FSLSM dimensions to enhance generalizability and practical implementation of the research.
Co-Authors Abdul Kadir Achmad Noerkhaerin Putra Adhistya Erna Permanasari Adi Wijaya Adi Wijaya Agastya, I Made Artha Aris Budianto, Aris Bagus Kurniawan Buana, Ratna Lestari Budiani Dwi Retno Puspita Sari Dyah Mustika, Dyah Elfrida Ratnawati Faisal Najamuddin Fathan, Gess Fauziazzuhry Rahadian Gess Fathan Hairani Hairani Hamka, Cakra Aminuddin Hamka, Cakra Aminuddin Hanung Adi Nugroho Heilbert Armando Mapaly Hesti Khuzaimah Nurul Yusufiyah Igi Ardiyanto Ignatia Dhian Estu Karisma Ratri Indriana Hidayah KZ Widhia Oktoeberza Maghfirah Maghfirah Maghfirah Maghfirah Maghfirah Moch. Ari Nasichuddin Muhammad Esa Permana Putra Naufal Azmi Verdikha Nisrina Akbar Rizky Putri Noor Akhmad Setiawan Nugroho, Arseto Satriyo Nur Annisa Tresnasari Nurirwan Saputra, Nurirwan Rahadian, Fauziazzuhry Ratna Lestari Budiani Buana Ridi Ferdiana Rifkie Primartha Risanuri Hidayat Rudy Hartanto Sampurna Dadi Riskiono Selo Sulistyo, Selo Setialana, Pradana SRI RAHAYU Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Sudaryanto . Sudaryanto Sudaryanto Sulis Setiowati Sulis Setiowati Syahroni Hidayat SYAIFULLOH AMIEN PANDEGA PERDANA Tari Mardiana Titik Hinayah Widhi Hartanto Widhia K.Z Oktoeberza Widiyanti, Tanty Yanuar Widyawan Wijaya, Adi