Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi

A Multi-Task Learning Approach for News Classification and Number of Reader Prediction

2025-07-04T21:35:26+07:00

The large volume of online news content presents a challenge in effectively managing and organizing information, especially regarding enhancing literacy rates in Indonesia. As the amount of news articles continues to grow, there is a need for a robust system that can categorize news and predict the number of readers to assess its impact on literacy. This study introduces a Multi-Task Learning (MTL) approach, utilizing data from online websites to simultaneously address news classification and reader prediction tasks. Cross-entropy loss is applied in the model to handle the class imbalance issue. The research compares two performance MTL architectures, the Dense architecture and the CNN architecture. The experiments assess the models' abilities to classify news and predict reader numbers. The results show that the Dense architecture outperforms the CNN architecture, achieving 94% accuracy and a 99% AUC-ROC score, whereas the CNN model achieved 91% accuracy and a 98% AUC-ROC score. This study highlights the effectiveness of the Dense architecture in classifying online news and predicting reader engagement. The findings provide valuable insights for enhancing news sorting systems and could contribute to improving literacy initiatives in Indonesia by offering more accurate predictive models for online news consumption. The results indicate that integrating Multi-Task Learning into news classification systems can enhance content management and offer a deeper understanding of public interaction with news.

The Design and Empirical Analysis of Smart Tire Monitoring System using Cloud and Docker Container Technology

2025-07-04T21:35:38+07:00

Modern vehicles rely on Tire Pressure Monitoring Systems (TPMS) to improve safety and enhance the driving experience by monitoring tire pressure and temperature. Traditionally, TPMS systems rely on specialized hardware to collect and transmit data to the vehicle’s onboard computer, making this information accessible solely to the driver. This study proposes an enhanced TPMS that leverages Cloud and Docker Container technologies. The Message Queuing Telemetry Transport (MQTT) protocol enables efficient communication between a cloud server and a central controller. At the same time, Docker containers support streamlined application integration and deployment. Findings indicate optimal standard deviations for tire pressure at 1.8 during the day and 1.2 at night, with temperature deviations at 1.54 during the day and 1.23 at night, showing minimal fluctuation. This setup supports real-time, remote monitoring of tire data, accessible through a smartphone interface. This system offers significant practical benefits, including improved driver awareness and preventive maintenance capabilities. However, potential challenges such as data security, system reliability, and integration with existing vehicle infrastructure warrant further investigation to ensure widespread adoption and effectiveness.

Enhancing Facial Image Restoration Using CNN for Blur Severity Classification and U-Net for Deblurring

2025-07-04T21:35:49+07:00

Blurring facial images can significantly degrade the performance of face recognition and video surveillance applications. Therefore, an effective image restoration method is essential to address this issue. However, existing methods struggle with varying levels of blur severity, limiting their effectiveness. This study proposes a facial image restoration approach that integrates blur severity classification using a Convolutional Neural Network (CNN) with a U-Net-based deblurring model to overcome these challenges. This method ensures that each blurred image is processed using the most suiTable deblurring model, optimizing the restoration process. The dataset used in this study is Flickr-Faces-HQ (FFHQ), to which a Gaussian blur is applied and categorized into five levels: very low, low, medium, high, and very high. They employ the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) as quantitative metrics to evaluate the model's performance. Experimental results show that the proposed method consistently outperforms fixed kernel size and multi-kernel size deblurring approaches across all blur severities. Specifically, our method achieves a PSNR of 40.001 dB for very low blur severity and an SSIM of 0.990. For low severity, it attains a PSNR of 31.104 dB and an SSIM of 0.946. For medium severity, the results are 27.995 dB (PSNR) and 0.874 (SSIM). At high severity, our model achieves a PSNR of 28.896 dB and an SSIM of 0.855. Finally, for very high severity, the PSNR drops to 26.566 dB, with an SSIM of 0.812. These results demonstrate the effectiveness of the proposed method in enhancing image clarity across different blur levels while preserving facial details. This research contributes to the development of more adaptive and efficient image restoration techniques, particularly for applications that require high-quality facial images, such as face recognition and video surveillance.

Hybrid Multi-Servo Motor Controller Within an IoT-Enabled Smart Mechatronics Framework

2025-07-04T21:36:07+07:00

The increasing demand for precise motor control in industrial automation and IoT-integrated applications has driven the development of hybrid control systems for multi-servo motor management. Existing solutions often rely solely on either IoT-based automation or standalone manual control, limiting adaptability in environments with unreliable network connectivity. This study proposes a hybrid control system that integrates local potentiometer-based control with IoT-enabled remote operation to enhance flexibility and reliability. An experimental approach is employed to design and evaluate the hybrid control system, utilizing a modular controller board and MQTT as an IoT communication protocol. The system’s performance is assessed based on response time and synchronization accuracy under varying network conditions. Experimental findings demonstrate that the proposed system effectively balances remote accessibility while ensuring on-site reliability. The integration of MQTT QoS level 2 enhances real-time performance by ensuring the accurate delivery of messages. Measured delays range from 21.72 ms to 55.61 ms, with jitter values between 1.17 ms and 33.89 ms, highlighting the impact of data traffic on control precision. By addressing latency, synchronization, and connectivity challenges, the proposed system bridges the gap between IoT-driven automation and manual control mechanisms, providing a scalable and reliable solution for broader automation applications.