Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.

Overweight and obesity are health problems caused by the accumulation of excess fat due to increased food intake and decreased physical activity. The accumulation of excess fat can trigger an inflammatory process resulting in metabolic disease. Research to examine how the application of diet and consumption of supplements in the productive age group who are overweight and obese. This study uses a quantitative descriptive method with observational data collection techniques and questionnaires. The research subjects are fitness members of productive age who are overweight and obese. Research instruments include questionnaires and blood sampling. The results of the study were that 17 subjects (81%) were obese and 4 subjects (19%) were overweight. All subjects received GDS levels <200 mg/dl. 13 subjects were experiencing uric acid >7.0 mg/dl and 8 female subjects having >5.7 mg/dl. A total of 13 subjects (62%) were not taking medication or dietary supplements and 8 subjects (38%) were taking medication or dietary supplements. Subjects who are overweight and obese do not experience health problems related to blood sugar levels at any time. Health problems that arise that are related to hyperuricemia occur in male subjects with levels (7.1-10.5 mg/dl) and women (5.9-8.4 mg/dl). Lack of understanding and information related to diet as well as recommendations for consuming supplements results in excessive protein intake, resulting in inflammation which is characterized by high uric acid levels.

Overweight and obesity are health problems caused by the accumulation of excess fat due to increased food intake and decreased physical activity. The accumulation of excess fat can trigger an inflammatory process resulting in metabolic disease. Research to examine how the application of diet and consumption of supplements in the productive age group who are overweight and obese. This study uses a quantitative descriptive method with observational data collection techniques and questionnaires. The research subjects are fitness members of productive age who are overweight and obese. Research instruments include questionnaires and blood sampling. The results of the study were that 17 subjects (81%) were obese and 4 subjects (19%) were overweight. All subjects received GDS levels <200 mg/dl. 13 subjects were experiencing uric acid >7.0 mg/dl and 8 female subjects having >5.7 mg/dl. A total of 13 subjects (62%) were not taking medication or dietary supplements and 8 subjects (38%) were taking medication or dietary supplements. Subjects who are overweight and obese do not experience health problems related to blood sugar levels at any time. Health problems that arise that are related to hyperuricemia occur in male subjects with levels (7.1-10.5 mg/dl) and women (5.9-8.4 mg/dl). Lack of understanding and information related to diet as well as recommendations for consuming supplements results in excessive protein intake, resulting in inflammation which is characterized by high uric acid levels.

Overweight and obesity are health problems caused by the accumulation of excess fat due to increased food intake and decreased physical activity. The accumulation of excess fat can trigger an inflammatory process resulting in metabolic disease. Research to examine how the application of diet and consumption of supplements in the productive age group who are overweight and obese. This study uses a quantitative descriptive method with observational data collection techniques and questionnaires. The research subjects are fitness members of productive age who are overweight and obese. Research instruments include questionnaires and blood sampling. The results of the study were that 17 subjects (81%) were obese and 4 subjects (19%) were overweight. All subjects received GDS levels <200 mg/dl. 13 subjects were experiencing uric acid >7.0 mg/dl and 8 female subjects having >5.7 mg/dl. A total of 13 subjects (62%) were not taking medication or dietary supplements and 8 subjects (38%) were taking medication or dietary supplements. Subjects who are overweight and obese do not experience health problems related to blood sugar levels at any time. Health problems that arise that are related to hyperuricemia occur in male subjects with levels (7.1-10.5 mg/dl) and women (5.9-8.4 mg/dl). Lack of understanding and information related to diet as well as recommendations for consuming supplements results in excessive protein intake, resulting in inflammation which is characterized by high uric acid levels.

Overweight and obesity are health problems caused by the accumulation of excess fat due to increased food intake and decreased physical activity. The accumulation of excess fat can trigger an inflammatory process resulting in metabolic disease. Research to examine how the application of diet and consumption of supplements in the productive age group who are overweight and obese. This study uses a quantitative descriptive method with observational data collection techniques and questionnaires. The research subjects are fitness members of productive age who are overweight and obese. Research instruments include questionnaires and blood sampling. The results of the study were that 17 subjects (81%) were obese and 4 subjects (19%) were overweight. All subjects received GDS levels <200 mg/dl. 13 subjects were experiencing uric acid >7.0 mg/dl and 8 female subjects having >5.7 mg/dl. A total of 13 subjects (62%) were not taking medication or dietary supplements and 8 subjects (38%) were taking medication or dietary supplements. Subjects who are overweight and obese do not experience health problems related to blood sugar levels at any time. Health problems that arise that are related to hyperuricemia occur in male subjects with levels (7.1-10.5 mg/dl) and women (5.9-8.4 mg/dl). Lack of understanding and information related to diet as well as recommendations for consuming supplements results in excessive protein intake, resulting in inflammation which is characterized by high uric acid levels.

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.

Salah satu permasalahan kesehatan yang sering ditemui di banyak negara termasuk Indonesia adalah stunting. Stunting telah mendapat banyak perhatian di Indonesia, terlihat dari alokasi APBN masing-masing sebesar Rp48,3 triliun dan Rp49,4 triliun pada tahun 2022 dan 2023 untuk bidang ini. Pada tahun 2022, Kementerian Kesehatan merilis temuan dari Survei Status Gizi Indonesia (SSGI) yang menyatakan bahwa angka stunting di Indonesia mencapai 21,6% pada saat Rapat Kerja Nasional BKKBN pada 25 Januari 2023.Hal ini menunjukkan pentingnya untuk mengerti pemahaman mendalam tentang faktor-faktor yang mengidentifikasi anak-anak berisiko tinggi terkena stunting. Banyak penelitian sebelumnya yang membahas faktor resiko stunting, namun masih sedikit penerapannya dalam metode machine learning, dalam data yang kompleks dan tidak seimbang.Penelitian ini mengevaluasi kinerja dari berbagai metode machine learning yang bertujuan dapat memberikan kontribusi penting dalam bidang kesehatan anak dan analisis data. Diantara metode machine learning yang dipilih metode Bagging Decision Tree mendapatkan nilai accuracy yang terbaik sebesar 78,93%, precision 78% dan recall sebesar 77,99%. Dalam penelitian ini menunjukkan bahwa metode ensemble learning mampu bekerja dengan baik dalam atribut multiclass dan data yang tidak seimbang pada dataset pertumbuhan balita.

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.