THE SUPERVISORY PERFORMANCE OF BAWASLU HULU SUNGAI UTARA IN THE 2019 ELECTION

The Hulu Sungai Utara Regency Election Supervisory Body is an Election organizing body tasked with overseeing the implementation of Elections, the problem is the improper installation and / or distribution of Campaign Props (Campaign), Campaign Materials (BK), Unclear division of labor. The purpose of this study was to determine the Performance of the Election Oversight Body in the Supervision of Election of the House of Representatives, Members of the Regional Representative Council, President and Vice President in 2019 at the Election Supervisory Body (Bawaslu) of North Hulu Sungai Regency and the factors that influence it. This research uses a qualitative approach with descriptive-qualitative type. Data collection techniques used were interviews, observation and documentation. Source of data taken through a purposive withdrawal of 15 people. After the data is collected, it is then analyzed using techniques including data reduction, data presentation, and data verification or drawing conclusions. Test the credibility of the data in this study is the extension of observation, increase perseverance, triangulation, negative case analysis, and hold a member check. The results of the research on the performance of the Election Oversight Body in the Supervision of the Election of the House of Representatives, Members of the Regional Representative Council, the President and Vice President in 2019 on the Election Supervisory Body (Bawaslu) of North Hulu Sungai Regency have not been good this can be seen from several indicators.

Characteristics of landslides triggered by the 2013 ML6.5 Nantou, Taiwan, earthquake

Earthquake-triggered landslides are common disasters of active mountain belts. Due to the lack of earthquake-triggered landslide inventory in Taiwan, it is not intuitive to observe spatial relationships and discover unique patterns between landslides and essential triggers. We examined strong earthquake events in Taiwan after the 1999 Mw7.6 Chi-Chi earthquake and targeted the 2013 ML6.5 Nantou earthquake to create the landslide inventory. We adopted two Landsat-8 satellite images before and after the event to detect landslides, and incorporated a 20-m DEM and rock type data of Taiwan to represent key factors triggering earthquake-induced landslides such as peak ground acceleration (PGA), lithology, slope roughness, slope, and aspect. Based on the analysis of the density of landslides, there are strong correlations between the landslide occurrence and seismic and geomorphic factors. Furthermore, we noticed that the landslide aspects have a systematic tendency towards the northeast, which is not correlated with the dip directions and wave propagation directions. Instead, we found that the northeastward landslide aspect is more associated with the westward–southwestward surface movement at the landslides. We found that the included angles between the landslide aspects and the displacement directions for all the landslides are ~ 100°–180°. The relationship indicated that the coseismic deformation of the Nantou earthquake may play a role in the landslide distribution. Graphical Abstract

Spectral Clustering of Mixed-Type Data

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.

Mapping Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on the Northern Songnen Plain

Soil organic matter (SOM) plays a critical role in agroecosystems and the terrestrial carbon cycle. Thus, accurately mapping SOM promotes sustainable agriculture and estimations of soil carbon pools. However, few studies have analyzed the changing trends in multi-period SOM prediction accuracies for single cropland soil types and mapped their spatial SOM patterns. Using time series 7 MOD09A1 images during the bare soil period, we combined the pixel dates of training samples and precipitation data to explore the variation in SOM accuracy for two typical cropland soil types. The advantage of using single soil type data versus the total dataset was evaluated, and SOM maps were drawn for the northern Songnen Plain. When almost no precipitation occurred on or near the optimal pixel date, the accuracies increased, and vice versa. SOM models of the two soil types achieved a lower root mean squared error (RMSE = 0.55%, 0.79%) and mean absolute error (MAE = 0.39%, 0.58%) and a higher coefficient of determination (R2 = 0.65, 0.75) than the model using the total dataset and resulted in a mean relative improvement (RI) of 30.21%. The SOM decreased from northeast to southwest. The results provide reference data for the accurate management of cultivated soil and determining carbon sequestration.

Differentially private synthetic mixed-type data generation for unsupervised learning

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.

