The classification of animal vocalizations through bioacoustic analysis has become a crucial tool in wildlife monitoring and conservation. This study presents a Systematic Literature Review (SLR) focused on the use of Convolutional Neural Networks (CNNs) for animal sound classification, synthesizing insights from 21 peer-reviewed journal articles published between 2016 and 2024. The review investigates the effectiveness of CNN models, the impact of different architectures (e.g., ResNet, VGG, MobileNet, Xception), commonly used evaluation metrics, and the advantages and limitations of CNN-based approaches. Results show that CNNs consistently outperform traditional classifiers by leveraging spectrogram-based feature extraction techniques and deep feature learning, achieving high classification accuracy across birds, mammals, amphibians, and marine species. Lightweight CNNs provide viable alternatives for real-time and resource-constrained applications, while ensemble learning and transfer learning further enhance model performance. Nonetheless, challenges persist, including computational cost, interpretability, and domain-specific generalization. This review underscores CNNs' growing role in ecological research and highlights areas for future improvement, particularly in data-scarce environments. The findings contribute to guiding future implementations and innovations in automated animal sound classification systems.

Modeling rainfall is crucial for hydrological studies and climate adaptation, especially in regions with complex topography such as the Toba Lake area, North Sumatra. Classical probability distributions often struggle to represent skewness, heavy tails, and variability observed in tropical rainfall. This study explores APTXL distribution as a flexible two-parameter model. Through the alpha power transformation, APTXL extends the X-Lindley distribution by introducing an additional shape parameter, allowing better accommodation of asymmetrical and extreme values while maintaining analytical tractability. Statistical properties are derived, and parameters are estimated using maximum likelihood. The model is applied to a long-term dataset from 13 meteorological stations, covering 408 monthly observations per station. Comparative analysis against Gamma, Lognormal, and Generalized Extreme Value distributions using multiple goodness-of-fit criteria indicates that APTXL provides consistently improved performance. These results suggest APTXL as a practical tool for rainfall modeling and water-resource applications in climate-sensitive regions.

As viruses evolve rapidly, variations in their DNA may arise due to environmental factors. This study examines the classification of COVID-19 DNA sequences based on their country of origin and analyzes their primary correlation with the country’s international travel policy. Focusing on DNA sequences from nine ASEAN countries, we conducted a two-class classification to distinguish sequences from individual countries and mixed sequences from others. The sequences were initially dissected into 200 base pair units, and a deep-learning method was employed to construct a model. Our results showcase the capacity to differentiate DNA sequences with varying accuracy for each country. Additionally, the index of international travel policy, which reflects how countries implemented varying levels of restrictions regarding inbound travel, several months before the sequence collection date, moderately correlated with the classification accuracy within each country. This finding suggests a preliminary insight that a country’s pandemic management might influence the variation in the DNA virus, determining whether these sequences will evolve distinctly from those of other countries or exhibit similarities.

Skin diseases are health problems that require accurate diagnosis to evaluation and ultimately leading to treatment decisions. One of the crucial roles in the diagnostic process is medical imaging. Machine learning technology can assist in classifying skin diseases using image data and achieving high levels of accuracy in diagnosis. The purpose of this research is to review machine learning algorithms that can be utilized to develop image-based skin disease classification systems. The methodology employed is a Systematic Literature Review (SLR), which can be used to provide a comprehensive review of the application of machine learning in the classification of skin diseases. The literature search strategy was based on the Boolean technique, applied to the Scopus database. The selected articles were screened using predefined inclusion and exclusion criteria. The results indicate that the most used machine learning algorithm with achieved the highest classification accuracy is the Convolutional Neural Network (CNN). Keywords - Skin Disease, Machine Learning, Classification, CNN.

Wildfires pose increasing environmental and socio-economic risks, particularly in climate-sensitive and tropical regions, necessitating reliable satellite-based monitoring and predictive frameworks. This study presents a comprehensive survey of satellite-derived wildfire indicators and spatiotemporal modeling approaches, covering their historical development, current methodologies, and emerging research directions. We review major active fire and hotspot datasets derived from MODIS, VIIRS, and related platforms, along with key environmental drivers such as vegetation indices, meteorological variables, and land-surface with a specific case study for the Indonesian region. Modeling approaches are synthesized from classical statistical regression and time-series analysis to contemporary machine learning and deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models. The analysis highlights the transition toward multi-source data integration and spatiotemporal deep learning frameworks capable of capturing complex wildfire dynamics. Finally, we identify future research challenges, including hybrid physical–AI modeling, uncertainty quantification, and scalable real-time wildfire intelligence systems. This survey provides a structured reference for researchers and practitioners seeking to advance satellite-based wildfire monitoring and prediction.

The development of Indonesia's new capital city, Ibu Kota Nusantara (IKN), is an innovative government policy that has sparked diverse public responses. This study aims to explore sentiment trends on the social media platform X to understand public perceptions of the policy. Additionally, a sentiment classification model combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) was developed and optimized through hyperparameter tuning. Exploratory analysis showed that positive sentiment dominated at 46%, followed by negative at 30% and neutral at 24%. The classification model achieved a test accuracy of 78% and an average accuracy of 81% across 10-fold cross-validation, with a standard deviation of 0.006. The achieved accuracy, together with the low cross-validation standard deviation, indicates that the BiLSTM-CNN model demonstrates stable and reliable performance.