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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Eksplora Informatika Communications in Science and Technology Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JAREE (Journal on Advanced Research in Electrical Engineering) Communications in Humanities and Social Sciences Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal FORTEI-JEERI Prosiding SNPBS (Seminar Nasional Pendidikan Biologi dan Saintek) Pancaran Pendidikan Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Teknomatika: Jurnal Informatika dan Komputer
Noor Akhmad Setiawan
Department Of Electrical Engineering And Information Technology, Faculty Of Engineering, Universitas Gadjah Mada, Yogyakarta
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Neuroscience Physics Social Sciences

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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.
A review on smartphone usage data for user identification and user profiling Auliya, Syafira; Nugroho, Lukito Edi; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 6 No 1 (2021)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.6.1.2021.363

Abstract

The amount of retrievable smartphone data is escalating; while some apps on the smartphone are evidently exploiting and leaking users’ data. These phenomena potentially violate privacy and personal data protection laws as various studies have showed that technologies such as artificial intelligence could transform smartphone data into personal data by generating user identification and user profiling. User identification identifies specific users among the data based upon the users’ characteristics and users profiling generates users’ traits (e.g. age and personality) by exploring how data is correlated with personal information. Nevertheless, the comprehensive review papers discussing both of the topics are limited. This paper thus aims to provide a comprehensive review of user identification and user profiling using smartphone data. Compared to the existing review papers, this paper has a broader lens by reviewing the general applications of smartphone data before focusing on smartphone usage data. This paper also discusses some possible data sources that can be used in this research topic.
Spontaneous gaze interaction based on smooth pursuit eye movement using difference gaze pattern method Murnani, Suatmi; Setiawan, Noor Akhmad; Wibirama, Sunu
Communications in Science and Technology Vol 7 No 1 (2022)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.7.1.2022.739

Abstract

Human gaze is a promising input modality for being able to be used as natural user interface in touchless technology during Covid-19 pandemic. Spontaneous gaze interaction is required to allow participants to directly interact with an application without any prior eye tracking calibration. Smooth pursuit eye movement is commonly used in this kind of spontaneous gaze-based interaction. Many studies have been focused on various object selection techniques in smooth pursuit-based gaze interaction; however, challenges in spatial accuracy and implementation complexity have not been resolved yet. To address these problems, we then proposed an approach using difference patterns between gaze and dynamic objects' trajectories for object selection named Difference Gaze Pattern method (DGP). Based on the experimental results, our proposed method yielded the best object selection accuracy of and success time of ms. The experimental results also showed the robustness of object selection using difference patterns to spatial accuracy and it was relatively simpler to be implemented. The results also suggested that our proposed method can contribute to spontaneous gaze interaction.
Meningkatkan Aktivitas dan Hasil Belajar Peserta Didik dengan Menerapkan Metode Pembelajaran Kooperatif Tipe TAI Zatadini, Galuh Indah; Permanasari, Adhistya Erna; Setiawan, Noor Akhmad
Prosiding SNPBS (Seminar Nasional Pendidikan Biologi dan Saintek) 2017: Prosiding SNPBS (Seminar Nasional Pendidikan Biologi dan Saintek)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.541 KB)

Abstract

Cooperative Learning adalah model pembelajaran yang dilakukan oleh peserta didik dalam kelompok sehingga akan membuat peserta didik lebih banyak belajar dan bekerjasama dibandingkan dengan peserta didik yang melakukan kegiatan pembelajaran dengan menggunakan model pembelajaran tradisional. Penelitian yang dilakukan ini termasuk dalam model penelitian tindakan kelas yang bertujuan untuk mengetahui peningkatan aktivitas dan hasil belajar peserta didik pada mata pelajaran TIK dengan menggunakan metode Team Asissted Individualization (TAI). Dalam penelitian yang menggunakan rancangan penelitian tindakan kelas ini memiliki 3 siklus dengan masing tahapan yaitu perencanaan, pelaksanaan, pengamatan dan refleksi yang dilakukan langsung ketika kegiatan belajar. Data penelitia hasil belajar diambil ketika proses refleksi yaitu setelah dilakukan post test kepada seluruh peserta didik setelah proses belajar selesai. Sedangkan pengambilan data aktivitas dilakukan ketika proses pelaksanaan yaitu ketika peserta didik mengkuti kegiatan belajar mengajar. Dari hasil penelitian, penerapan model pembelajaran kooperatif tipe TAI dapat meningkatkan aktivitas peserta didik pada standart kompetensi “Menggunakan Perangkat Lunak Pengolah Angka untuk Menghasilkan Informasi” dan kompetensi dasar “Membuat Dokumen Pengolah Angka dengan Variasi Teks, Tabel, Grafik, Gambar dan Diagram”. Hal ini dapat dilihat dari peningkatan persentase aktivitas peserta didik dari siklus I, II, dan III. Pada siklus I total presentase aktivitas peserta didik adalah 64.1% dengan presentase masih kurang. Kemudian dilanjutkan dalam siklus II persentase nya yaitu 78% dan siklus III total persentase nya adalah 89.7%. Selain itu pembelajaran kooperatif tipa TAI juga dapat meningkatkan hasil belajar peserta didik baik dalam ranah afektif, kognitif maupun psikomotorik. Model pembelajaran ini dapat menjadi salah satu alternative yang dapat diterapkan dalam kegiatan pembelajaran namun juga harus sesuai dengan materi maupun tipe kegiatan belajar yang akan dilakukan karena model pembelajaran ini hanya cocok untuk kompetensi/pemecaham masalahnya dapat diselesaikan secara individual maupun kelompok.
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.
Interpretability Evaluation of Rule-Based Classifier in Myocardial Infarction Classification Based on Syntactical Features of ECG Signal Fityah, Farhatul; Setiawan, Noor Akhmad; Anggrahini, Dyah Wulan
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.10.2.2025.1851

