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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Manajemen dan Keuangan Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal Manajemen Magister Darmajaya Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JIM UPB (Jurnal Ilmiah Manajemen Universitas Putera Batam) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Jurnal Informasi dan Komputer Jurnal Teknologi Komputer dan Sistem Informasi Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Inovasi Penelitian Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Sylva Scienteae Jurnal Publika Pengabdian Masyarakat Jurnal INFOTEL Neraca Manajemen, Akuntansi, dan Ekonomi JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Aziz, RZ. Abdul
Institut Informatika Dan Bisnis Darmajaya, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Journal of Applied Data Sciences

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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.
An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data Aziz, RZ Abdul; Nurpambudi, Ramadhan; Herwanto, Riko; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.913

Abstract

Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Co-Authors -, Sukatmi Abda Abda Abdollah, Mohd Faizal Achmad Nuzul Mariyus Agustian Prakarsyah anggalia wibasuri Anggawidia Wibaselppa Ari Kurniawan Saputra Arioen, Refi Asnusa, Soraya Ayunisa, Puan Mutia Damsir, Damsir Delli Maria Delli Maria Delli Maria Dewi Agushinta R. Dewi, Deshinta Arrova Dona Yuliawati Eka Suci Puspita Wulandari Esti Dwi Putri Firmansyah Firmansyah Hardiansyah, Deni Hasibuan, M Said Herwanto, Riko Herwanto, Riko Ikhsanudin, Anas Irianto Irianto Jeprianto Jeprianto Joko Triloka Khodijah Khodijah Kurniawan, Tri Basuki Kurniawati . M Said Hasibuan Maizary, Ary Mawarni, Rima Muhamad Galih Ramaputra Muhammad Galih Ramaputra Mutiara, Suci Neni Purwati Nurkholish Setya, Yoga Nurpambudi, Ramadhan Onno W. Purbo Pakarti Riswanto R Rizal Isnanto Rahma, Safaa Atiqa Ramadhan Nurpambudi Retno Dwi Handayani Retno Dwi Handayani Rini Gustini Rini Nur Listiani Rionaldi Ali Rivardi, Ganda Syahertian Rustam Rustam Saputra, Novian Ridho Satria, Fiqih Selamat Soni Harsono Wijaya Sri Karnila Sri Karnila Sri Lestari SRI RAHAYU Sriyanto . Sriyanto, Sriyanto Suci Mutiara Suhendro Suhendro Tri Nurul Fajarotun Triloka, Joko Wibaselppa, Anggawidia Wibaselppa, Anggawidia Wiratama, Yunada Wulandari, Heptyana Sri Y. Suhendro Yan Aditiya Pratama Yeremia Firhan Subagja Yoga Pratomo Yulmaini Yulmaini Yuni Puspita Sari Zuriati, Zuriati