Increasing global warming can potentially increase the intensity of ENSO and IOD extreme phenomena in the future, which could increase the potential for wildfires. This study aims to develop a hotspot prediction model in the Kalimantan region using climate indicators such as precipitation and its derivatives, ENSO and IOD. The hotspot prediction model was developed using Principal Model Analysis (PMA) as the initial model basis. The overall model performance is evaluated using the concept of Cross-Validation. Furthermore, the model’s performance will be improved using the Bayesian Inference principle so that the average performance increases from 28.6% to 61.1% based on the model’s coefficient of determination ($R^2$). The character of each year in the model development process is also evaluated using the concept of cross validation. Since the climate indicator we used was integrated with the ENSO and IOD index, model performance is strongly influenced by the ENSO and IOD phenomena. To obtain better performance when estimating future forest fires (related to El Niño and positive IOD), years with a high number of hotspots and coinciding with the occurrence of El Niño and IOD are better used as early model years (PMA). However, the model tends to overestimate the hotspot value, especially with a lower strength El Niño and positive IOD. Therefore, years with a low number of hotspots, as in normal years and La Niña, are better used in the model performance improvement stage (Bayesian Inference) to correct the overestimation.

Angka harapan hidup menggambarkan rata-rata lamanya waktu seseorang hidup sejak lahir di dunia. Angka harapan hidup menjadi salah satu aspek dalam menentukan indeks pembangunan manusia. Semakin tinggi Angka harapan hidup maka akan semakin tinggi nilai IPM. Tujuan penelitian ini adalah memprediksi angka harapan hidup melalui model yang paling akurat dengan menggunakan model decision tree regression, random forest regression, gradient boosting regression, dan XGBoost regression, serta analisis variabel penjelas yang paling mempengaruhi angka harapan hidup. Data yang digunakan dalam penelitian ini adalah dataset Global Country Information Dataset 2023. Data diperoleh dari situs Kaggle. Berdasarkan analisis diperoleh bahwa model random forest regression menunjukkan kinerja yang lebih unggul dalam memprediksi hasil, yang ditunjukkan dengan nilai RMSE yang lebih rendah dan nilai R² yang lebih tinggi. Kematian bayi dan rasio kematian ibu secara konsisten diidentifikasi sebagai prediktor yang signifikan di semua model, sedangkan populasi merupakan prediktor yang kurang memprengaruhi angka harapan hidup.

Prediksi Angka Harapan Hidup Menggunakan Regresi Linear Berganda, Lasso, Ridge, Elastic Net, dan Kuantil Lasso

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.