An advanced machine learning (ML)-driven web platform was developed and deployed to automate the identification of rice insect pests, addressing limitations associated with traditional pest detection methods and conventional ML algorithms. Historically, pest identification in rice cultivation has relied on expert evaluation of pest species and their associated crop damage, a process that is labor-intensive, time-consuming, and prone to inaccuracies, particularly in the misclassification of pest species. In this study, a subset of the publicly available IP102 benchmark dataset, consisting of 7,736 images across 12 rice pest categories, was curated for model training and evaluation. Two classification models—a Support Vector Machine (SVM) and a deep Convolutional Neural Network (CNN) based on the Inception_ResNetV2 architecture—were implemented and assessed using standard performance metrics. Experimental results demonstrated that the Inception_ResNetV2 model significantly outperformed SVM, achieving an accuracy of 99.97%, a precision of 99.46%, a recall of 99.81%, and an F1-score of 99.53%. Owing to its superior performance, the Inception_ResNetV2 model was integrated into a web-based application designed for real-time pest identification. The deployed system exhibited an average response time of 5.70 seconds, representing a notable improvement in operational efficiency and usability over previous implementations. The results underscore the potential of artificial intelligence in transforming agricultural practices by enabling accurate, scalable, and timely pest diagnostics, thereby enhancing pest management strategies, mitigating crop losses, and supporting global food security initiatives.

Accurate forecasting of Gross Domestic Product (GDP) growth remains essential for supporting strategic economic policy development, particularly in emerging economies such as Indonesia. In this study, a hybrid predictive framework was constructed by integrating fuzzy logic representations with machine learning algorithms to improve the accuracy and interpretability of GDP growth estimation. Annual macroeconomic data from 1970 to 2023 were utilised, and 19 input features were engineered by combining numerical economic indicators with fuzzy-based linguistic variables, along with a forecast label generated via the Non-Stationary Fuzzy Time Series (NSFTS) method. Six supervised learning models were comparatively assessed, including Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Huber Regressor, Decision Tree (DT), and Multilayer Perceptron (MLP). Model performance was evaluated using Mean Absolute Error (MAE) and accuracy metrics. Among the tested models, the RF algorithm demonstrated superior performance, achieving the lowest MAE and an accuracy of 99.45% in forecasting GDP growth for 2023. Its robustness in capturing non-linear patterns and short-term economic fluctuations was particularly evident when compared to other models. These findings underscore the RF model's capability to serve as a reliable tool for economic forecasting in data-limited and volatile macroeconomic environments. By enabling more precise GDP growth predictions, the proposed hybrid framework offers a valuable decision-support mechanism for policymakers in Indonesia, contributing to more informed resource allocation, proactive economic intervention, and long-term development planning. The methodological innovation of integrating NSFTS with machine learning extends the frontier of data-driven macroeconomic modelling and provides a replicable template for forecasting applications in other emerging markets.