This study aims to develop deep learning-based Smart Storytelling media to improve Indonesian language literacy and instill Sustainable Development Goals (SDGs) values among fifth-grade elementary school students. Employing a Research and Development (R&D) approach, the study used the ADDIE model, consisting of five stages: Analysis, Design, Development, Implementation, and Evaluation. The resu…
Masalah: Identifikasi batas jantung dan rongga dada pada citra USG jantung janin masih dilakukan secara manual sehingga berpotensi menimbulkan variabilitas hasil. Selain itu, karakteristik citra USG seperti speckle noise, kontras yang rendah, serta dominasi intensitas abu-abu membuat proses segmentasi struktur anatomi menjadi lebih menantang. Tujuan: Penelitian ini bertujuan mengimplementasikan…
Deteksi karies gigi masih menjadi tantangan karena bergantung pada interpretasi visual subjektif, yang dapat menyebabkan inkonsistensi. Metode deep learning, khususnya Convoulutional Neural Network (CNN), telah menunjukkan potensi besar dalam meningkatkan akurasi diagnostik berbasis radiografi. Tujuan: Untuk meninjau literatur terkini mengenai aplikasi deep learning dalam deteksi karies gigi ot…
Diagnosis of congenital heart disease in children, namely Atrial Septal Defect (ASD), Atrioventricular Septal Defect (AVSD), and Ventricular Septal Defect (VSD), is hindered by the limited availability of cardiologists in interpreting echocardiography images, necessitating an automated system based on artificial intelligence. This study implements and evaluates a deep learning-based image capti…
Ultrasound (USG) image analysis for detecting Congenital Heart Disease (CHD), such as Atrial Septal Defect (ASD), Ventricular Septal Defect (VSD), and Atrioventricular Septal Defect (AVSD), is still limited by the scarcity of clinical datasets. This study evaluates three Generative Adversarial Network (GAN) architectures, namely Deep Convolutional GAN (DCGAN), Wasserstein GAN with Gradient Pena…
A lung cancer diagnosis can be performed using 3D CT scan images. Cancerous areas are identified through a segmentation process. This study develops a 3D U-Net with Residual Network (ResNet) and InceptionV3 to segment lung cancer in 3D CT scans. The 3D U-Net architecture was applied because it can exploit volumetric spatial information in 3D CT scan images. ResNet is applied to the encoder sect…
This study aims to describe the mathematics learning process and analyze the learning outcomes of Two Variable Linear Equation Systems (SPLDV) material using a Deep Learning approach. The research subjects were 23 ninth-grade students at SMP Negeri 58 Palembang. This study used a descriptive method with data collection through observation and written tests consisting of three contextual questio…
Traffic accidents are a road safety issue that can result in fatalities. This study aims to compare the performance of machine learning models namely, Random Forest, XGBoost and LightGBM and to explain the prediction results of the best model using the Explainable Artificial Intelligence (XAI) approach, with SHapley Additive exPlanations (SHAP) employed as the interpretation method. The data us…
Traffic accidents are one of the transportation issues that require distribution analysis to identify areas with different accident characteristics. This study aims to compare the K-Means, DBSCAN, and Hierarchical Clustering methods in clustering traffic accident data based on geographical location and accident severity. The dataset used is derived from traffic accident data in the United Kingd…
Electrocardiogram (ECG) signals represent the electrical activity of the heart and are used to record disorders such as arrhythmia and heart failure. Due to their non-stationary nature, ECG signals require a time-frequency domain approach to capture their dynamic characteristics more accurately. This study aims to develop and evaluate machine learning-based heart disorder classification models …
Congenital heart disease (CHD) in children, such as atrial septal defects (ASD), ventricular septal defects (VSD), and atrioventricular septal defects (AVSD), requires accurate diagnosis through dynamic analysis. However, existing methods for analyzing echocardiographic video are often limited to frame-by-frame analysis and are not yet capable of consistently tracking temporal changes. This stu…
Image captioning is a task in the fields of computer vision (CV) and natural language processing (NLP) that aims to generate textual descriptions from an image. In this study, various combinations of encoder–decoder architectures were designed and evaluated to improve captioning performance on cervical medical images from the International Agency for Research on Cancer (IARC). The encoders us…
Teknologi kendaraan otonom memerlukan sistem navigasi yang andal untuk menghadapi lingkungan dinamis dan kondisi jalan yang kompleks. Tantangan utama meliputi perencanaan rute optimal, deteksi objek secara real-time, serta adaptasi terhadap rintangan. Penelitian ini mengusulkan integrasi Improved Ant Colony Optimization (IACO) dan YOLOv8 sebagai solusi terpadu untuk perencanaan rute dan deteksi…
Glaucoma is a chronic eye disease that can lead to blindness. Glaucoma detection can be done by classification using the DenseNet architecture. DenseNet provides good model performance, but often suffers from overfitting. Bottleneck layers can be used to prevent overfitting by reducing the feature dimensions before entering deeper layers. However, reducing the feature dimension may lead to the …
Classification of music genres has been carried out using various features. Research by Zhang (2022) conducted music genre classification based on sound frequency waves, resulting in an accuracy of 91% with a dataset of 1000 audio files. Music genre classification based on lyrics has also been studied by Oramas et al. (2017), the research used a dataset of 31,471. This study focuses on solving …