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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Majalah Ilmiah Teknologi Elektro Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM KOMPUTER JURNAL SISTEM INFORMASI BISNIS TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Rekayasa Mesin Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Scientific Journal of Informatics Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics Jurnal Ilmu Teknik dan Komputer Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Jurnal Profesi Insinyur Indonesia Jurnal Teknologi Majalah Ilmiah Teknologi Elektro Siber International Journal of Advanced Law
R Rizal Isnanto
Departemen Teknik Sistem Komputer, Universitas Diponegoro Jl. Prof. Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Transportation

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Journal : Journal of Applied Data Sciences

 1 
High-Accuracy Stroke Detection System Using a CBAM-ResNet18 Deep Learning Model on Brain CT Images Tahyudin, Imam; Isnanto, R Rizal; Prabuwono, Anton Satria; Hariguna, Taqwa; Winarto, Eko; Nazwan, Nazwan; Tikaningsih, Ades; Lestari, Puji; Rozak, Rofik Abdul
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.569

Abstract

Stroke is a brain dysfunction that occurs suddenly as a result of local or overarching damage to the brain, lasts for at least 24 hours, and causes about 15 million deaths each year globally. Immediate medical treatment is essential to reduce the potential for further brain damage in stroke patients. Medical imaging, especially computed tomography (CT scan), plays a crucial role in the diagnosis of stroke. This study aims to develop and evaluate a deep learning architecture based on Convolutional Block Attention Module (CBAM) and ResNet18 for stroke classification in CT images. This model is designed through data preprocessing, training, and evaluation stages using a cross-validation approach. The results showed that the CBAM-ResNet18 integration resulted in a high accuracy of 95% in distinguishing stroke and non-stroke cases. The accuracy rate reached 96% for nonstroke identification (class 0) and 94% for stroke (class 1), with recall rates of 96% and 93%, respectively. Outstanding classification ability is demonstrated by an Area Under the Curve (AUC) value of 0.99. In comparison, the standard ResNet18 model shows significant fluctuations in validation loss and difficulty in generalization, with training accuracy only reaching 64-68%. On the other hand, CBAM-ResNet18 showed a significant performance improvement with a validation accuracy of 95%, a validation loss of 0.0888, and good generalization on new data. However, the limitations of the dataset and the interpretation of the results indicate the need for further validation to ensure the generalization of the model. These results show the great potential of the CBAM-ResNet18 architecture as an innovative tool in stroke diagnostic technology based on CT imaging analysis. This technology can support faster and more accurate clinical decision-making, as well as open up opportunities for further research related to the development of artificial intelligence-based systems in the medical field.
A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG) Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Yeh, Ming-Lang; Wijaya, Adi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.659

