Deep Neural Networks for Intelligent Voice Authentication Systems in Large-Scale Electronic Voting

The authentication of eligible voters is an area of concern that needs further exploration of the prospects of electronic voting systems. The integration of voice authentication in electronic voting systems for varying numbers of disabled and prospective voters should be secure, scalable, and suitable in both federal and state elections. Machine learning (ML) is an evolving field of computing that presents prospects in electronic voting. Applying ML algorithms to electronic voting provides optimal solutions to a wide range of biometric authentication challenges. This paper presents the design of an effective voice classification algorithm from a narrower perspective that can be used in developing prototype electronic voting systems in large-scale voting scenarios, particularly for disabled voters. Applying the knowledge of deep neural networks, a three hidden layer network using a feed-forward architecture is designed for classifying voice data acquired from prospective voters. The proposed design is tested on two different datasets and is adapted to handle small and vast amounts of voters’ voice information. Results indicated average training and average validation accuracies of 92% and 97% respectively for both deep learning models for inclusivity and accountability of disabled voters in secure electronic voting systems

Development of a Littering Behavior Detection Using 3D Convolutional Neural Networks (3D CNN)

Littering has become a significant problem that negatively impacts public health and environmental cleanliness. This research introduces an innovative solution using 3D Convolutional Neural Networks (3D CNN) technology to automatically detect littering behavior through real-time CCTV recordings. Two models were developed and tested. Model 1, which employs Conv3D, Batch Normalization, and Dropout, showed high training accuracy but exhibited fluctuations in validation accuracy, indicating potential overfitting. In contrast, Model 2, designed with a simpler structure without Batch Normalization and Dropout, achieved higher classification accuracy and efficiency. Both models significantly contribute to addressing littering in public areas, increasing awareness, and supporting environmental law enforcement. The integration of 3D CNN technology in detecting littering behavior demonstrates its potential to reduce pollution and promote environmentally responsible behavior. &nbsp

TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions

In 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called Tele-OTIVA. The Tele-OTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, Tele-OTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The Tele-OTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, Tele-OTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis

Emotion Classification in Indonesian Text Using IndoBERT

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts

Anxiety Detection for Autism Children through Vital Signs Monitoring using a Socially Assistive Robot

Socially Assistive Robot (SAR) to detect anxiety levels in children with Autism Spectrum Disorder (ASD), a condition often accompanied by difficulties in recognising and expressing emotions, including anxiety. Early recognition of anxiety in children with Autism Spectrum Disorder (ASD) is crucial as it can affect their behaviour and social interactions. This SAR monitors vital signs namely blood pressure, heart rate and body temperature. This study involved children with Autism Spectrum Disorder (ASD) with two conditions, namely Asperger Syndrome and Classical Autism who interacted with a Socially Assistive Robot (SAR) equipped with a tensimeter (MPS20N0040D sensor) for blood pressure, MAX30100 sensor for heart rate, and MLX90614 sensor to measure body temperature. Results show that the Socially Assistive Robot (SAR) is able to measure vital signs with high accuracy and provide an indication of anxiety levels effectively, as vital signs correlate with anxiety levels. These findings demonstrate the potential of the Socially Assistive Robot (SAR) as a reliable tool in anxiety monitoring in children with ASD, with important implications for the development of future therapeutic intervention

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis

Enhanced Short-Term Residential Load Forecasting Using K-means Clustering and Iterative Residual LSTM Networks

Accurate short-term load forecasting (STLF) is essential for optimizing energy management systems, ensuring operational efficiency, and balancing supply and demand in power grids. This study introduces a hybrid model, K-RNLSTM, which integrates K-means clustering with iterative Residual Long Short-Term Memory (LSTM) networks to improve prediction accuracy. The K-means clustering algorithm categorizes similar load patterns, allowing the model to handle seasonal and hourly variations more effectively. Iterative ResBlocks are incorporated within the LSTM framework to capture complex non-linear dependencies and improve the learning process without suffering from degradation. The model was evaluated using real-world residential electricity consumption data across four seasons: winter, spring, summer, and autumn. The K-RNLSTM model consistently outperformed traditional methods such as Extreme Learning Machines (ELM), Seasonal-Trend Loess (STL), Gated Recurrent Units (GRU), and standard LSTM in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results demonstrated that K-RNLSTM achieved an average RMSE of 0.71, MAE of 0.43, and MAPE of 1.31%, surpassing benchmark models across all seasonal variations. Furthermore, the integration of ResBlocks significantly improved the model's ability to minimize large forecasting errors, particularly during peak demand periods. This research demonstrates the effectiveness of combining clustering techniques with deep learning models for short-term load forecasting, offering a robust solution for power system operators to optimize energy distribution and reduce operational costs

Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split

CLUSTER ANALYSIS OF OBESITY RISK LEVELS USING K-MEANS AND DBSCAN METHODS

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon  and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity

MRI-Based Brain Tumor Instance Segmentation Using Mask R-CNN

Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor segmentation using Mask R-CNN, a state-of-the-art deep learning model for instance segmentation. Our method leverages MRI images to identify and delineate brain tumors with high precision. We trained the Mask R-CNN model on a dataset of annotated MRI images and evaluated its performance using the mean Average Precision (mAP) metric. The results demonstrate that our model achieves a high mAP of 90.3%, indicating its effectiveness in accurately segmenting brain tumors. This automated approach not only reduces the manual effort required for tumor segmentation but also provides consistent and reliable results, potentially improving clinical outcomes