Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

1.881

P-Index

This Author published in this journals

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

Author-ID : 2459804

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

Published : 46 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1 2

Internal content classification of ultrasound thyroid nodules based on textural features Nugroho, Anan; Nugroho, Hanung Adi; Setiawan, Noor Akhmad; Choridah, Lina
Communications in Science and Technology Vol 1 No 2 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.2.2016.25

Ultrasound (US) is one of the best imaging modalities on thyroid identification. The suspicious thyroid is indicated in the existence of palpable nodules whose solid or cystic composition. Solid nodules have high possibility to be malignant than cystic. An effort to detect and classify the internal content of thyroid nodule has become challenge problem in radiology area. Operator dependence of ultrasound imaging makes it complicated due to missing interpretation among radiologists. Objective Computer Aided Diagnosis (CAD) was designed to solve it which works on texture analysis of histogram statistic, gray level co-occurrence matrice (GLCM) and gray level run length matrices (GLRLM). The fine-needle aspiration cytology (FNAC) is not needed because the textural pattern is significantly different between solid and cystic nodules.Â Multi-layer perceptron (MLP) was adopted to do classification process for 72 US thyroid images yield an accuracy of 90.28%, the sensitivity of 87.80%, specificity of 93.55% and precision of 94.74%.

Interval type-2 fuzzy logic system for diagnosis coronary artery disease Sajiah, Adha Mashur; Setiawan, Noor Akhmad; Wahyunggoro, Oyas
Communications in Science and Technology Vol 1 No 2 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.2.2016.26

Coronary artery disease (CAD) is a disease that has been the deadliest disease in Indonesia. The ratio of cardiologists over potential patients is not appropriate either. Intelligent system which can help doctors or patients for cheap and efficient diagnosing CAD is needed. Medical record data, acquisition of cardiologist knowledge and computing technology can be utilized for developing fuzzy logic based intelligent system. Type-1 fuzzy logic system (T1 FLS) has been widely used in various fields. T1 FS has limitation in representing and modelling uncertainty and minimize the impact. Whereas, type-2 fuzzy set (T2 FS) was also introduced as fuzzy set that can model uncertainty more sophisticated. T2 FLS does have a higher degree of freedom when modeling uncertainty but it is quite difficult to make the membership function. An interval T2 FS is a T2 FS in which the membership grade on third dimension is the same everywhere so it is simpler than T2 FS. This paper aims to clarify the better capability of IT2 FLS over T1 FLS on the development of CAD diagnosis system. Rules and membership function were formulated with the help of fuzzy c-means. This study illustrated the causes of CAD risk factors, fuzzification, type reduction and defuzzification. The resulted system was tested with percentage split method (50%-50%) to produce training data and testing data. This test is performed ten times with random seed to separate the data set. The resulted system generates an average of 73.78% accuracy, 71.94% sensitivity and 76.52% specificity.

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

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 TDMâ€™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.

Optimasi Deteksi Kebocoran dengan Menggunakan Phase Stretch Transform pada Retina Fluorescein Angiography Images untuk Penyakit Malaria Rochim, Febry Putra; Nugroho, Hanung Adi; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 3 No 2 (2018)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Malarial Retinopathy (MR) is indicated by retina alteration such as white dots occurrence which is caused by malaria. Leak detection is a key factor of MR’s early diagnosis. Inconsistent size and shape of the leakages with the colour contrast that relatively similar with the background. Leak detection’s algorithm is one of the most complex algorithms on the fundus image analysis field. Therefore, improving performance in the leakage detection is essential. This study focuses on automated leakage detection on fluorescein angiography (FA) images. The methods used in this study are vessel segmentation, saliency detection, phase stretch transform (PST), optic disk removal and leak detection to extract some features which then classified to correctly validate the leak. From 20 patient data large focal leak images with 31 leak points, 28 of them have been correctly detected. So, the experiment produced the accuracy and specificity of 0.98 and 0.9, respectively. With the proposed method of this study, there is a potential to enhance the knowledge on MR field in the future.

Wart treatment method selection using AdaBoost with random forests as a weak learner Putra, M. Azka; Setiawan, Noor Akhmad; Wibirama, Sunu
Communications in Science and Technology Vol 3 No 2 (2018)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Selection of wart treatment method using machine learning is being a concern to researchers. Machine learning is expected to select the treatment of warts such as cryotherapy and immunotherapy to patients appropriately. In this study, the data used were cryotherapy and immunotherapy datasets. This study aims to improve the accuracy of wart treatment selection with machine learning. Previously, there are several algorithms have been proposed which were able to provide good accuracy in this case. However, the existing results still need improvement to achieve better level of accuracy so that treatment selection can satisfy the patients. The purpose of this study is to increase the accuracy by improving the performance of weak learner algorithm of ensemble machine learning. AdaBoost is used in this study as a strong learner and Random Forest (RF) is used as a weak learner. Furthermore, stratified 10-fold cross validation is used to evaluate the proposed algorithm. The experimental results show accuracy of 96.6% and 91.1% in cryotherapy and immunotherapy respectively.

Machine learning algorithm for improving performance on 3 AQ-screening classification Pratama, Taftazani Ghazi; Hartanto, Rudy; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.

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

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

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

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.

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

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 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

Title Search

Found 11 Documents Search Journal : Communications in Science and Technology

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 11 Documents
Search
Journal : Communications in Science and Technology