Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source for both traditional and modern diagnostic approaches. This study aims to provide an overview of the significant role of medical images in skin cancer detection and highlight developments in the use of deep learning for early diagnosis. The scope of this survey includes an in-depth exploration of state-of-the-art deep learning methods, an evaluation of public datasets commonly used for training and validation, and a bibliometric analysis of recent advancements in the field. This survey focuses on publications in the Scopus database from 2019 to 2024. The search string is used to find articles by their abstracts, titles, and keywords, and includes several public datasets, like HAM and ISIC, ensuring relevance to the topic. Filters are applied based on the year, document type, source type, and language. The analysis identified 1697 articles, predominantly comprising journal articles and conference proceedings. The analysis shows that the number of articles has increased over the past five years. This growth is driven not only by developed countries but also by developing countries. Dermatology departments in various hospitals play a significant role in advancing skin cancer detection methods. In addition to identifying publication trends, this study also reveals underexplored areas to encourage new explorations using the VOSviewer and Bibliometrix applications.

Objectives: This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.Methods: Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. Results: The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks.Conclusions: The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Deteksi emosi berbasis teks menjadi salah satu topik utama pembahasan dalam pemrosesan bahasa alami. Hal ini dapat disebabkan karena adanya peningkatan jumlah data yang dihasilkan dari interaksi digital, seperti media sosial dan aplikasi pesan instan. Deteksi emosi bertujuan untuk mengenali dan menganalisis respons emosional individu dalam situasi tertentu, dengan penerapan di berbagai bidang seperti interaksi manusia dengan komputer, analisis sentimen, dan layanan kesehatan mental. Penelitian ini bertujuan untuk meningkatkan performa deteksi emosi berbasis teks Bahasa Indonesia menggunakan model RoBERTa. RoBERTa merupakan salah satu varian dari model BERT (Bidirectional Encoder Representations from Transformers) yang telah dioptimalkan untuk pemrosesan bahasa alami dengan mempertimbangkan konteks linguistik secara lebih mendalam. Implementasi RoBERTa dalam deteksi emosi akan dioptimalkan melalui hyperparameter tuning seperti Grid Search, Randomized Search, dan Bayesian Optimization untuk memperoleh kombinasi nilai learning rate, batch size, dan epoch yang optimal, dengan tujuan meningkatkan akurasi dan kemampuan model dalam mendeteksi emosi secara konsisten. Deteksi emosi menggunakana dataset yang terdiri dari 7080 data tweet publik dengan enam kategori emosi, yaitu anger, fear, joy, love, sad, dan neutral. Hasil eksperimen menunjukkan bahwa metode Bayesian Optimization memberikan kombinasi hyperparameter yang optimal dan meningkatkan performa deteksi emosi pada teks berbahasa Indonesia dengan hasil akurasi sebesar 83,64% dan nilai precision, recall dan F1-score secara berutur-turut adalah 83,75%, 83,64%, dan 83,58%.

Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research aims to improve the precision and clinical reliability of pulmonary abnormality segmentation by developing NCT-CXR, a framework that combines anatomically constrained data augmentation with expert-guided annotation refinement. NCT-CXR applies carefully calibrated discrete-angle rotations (±5°, ±10°) and intensity-based augmentations to enrich training data while preserving spatial and anatomical integrity. To address label noise in the NIH Chest X-ray dataset, we further introduce a clinically validated annotation refinement pipeline using the OncoDocAI platform, resulting in multi-label pixel-level segmentation masks for nine thoracic conditions. YOLOv8 was selected as the segmentation backbone due to its architectural efficiency, speed, and high spatial accuracy. Experimental results show that NCT-CXR significantly improves segmentation precision, especially for pneumothorax (0.829 and 0.804 for ±5° and ±10°, respectively). Non-parametric statistical testing (Kruskal–Wallis, H = 14.874, p = 0.0019) and post hoc Nemenyi analysis (p = 0.0138 and p = 0.0056) confirm the superiority of discrete-angle augmentation over mixed strategies. These findings underscore the importance of clinically constrained augmentation and high-quality annotation in building robust segmentation models. NCT-CXR offers a practical, high-performance solution for integrating deep learning into radiological workflows.

Penelitian ini menyelidiki faktor yang memengaruhi adopsi penggunaan aplikasi galeri virtual GaYa dengan menggunakan Technology Acceptance Model (TAM) sebagai kerangka teori untuk mengeksplorasi hubungan antara Perceived Ease of Use (PEU), Perceived Usefulness (PU), dan Behavioral Intention (BI) dalam konteks aplikasi GaYa. Data yang diperoleh dari 120 responden survei dianalisis menggunakan Generalized Structured Component Analysis (GSCA) untuk mengkaji hubungan antar variabel dalam studi ini. Melalui kalkulasi nilai Critical Ratio (CR), hasil penelitian menunjukkan bahwa PEU memiliki pengaruh signifikan terhadap PU dan BI. Selain itu, PU terbukti menjadi prediktor kuat terhadap BI sehingga menegaskan bahwa pentingnya kegunaan dan nilai fungsional dalam adopsi teknologi. Temuan ini memberikan wawasan berharga bagi praktisi dan pengembang yang ingin meningkatkan efektivitas platform galeri virtual. Hasil studi ini juga menyoroti pentingnya pengembangan desain yang intuitif dan berpusat pada pengguna serta mampu memberikan manfaat nyata bagi pengguna. Keterbatasan dari studi ini terletak pada desain penelitian yang bersifat cross-sectional dan penggunaan sampel yang terbatas secara lokal, yang mungkin membatasi generalisasi temuan ke konteks yang lebih luas. Untuk meningkatkan pemahaman mengenai penerimaan pengguna dalam platform seni digital, penelitian selanjutnya disarankan untuk menggunakan desain longitudinal, menyelidiki faktor tambahan seperti kepercayaan pengguna dan estetika, serta melibatkan partisipan yang lebih beragam. Kata Kunci - Technology Acceptance Model (TAM), Perceived Ease of Use (PEU), Perceived, Usefulness (PU), Behavioral Intention (BI), Virtual Gallery

This research proposes an explanatory deep learning-based music generation approach, where the output of a deep learning model is validated through a set of predefined musical rules, with a refinement process applied when inaccuracies are detected. The study focuses on gamelan, a traditional form of Indonesian music. A Long Short-Term Memory (LSTM) network is used to generate musical compositions, while a modified Genetic Algorithm (GA), omitting the selection and crossover operators, performs validation and, when necessary, refinement via mutation. The LSTM network produces initial compositions, and the GA module ensures compliance with musical rules, enhancing both explainability and creativity. The model successfully generates new bars and lines with notation sequences not found in the original dataset, indicating creative variation. Whether produced directly by the LSTM or refined through GA, the generated output demonstrates the system’s ability to innovate while preserving core musical characteristics. Furthermore, the GA-based validation allows the generated music to be interpreted in terms of the underlying rule constraints. Statistical analysis, including correlation and regression tests using the t-test, reveals a near-perfect relationship between the original and generated compositions. These findings support the effectiveness of the proposed Automatic Music Generation (AMG) model in fostering creative exploration of new tonal patterns aligned with the target genre.