Abstract

Cardiovascular diseases remain the leading cause of mortality on a global scale, with myocardial infarction (MI) representing a critical and life-threatening condition. Electrocardiography (ECG) is a widely utilized method for the detection of myocardial infarction (MI), and artificial intelligence (AI) has demonstrated a promising performance in the automated ECG-based diagnosis. However, most existing studies emphasizepredictive accuracy while failing to provide substantial evidence that model decision logic aligns with clinical reasoning, thereby limiting clinical adoption. This present study evaluates the interpretability of three rule-based machine learning classifiers—Decision Tree, RIPPER, and Rough Set—for MI detection from ECG signals, including a comparison between models with and without feature selection. Interpretability of the system is assessed through rule complexity analysis and a standardized qualitative clinical validation protocol involving three cardiologists, based on contemporary AHA/ESC ECG diagnostic guidelines. The findings indicate that the Rough Set classifier attains the optimal overall performance, with 80% of its generated rules demonstrating clinically aligned, thereby outperforming the other models regarding interpretability. The findings demonstrate the benefit of guideline-based clinical validation for advancing trustworthy ECG-based MI diagnostic systems.
Co-Authors Adhistya Erna Permanasari Adi Nugroho Adi Wijaya Adi Wijaya Agastya, I Made Artha Ahmad Fauzi Mabrur Aji, Marcus Nurtiantara Anggrahini, Dyah Wulan Anugrah Galang Persada Anugrah Galang Persada, Anugrah Galang Aras, Rezty Amalia Auliya, Syafira Baehaqi Bagus Kurniawan Bambang Sugiyantoro Berbudi Bowo Laksono Brahmantya Aji Pramudita Daru Hagni Setyadi Desyandri Desyandri Dewi, Sri Kusuma Dwi Retno Puspita Sari E. Elsa Herdiana Murhandarwati Eko Nugroho Eko Nugroho Fery Antony Fityah, Farhatul Galuh Indah Zatadini Gilang Adityasakti Hairani Hairani Hanung Adi Nugroho Haried Novriando Heilbert Armando Mapaly I Made Yulistya Negara I Md. Dendi Maysanjaya Igi Ardiyanto Indah Soesanti Ipin Prasojo Ipin Prasojo, Ipin Irma Yuliana Julianto Lemantara Kadek Dwi Pradnyani Novianti Kadek Dwi Pradnyani Novianti, Kadek Dwi Pradnyani Kharisma Adi Utama Lina Choridah Lukito Edi Nugroho Luthfi Ardi Made Satria Wibawa Maghfirah Maghfirah Maghfirah Maghfirah Maghfirah Marcus Nurtiantara Aji Mochammad Wahyudi Muhammad Arzanul Manhar Muhammad Fawaz Saputra Murnani, Suatmi Ni Wayan Priscila Yuni Praditya Nugraha, Anggit Ferdita Nugroho, Adi Nugroho, Anan Oyas Wahyunggoro Paulus Insap Santosa Persada, Anugrah Galang Prasojo, Ipin Putra, M. Azka Rahmadi, Ridho Ratna Lestari Budiani Buana Ridho Rahmadi Ridho Rahmadi Rifkie Primartha Rochim, Febry Putra Rudy Hartanto Sajiah, Adha Mashur Sekar Sari Siti Helmyati Sri Kusuma Dewi Sri Kusuma Dewi, Sri Kusuma SRI RAHAYU Sri Suning Kusumawardani Subhan Afifi Sunu Wibirama Surjono Surjono Teguh Bharata Adji Tito Yuwono, Tito Tole Sutikno Utama, Kharisma Adi Widhi Hartanto Widhia K.Z Oktoeberza Wijaya, Adi Zatadini, Galuh Indah