Pengaruh Penerapan Pembelajaran Kooperatif Tipe Pair Checks Terhadap Hasil Belajar Matematika Siswa Kelas VIII SMP Pertiwi 2 Padang

The background of this research is that srudents’ mathematics learning outcomes are still low, students participate less and have not maximized their role in group learning. The purpose of the study was to determine the improvement of students’ mathematics learning outcomes by applying the pair checks type cooperative learning model to the VIII grade students of SMP Pertiwi 2. This type of researchis pre-experimental design research with research design one shoot case study. The population in this study were students class VIII SMP Pertiwi 2 Padang which consists of 2 classes retrieval technique. The sample was taken using purposive sampling, so that class VIII.1 was chosen SMP Pertiwi 2 Padang as a sample. The research instrument is the final test in the form of essays and reliability of questions = 0,86 with reliable criteria. Based on the calculation results, it was obtained that the percentage of learning outcomes obtained by students while using the pair checks type of cooperative learning model obtained t count = -2,5 = 2,5 (absolute value) and t table = 1,79 with the degree of truth (dk) = n-1, n = 12 then (dk) = 11 and a significant rate of 5% = 0,05. So is rejected because t count > t table. So it can be concluded that there is an increase in student learning outcomes by applying the pair checks cooperative learning model to class VIII students SMP Pertiwi 2 Padang.

Analysis of combined approaches of CBIR systems by clustering at varying precision levels

<span lang="EN-US">The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and color-texture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named cluster-based retrieval of images by unsupervised learning (CLUE).</span>

Evaluasi Program untuk Pengembangan Literasi Qur’an Komunitas Pedagang Asongan di Kota Tegal Menggunakan Model Evaluasi CIPP

ABSTRAK Tujuan penelitian ini untuk menganalisis: 1) konteks program BTQ, 2) masukan program BTQ, 3) proses program BTQ, dan 4) hasil program BTQ. Pendekatan penelitian yang digunakan kualitatif dengan metode deskriptif. Informan penelitian terdiri dari guru BTQ, peserta program, dan perwakilan pihak TBM Sakila Kerti. Teknik pengambilan informasi menggunakan purposive sampling. Model evaluasi yang digunakan adalah CIPP (context, input, process, product). Hasil penelitian menunjukkan bahwa: (1) konteks menunjukkan program BTQ yang diselenggarakan oleh TBM Sakila Kerti sejalan dengan kebutuhan komunitas pedagang asongan, (2) masukan program, konsep kurikulum program BTQ disusun secara mandiri dengan melihat kebutuhan komunitas pedagang asongan dan tujuan program, (3) proses program, jadwal pelaksanaan program berjalan secara rutin, media pembelajaran cukup beragam, metode yang digunakan disesuaikan dengan kebutuhan sasaran yang selama ini sudah berjalan baik, dan (4) hasil program, dilihat pada tataran kognitif, afektif dan psikomotorik menunjukkan bahwa hasil program BTQ baik akan tetapi perlu ditingkatkan kembali agar tujuan-tujuan yang belum tercapai dapat tercapai. Kata Kunci: Evaluasi Program, Baca Tulis Al-Qur’an, Komunitas Pedagang Asongan ABSTRACT The purpose of this program is to eradicate illiteracy in the Qur'an because there are still many target audiences of Muslims who are not able to read and write the Qur'an. Therefore, it is important and interesting to conduct an evaluation study of the Al-Qur'an reading and writing program at TBM. The purpose of this study is to analyze: 1) the context of the BTQ program, 2) the input of the BTQ program, 3) the process of the BTQ program, and 4) the results of the BTQ program. The research approach used is qualitative with descriptive method. Research informants consisted of BTQ teachers, program participants, and representatives of TBM Sakila Kerti. Information retrieval technique using purposive sampling. The evaluation model used is CIPP (context, input, process, product). The results of this study indicate that: (1) the context shows that the BTQ program organized by TBM Sakila Kerti is in line with what is required by the community. the hawker community, (2) program input, the concept of the BTQ program curriculum is prepared independently by looking at the needs of the hawker community and program objectives, (3) the program process, the program implementation schedule runs regularly, the learning media is expected to be more diverse, the methods used are adjusted to the needs of the target which has been going well so far. Keywords: Program Evaluation, Reading and Writing of the Qur'an, Community of Peddlers

Single-pixel polarimetric direction of arrival estimation using programmable coding metasurface aperture

This paper presents a single-pixel polarimetric compressive sensing (CS)-based direction of arrival (DoA) estimation technique using a cavity backed programmable coding metasurface aperture. The single-pixel DoA retrieval technique relies on a dynamically modulated waveform diversity, enabling spatially incoherent radiation masks to encode the incoming plane waves on the radar aperture using a single channel. The polarimetric nature of the wave-chaotic coded metasurface ensures that the DOA estimation is sensitive to the polarization state of the incoming waves. We show that the polarimetric single-pixel DoA concept can be realized by encoding the polarization information of the incoming waves at the physical layer level within the antenna. A dynamically reconfigurable wave-chaotic metasurface, which possesses a structured sparsity of dual-polarized coded metamaterial elements, is proposed for the proof of concept. It is shown that by encoding and compressing the source generated far-field incident waves into a single channel, we can retrieve high fidelity polarimetric DoA information from compressed measurements.

Retrieving exoplanet atmospheric parameters using random forest regression

Understanding of exoplanet atmospheres can be extracted from the transmission spectra using an important tool based on a retrieval technique. However, the traditional retrieval method (e.g. MCMC and nested sampling) consumes a lot of computational time. Therefore, this work aims to apply the random forest regression, one of the supervised machine learning technique, to retrieve exoplanet atmospheric parameters from the transmission spectra observed in the optical wavelength. We discovered that the random forest regressor had the best accuracy in predicting planetary radius ( R F i t 2 = 0.999) as well as acceptable accuracy in predicting planetary mass, temperature, and metallicity of planetary atmosphere. Our results suggested that the random forest regression consumes significantly less computing time while gives the predicted results equivalent to those of the nested sampling PLATON retrieval.

Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.

Balloon-assisted inferior vena cava filter retrieval: A novel technique to enhance irretrievable filter extraction

Although the use of inferior vena cava (IVC) filters has expanded, complications have led to the recommendation to remove all unnecessary filters. Several techniques exist to aid in retrieving irretrievable IVC filters. The balloon-assisted retrieval technique has previously been described to aid in cases of an embedded hook. This report describes an alternative use of the balloon-assisted technique, specifically to aid in the retrieval of Greenfield IVC filters in cases of strut perforation of the cava wall. This technique was successfully performed in six out of six attempted cases with no associated complications.

Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.