anfaatan Video Tiktok Sebagai Media Pembelajaran Menulis Teks Eksplanasi Peserta Didik Kelas VIIISMPN31 Semarang Tahun Ajaran 2022/ 2023

The background of this research is that the activities of Class VIII students of SMPN 31 Semarang in the learning process are not enthusiastic, students only focus on using books as learning aids. Therefore, in the learning process we need a media-based learning aid that can attract students' attention in order to improve student performance and understanding. It is important to apply TikTok-based learning application innovations to involve students in learning so that the material being taught is easy to understand.    Based on this background, the problem arises How to Use the Tiktok Application as a Learning Media in Writing Explanatory Text Writing Skills for Class VIII Students of SMPN 31 Semarang Academic Year 2022/2023? This study aims to identify the Utilization of the Tiktok Application as a Learning Media in Writing Explanatory Text Writing Skills for Class VIII Students of SMPN 31 Semarang Academic Year 2022/2023. This type of research is mixed method . Data collection was carried out by observation, interview, test and documentation methods.    The results of this study indicate that students in class VIII use the media using the social media application TikTok above, it can be seen that the average score of students in class VIII SMPN 31 Semarang in the 2022/2023 academic year when writing explanatory texts in general is at the moderate level (50- 74). The average score of the students on the explanatory text writing test performance was 66.71 at the sufficient level

UHF RFID split-ring resonator tag antenna inductively coupled feed for metallic object

In this paper, a split-ring resonator and an inductively coupled feed technique are designed to improve the RFID tag antennas performance. The presented tag antenna consists of two symmetrical C-shaped resonators with strip line in order to feed the radiating split-ring resonator structures by implementing an inductively coupled feed approach placed on the upper surface of Polytetrafluoroethylene substrate. By selecting a proper length and width of spilt-ring resonator structures, along with desirable coupling distance between two symmetrical C-shaped resonators and spilt-ring resonator structures, the input impedance of tag antenna can be obtained, which leads to attain an excellent conjugate match between antenna and IC chip. The proposed feeding technique and spilt-ring resonator structures offer a better performance of RFID tag among antenna size, reflection coefficient, and gain. The gain of the designed tag antenna is -2.272 dB at its operating frequency (916 MHz), the tag antenna is mounted on a square perfect electrical conductor of 200 mm side length, while the thickness of PEC is 1 mm. The simulation results were verified by the presented method via enhancing the performance of tag antennas for metallic object

A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals.

This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a signal-to-image technique, which is continuous wavelet transformation (CWT) that decomposes the AE signals over time-frequency scales and extracts the crack/fracture information in both the time and frequency domains. First, AE signals for two types of cracks (minor and severe), along with the normal condition (no crack), are collected from the experimental test bed. Second, the previously mentioned CWT based signal-to-image technique is applied to generate two-dimensional time-frequency images that are then converted to gray scale images for faster computation. These images are supplied to the SqueezeNet for classification of the concrete crack types. We extensively modified the fire module of the SqueezeNet (SQN-MF) by introducing depth-wise convolutional kernels and channel shuffling operations. Not only does the proposed method utilize deep learning-based techniques for crack classification of concrete beams for the first time, but also the CWT-based imaging technique has not yet been explored in this field either. Additionally, this method does not follow the typical AE burst feature (features like AE counts, peak-amplitude, rise time, decay time, etc.) based methods, and as a result, we no longer require extensive human intervention and expertise to get deep understanding of the crack types. SQN-MF achieves AlexNet-level accuracy with fifty times fewer parameters and has an implementable memory size for the field programmable gate array boards. Overall, the method achieves 100% accuracy. It is 20.8% higher than the typical feature extraction and traditional machine learning based methods. We observed a 4% accuracy increase for the proposed SQN-MF compared to the typical SqueezeNet with bypass connections

Perancangan Data Warehouse Untuk Data Penelitian Di Perguruan Tinggi Menggunakan Pendekatan Nine Steps Methodologhy

Data yang berada dan digunakan pada perguruan tinggi bermacam-macam seperti data akademik, data mahasiswa, data penelitian, dan lain-lain. Penggunaan teknologi data warehouse banyak digunakan oleh berbagai industri karena memungkinkan integrasi berbagai macam aplikasi atau sistem. Desain data warehouse yang efektif dapat membantu manajemen lembaga untuk memutuskan evaluasi kritis untuk organisasinya. Penelitian ini membahas tentang perancangan data warehouse untuk data penelitian di perguruan tinggi menggunakan pendekatan nine steps methodology. Data warehouse penelitian yang telah dibuat, berguna didalam menganalisis data-data penelitian di perguruan tinggi. Sebelumnya perguruan tinggi tersebut belum mempunyai database untuk data penelitian. Sehingga dapat dijadikan sebagai analisis data menggunakan OLAP untuk dijadikan pendukung pengambilan keputusan

ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and and well defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection through reducing human bias and effort required for such task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://github.com/saifarnab/code_revie

Spatially Optimized Compact Deep Metric Learning Model for Similarity Search

Spatial optimization is often overlooked in many computer vision tasks. Filters should be able to recognize the features of an object regardless of where it is in the image. Similarity search is a crucial task where spatial features decide an important output. The capacity of convolution to capture visual patterns across various locations is limited. In contrast to convolution, the involution kernel is dynamically created at each pixel based on the pixel value and parameters that have been learned. This study demonstrates that utilizing a single layer of involution feature extractor alongside a compact convolution model significantly enhances the performance of similarity search. Additionally, we improve predictions by using the GELU activation function rather than the ReLU. The negligible amount of weight parameters in involution with a compact model with better performance makes the model very useful in real-world implementations. Our proposed model is below 1 megabyte in size. We have experimented with our proposed methodology and other models on CIFAR-10, FashionMNIST, and MNIST datasets. Our proposed method outperforms across all three datasets.Comment: 5 pages, 3 figures

CATALYZING METEOROLOGICAL INSIGHTS WITH A COST-EFFECTIVE WEATHER MONITORING SYSTEM

Undoubtedly, one of the biggest alarming phenomena of this decade is the tremendous fluctuations in the weather and climate. Therefore, different types of surveys, investigations, and research are required in this regard in every region. A low-cost weather monitoring system can be implemented in every educational and research institute to collect and analyze different types of weather-related data. This study establishes the method of developing such a system and analyzing data in a simplified way which the data gathered during thunderstorms and cyclonic activity in Bangladesh. The system was designed with Proteus 8 professional software and developed by using a microcontroller, a temperature-humidity sensor, a wind speed analyzer, an automated rainfall analyzer, a barometric pressure sensor, and an LDR-based lightning bolt analyzer with a Linux-operated computer. The result obtained from the developed system is calibrated and compared with the standard value or theoretical value. The comparison graph shows that the developed system is efficient and reliable. After calibrating the system, several data points were collected at Mawlana Bhashani Science and Technology University, Tangail, Bangladesh. Developing an in-house weather monitoring system allows institutions to avoid costly foreign data purchases, reducing expenses and reliance on international services. Practically, this research can be applied to support climate studies and localized forecasting without the expense of high foreign exchange rates, allowing for more affordable meteorological research and enhancing local expertise