Abstract

The identification of learning styles plays a crucial role in enhancing personalized education and optimizing learning outcomes. This research proposes a model for detecting learning styles based on the Felder-Silverman model using two machine learning algorithms: K-Nearest Neighbors (KNN) and Linear Regression (LR). Electroencephalography (EEG) data, known for its ability to capture cognitive and neural activity, serves as the primary dataset for this study. The proposed model was tested on a dataset comprising EEG signals collected during various learning tasks. Feature extraction and preprocessing techniques were employed to ensure high-quality input for the learning algorithms. The experimental results revealed that the LR-based model achieved an accuracy of 96.4%, significantly outperforming the KNN-based model, which obtained an accuracy of 89.9%. These findings highlight the potential of EEG-based models for accurately identifying learning styles, offering valuable insights for educators and researchers aiming to implement adaptive learning systems. This study demonstrates the feasibility and effectiveness of combining EEG data with machine learning techniques for learning style detection, paving the way for more personalized and efficient educational approaches. Future research will explore the integration of additional physiological data and advanced machine learning methods to further improve model accuracy and applicability.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Triloka, Joko; Aziz, RZ Abdul; Kurniawan, Tri Basuki; Maizary, Ary; Wibaselppa, Anggawidia
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Co-Authors Abdul Syakur Achmad Chaerodin Achmad Hidayatno Achmad Hidayatno Ade Riyantika Dewi Adhi Susanto Adi Mora Tunggul Adi Wijaya Adian Fatchur Rochim Adrian Putranda Rispurwadi Agus Suprihanto Agustini, Eka Puji Ahmad Ramdhani Ajub Ajulian Zahra Macrina Alan Prasetyo Rantelino Albert Ginting An'im Almiktad Andhika Dewanta Andhika Hanifa Naufaliawan Andino Maseleno Anton Satria Prabuwono Ardianto Eskaprianda Ari Muhardono Arianto, Mufid Aris Puji Widodo Aris Sugiharto Aris Triwiyatno Astrid Aprillini Aulia Nastiti Aziz, RZ. Abdul Bhutra, Yuvraj Budi Warsito Catur Edi Widodo Chauhan, Rahul Damar Wicaksono Danang Respati Setyabudi Deddy Sucipta Syahril Dewi, Deshinta Arrova Dewi, Rany Puspita Dhody Kurniawan Dian Kurnia Widya Buana Dilan Arya Sujati Dimas Robby Firmanda Dini Indriyani Putri Donni Widagdo Dwi Novianto Eko Didik Widianto Eko Winarto Erizco Satya Wicaksono Ervin Adhi Cahyanugraha Fatima Setyani Ferry Dwi Setiyawan Firdaus Aditya GALIH WICAKSONO Gilang Aditya Pamungkas Handayani, Sri Hardiyanto Hardiyanto Hayu Andarwati Hefmi Fauzan Imron Hendy Cahya Lesmana Hilal Afrih Juhad Ike Pertiwi Windasari Imaduddin Amrullah Muslim Imam Tahyudin Irham Fa'idh Faiztyan Iwan Purwanto Jatmiko Endro Suseno Julianto, Dewa Rizki Rahmat Kataria, Yachi Kurniawan Teguh Martono Kurniawan, Tri Basuki Kusworo Adi Lia Lidya Roza Liga Filosa M Said Hasibuan Maizary, Ary Maman Somantri Martin Clinton Tosima Manullang Maulana Muhammad Iqbal Misik Puspajati Nurmadjid Saputri Mona Pradipta Hardiyanti Muh. Udka Muhamad Taopik Gibran Muhammad Fahmi Awaj Muhammad Kautsar Muhammad Nur Hadi Munawar Agus Riyadi Mustafa, Mustafa Mustafid Mustafid Nahdi Saubari Nanang Sulaksono, Nanang Nazwan, Nazwan Neneng Neneng Nur Setyo Permatasasi Putri W Nurhayati, Oki Dwi Nurul Arifa Oky Dwi Nurhayati Pertiwi, Rahayu Putri Prakasa, Fawwaz Bimo Pramuko Tri Prastowo Prima Widyaningrum PUJI LESTARI Qoriani Widayati Rachel Chrysilla Tijono Refika Khoirunnisa Reza Najib Hidayat Rinta Kridalukmana Rinta Kridalukmana Rivaldi MHS Riyana Putri, Fayza Nayla Rizaldi Habibie Rizaldy Khair Rizky Gelar Maliq Rosdelima Hutahaean Roza, Lia Lidya Rozak, Rofik Abdul Ruli Handrio Santoso, Imam Saptian, Fiega Adhi Sapto Nisworo Sasongko, Cornelius Damar Satya Arisena Hendrawan Setiawan, Annas Sri Lestari Sri Sumiyati Sri Widodo, Thomas Suhardjo Suhardjo Sumardi . Talitha Almira Taqwa Hariguna Teguh Dwi Prihartono Tikaningsih, Ades Toni Wijanarko Adi Putra Triloka, Joko Tyas Panorama Nan Cerah Ucky Pradestha Novettralita Ufan Alfianto Unaliya, Maitri Waluyo Nugroho Waluyo Wandri Okki Saputra Wibaselppa, Anggawidia Widi Puji Atmojo Widiasmoro, Andi Wijaya, Elang Pramudya Yatim, Ardiyansyah Saad Yeh, Ming-Lang Yenita Dwi Setiyawati Yessy Kurniasari Yongki Yonatan Marbun Yudi Eko Windarto Yunus Anis, Yunus