This study examines public responses on X to the 3-Kg LPG retail ban implemented on February 1, 2025, and revoked on February 4, 2025, which caused widespread shortages, long queues, and limited access, particularly for citizens living far from official distribution points. A total of 2,524 Indonesian-language tweets were collected via crawling and systematically processed through text cleaning, tokenization, normalization, stopwords removal, and stemming, followed by automatic labeling using the Indonesian Sentiment (InSet) Lexicon. After removing 229 neutral tweets, 1,405 tweets (61.2%) were classified as negative and 890 tweets (38.8%) as positive, with the study focusing on these two sentiment classes. Text features were extracted using TF-IDF, and classification was conducted using a linear-kernel Support Vector Machine (C = 0.1) with an 80:20 train-test split. The model achieved an overall accuracy of 84%, with precision, recall, and F1-score of 0.82, 0.94, and 0.88 for the negative class, and 0.87, 0.68, and 0.76 for the positive class. Results indicate that negative sentiment was dominated by criticism related to LPG shortages and insufficient policy communication, while positive sentiment reflected user relief over restored supply and hopes for fairer distribution in the future. These findings suggest that revoking the ban did not fully restore public perception, highlighting the necessity for more effective policy dissemination and stricter monitoring of 3-Kg LPG distribution. The study also emphasizes the importance of leveraging social media, particularly X, as a real-time source for monitoring public opinion and evaluating the effectiveness of energy distribution policies in Indonesia.

This research investigates the strategic identification of new export destinations for Indonesian textile products by integrating international market segmentation and product competitiveness analysis. The study employs clustering techniques (K-Means, K-Medoids, and Hierarchical) validated through Silhouette and Davies-Bouldin indices to classify 149 countries based on trade indicators (import growth, trade balance, global market share), economic indicators (population, purchasing power parity, industrial proportion to GDP), and trade barrier indicators (logistics performance index, geographic distance, free trade agreements). Complementarily, the Revealed Comparative Advantage (RCA) framework is applied to evaluate Indonesia’s product-level competitiveness in the global textile market. The results reveal that export opportunities are can be concentrated in 20 countries across Europe, Asia, Africa, the Caribbean, and Melanesia, characterized by positive import growth, significant trade deficits, large market capacities, and relatively low trade barriers. Moreover, Indonesia demonstrates high comparative advantages in artificial and synthetic fibbers, wigs, and leather footwear, while apparel products such as suits, shirts, knitwear, and brassieres represent moderately competitive but globally demanded items. The study concludes that Indonesia’s export strategy should balance high purchasing power markets and emerging economies with high import dependency.

Healthcare services in Indonesia currently need to be improved given Indonesia's dense population, which results in patient queues at health service facilities. This is due to several factors, one of which is the manual processing of health data, as is the case at the Sidhi Sai Pratama Clinic. This research aims to improve healthcare services and provide easy access to information for both clinic staff and patients. The stages of this research method are needs analysis, system design, implementation, and testing. In the needs analysis stage, data was collected through direct observation and interviews with one of the clinic staff. The system design stage was carried out by creating a system flowchart and database model required to ensure the clinic's needs for the system were met. The results of the study showed that the system can run effectively in terms of managing patient data, patient medical records, and managing medication data. Based on the results of testing using the black box testing method, all features in the system are functioning well according to the objectives. With this system, it is hoped that the problem of patient queues can be overcome by providing effective and efficient healthcare services

Braille plays a crucial role in supporting literacy for individuals with visual impairments. However, converting Braille documents into digital text remains a technical challenge, particularly in accurately segmenting Braille dots from scanned images. This study aims to evaluate and compare the effectiveness of several classical image segmentation techniques—namely Otsu, Otsu Inverse, Otsu Morphology, and Otsu Inverse Morphology—in enhancing Braille image pre-processing. The methods were tested using a set of Braille image datasets and evaluated based on six quantitative image quality metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Edge Similarity Index (ESSIM). The results show that the Otsu Morphology method achieved the highest PSNR (27.6798) and SSIM (0.5548), indicating superior image fidelity and structural preservation, while the standard Otsu method yielded the lowest MSE (113.3485).These findings demonstrate that applying morphological operations in combination with thresholding significantly enhances the segmentation quality of Braille images, supporting better accuracy in subsequent recognition tasks. This approach offers a practical and efficient alternative to deep learning models, particularly for resource-constrained systems such as portable Braille readers.

The detection of cryptographic attacks is a vital aspect of maintaining cybersecurity, especially as digital infrastructures become increasingly intricate and susceptible to sophisticated threats. This systematic review aims to examine and compare a range of machine learning approaches applied to cryptographic attack detection, focusing on their performance in terms of detection rates, efficiency, and overall effectiveness. A comprehensive review and meta-analysis were conducted, focusing on existing research that utilized machine learning models for identifying cryptographic attacks. The models included in the review were Naïve Bayes, C4.5, Random Forest, Decision Tree, K-Means, and Particle Swarm Optimization (PSO) combined with Neural Networks. Studies were selected based on their relevance to cryptographic security, with particular attention paid to performance metrics like classification accuracy, precision, recall, and area under the curve (AUC). The findings indicated that the C4.5 decision tree model achieved a high classification rate of 98.8%, while both Random Forest and Decision Tree models performed with an accuracy of 99.9%, making them highly suitable for real-time attack detection. Additionally, the PSO + Neural Network model showed enhanced detection precision, illustrating the value of integrating optimization techniques with machine learning models. The use of machine learning, especially with ensemble methods such as Random Forest and Decision Trees, proves to be highly effective for cryptographic attack detection. The study underscores the necessity for customized machine learning solutions in cybersecurity, balancing both high accuracy and operational efficiency. Further research should focus on the real-world deployment of hybrid models to confirm their practical effectiveness.

Machine learning offers promising solutions for the recognition of handwritten Javanese Nglegena script, which is crucial for preserving Indonesia's cultural heritage. This study explores the application of several supervised learning algorithms-K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest-for classifying handwritten images of Nglegena Javanese script. Feature extraction is performed using a zoning technique, where each character image is divided into multiple zones (16, 25, 36, and 64) to capture local details. The extracted features are further processed using normalization methods, including Min-Max, Z-Score, and Binary normalization, to ensure uniform data distribution. The dataset, consisting of 600 images representing Javanese Nglegena characters, is split into training and testing sets using various ratios. Experimental results show that the combination of Naïve Bayes classification, 36-zone feature extraction, and Min-Max or Z-Score normalization achieves the highest accuracy of 65%. These findings demonstrate that optimizing zoning and normalization can significantly enhance the accuracy of machine learning models for Javanese script recognition. The research contributes to developing Optical Character Recognition (OCR) technology for Javanese script, supporting the digital preservation of Indonesia's historical and cultural heritage.

The classification of white blood cells (WBC) plays a critical role in haematological diagnostics, yet manual examination remains a labour-intensive and subjective process. In response to this challenge, this study investigates the application of deep learning, specifically the ResNet18 convolutional neural network architecture, for the automated classification of blood cell images into four classes: eosinophils, lymphocytes, monocytes, and neutrophils. The dataset used comprises microscopic images annotated by cell type and is divided into training and validation sets with an 80:20 ratio. Standard pre-processing techniques such as image normalization and augmentation were applied to enhance model robustness and generalization. The model was fine-tuned using transfer learning with pre-trained weights from ImageNet and optimized using the Adam optimizer. Performance was evaluated through a comprehensive set of metrics including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). The best model achieved a validation accuracy of 86.89%, with macro-averaged precision, recall, and F1-score of 0.8738, 0.8690, and 0.8688, respectively. Lymphocyte classification yielded the highest F1-score (0.9515), while eosinophils posed the greatest classification challenge, as evidenced by lower precision and higher misclassification rates in the confusion matrix. Error-based evaluation further supported the model’s consistency, with an MSE of 0.7125 and RMSE of 0.8441. These results confirm that ResNet18 is capable of learning discriminative features in complex haematological imagery, providing an efficient and reliable alternative to manual analysis. The findings suggest potential for practical implementation in clinical workflows and pave the way for further research involving multi-model ensembles or cell segmentation pre-processing for improved precision

Introduction: Mental disorders such as bipolar disorder are becoming increasingly prominent, particularly with the rise of emotional expression through social media. Early detection remains a significant challenge due to the lack of non-invasive, real-time assessment methods. Methods: This study proposes a hybrid deep learning approach combining Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and the Cox Proportional Hazards (Cox PH) model to analyze the risk and timing of bipolar disorder onset. A dataset of 3,511 tweets from 517 Twitter users was collected. The CNN-LSTM model classified bipolar risk levels based on text data, while the Cox PH model estimated the time-to-event for high-risk conditions using behavioral features and predicted risk labels. Results: The hybrid model demonstrated strong predictive performance. The risk label significantly influenced the time to high-risk condition (hazard ratio = 5.39, p < 0.005). The model achieved a concordance index (C-index) of 0.816, indicating high reliability in survival prediction. Conclusions: This case study highlights the potential of integrating deep learning and survival analysis for early bipolar disorder detection using social media data. The proposed non-invasive method can support mental health monitoring while raising awareness of ethical and privacy considerations

Tomato productivity is often disrupted by diseases affecting tomato plants, such as early blight and late blight, which can significantly reduce crop yields. Early detection of these diseases is crucial to prevent greater losses. This study compares two machine learning-based classification methods, namely Random Forest and Naïve Bayes, in identifying diseases on tomato leaves. The dataset used consists of 1,255 images obtained from Kaggle, with the data divided into two classes: early blight with 627 images and late blight with 628 images, which then underwent preprocessing and data splitting with three ratio scenarios (70:30, 80:20, and 90:10) for training and testing. This study shows that it only achieved an accuracy of 76.98%, while the Random Forest method had the highest accuracy of 92.86% in the 90:10 data ratio scenario. Thus, the Random Forest method proves to be more effective in classifying tomato leaf diseases compared to Naïve Bayes. The implementation of this model can help farmers detect diseases more quickly and accurately, thereby increasing agricultural productivity.

Lontara script is a traditional writing system of the Bugis-Makassar people in South Sulawesi, used to write the Bugis, Makassar, and Mandar languages. This system is based on an abugida, in which each letter represents a consonant with an inherent vowel. It was once used to record history, customary law, and literature, but its use has declined due to the influence of the Latin alphabet. Today, the Lontara script is preserved through education and digitization as part of the cultural heritage of the Indonesian archipelago. In this article, the researchers attempt to use a dataset of handwritten Lontara Bugis-Makassar characters. The process begins with the collection of character datasets, which are then processed through Canny segmentation and Hu Moment feature extraction to obtain a representation of the shape that is invariant to rotation and scale. The processed data was divided into training and testing data, then classified using the K-NN, Decision Tree, and Random Forest algorithms. The results showed that the KNN algorithm with 6 neighbors achieved the highest accuracy, precision, and recall of 98%. The Decision Tree algorithm achieved an accuracy of 96.67%, precision of 96.22%, recall of 95.33%, and an F1-score of 95.98%. Meanwhile, Random Forest showed an accuracy of 96.67%, precision of 96.34%, recall of 96%, and an F1-score of 95.98%.