IMPLEMENTASI MATA PELAJARAN KEMUHAMMADIYAHAN DALAM MEMBENTUK KADER MUHAMMADIYAH YANG BERAKHLAK MULIA DI SMA MUHAMMADIYAH 6 DESA PON KECAMATAN SEI BAMBAN KABUPATEN SERDANG BEDAGAI

This study examines the problems in the Implementation of Muhammadiyah Subjects in Forming Muhammadiyah Cadres with Noble Morals at Muhammadiyah 6 High School, Pon Village, Sei Bamban District, Serdang Bedag Regency. The phenomenon that appears in the community is that there are still many students who graduate from Muhammadiyah high school who have not been maximized to become Muhammadiyah cadres and have noble qualities in themselves. The purpose of this research is to make students as Muhammadiyah cadres with noble character. Aspects of Kemuhammadiyah learning at SMA 6 Pon Village, Sei Bamban District in the form of Islamic activities such as greeting first when entering class, getting students to pray before learning, then exemplary methods in the aspect of the teacher saying greetings before entering class, the teacher praying before teaching and politely dressed teacher. The researcher used the approach in this research was qualitative with descriptive research type. Data collection with observation and interview techniques. Subjects that will be observed and interviewed are teachers and students. Data processing is carried out with the stages of data analysis, namely reducing data, presenting data and drawing conclusions.

An integrated surrogate modeling method for fusing noisy and noise-free data

Datasets in engineering applications often contain multiple types of data, i.e., noise-free data, noisy data with known noise variances, and noisy data with unknown noise variances. In this paper, a data fusion method, termed as multi-type data fusion (MTDF) model, is proposed to fully utilize the information provided by each of these types of data. The proposed model strives to capture the underlying trend implied in the multiple types of data better by approximately interpolating the noise-free data while regressing with the noisy data. To evaluate the prediction accuracy of the MTDF model, it is compared with multiple surrogate models including interpolation models, regression models, and multi-fidelity models on both numerical and practical engineering problems. The results show that the proposed MTDF model presents a more outstanding performance than the other benchmark models. The key issues, i.e., the effect of noise level, the effect of the sample size of noise-free data, and the robustness of the MTDF model are also investigated. The results illustrate that the MTDF model possesses satisfactory feasibility, practicality, and stability.

Use of machine-learning for monitoring solar thermal plants

A machine-learning algorithm (MLA) was developed to assess the operational state of solar thermal plants, based on the data of only one temperature sensor, and the irradiance and ambient temperature data from the nearest weather station. A detailed requirements analysis of the situation results in the classification of a multivariate time series problem. Neural networks used in the field of data science are ideally suited for problems of this type. Data from the operational monitoring system, which runs a rule-based algorithm, were used to train the neural network using the software framework TensorFlow. It was shown that the chosen MLA can detect malfunctions such as heat loss due to gravity-driven circulation during night. However, further development towards a practical tool requires not only far more data for training and validation. It became clear that corresponding pressure data are needed to classify temperature transients and to attribute these classes to certain malfunctions.

PENGARUH KEPEMILIKAN SAHAM, ROI, TOTAL ASET, DAN DIVIDEN TERHADAP DAR PADA PERUSAHAAN PT MEDIA NUSANTARA CITRA Tbk TAHUN 2011-2019

Abstrak: Penelitian ini berfokus pada permasalahan pengambilan kebijakan utang perusahaan PT Media Nusantara Citra Tbk yang meliputi kebijakan dividen, profitabilitas, struktur kepemilikan, dan ukuran perusahaan. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian asosiatif. Metode pengumpulan data menggunakan metode studi pustaka dan dokumentasi. Teknik pengambilan sampel menggunakan metode purposive sampling. Data yang digunakan yaitu data sekunder yang diperoleh dari laporan keuangan triwulan PT Media Nusantara Citra Tbk tahun 2011-2019. Penelitian ini menggunakan analisis regresi linier berganda melalui SPSS. Hasil penelitian menunjukkan bahwa secara parsial variabel kepemilikan saham dan total aset berpengaruh positif dan signifikan terhadap DAR. Sedangkan variabel ROI dan dividen berpengaruh negatif dan signifikan terhadap DAR. Sementara, secara simultan variabel kepemilikan saham, ROI, total aset, dan dividen memiliki pengaruh yang signifikan terhadap DAR pada PT Media Nusantara Citra Tbk Tahun 2011-2019. Kata Kunci: Kepemilikan Saham; Total Aset; ROI; Dividen; DAR. Abstract: This research focuses on the problem of PT Media Nusantara Citra Tbk debt policy-making which includes dividend policy, profitability, ownership structure, and company SIZE. This research uses a quantitative approach with an associative research type. Data collection methods using a library and documentation study methods. Sampling techniques using a purposive sampling method. The data used is secondary data obtained from PT Media Nusantara Citra quarterly financial report for 2011-2019. This study used multiple linear regression analysis with SPSS. The results showed that partially variable shareholding and total assets had a positive and significant effect on DAR. Meanwhile, variable ROI and dividends have a negative and significant effect on DAR. Meanwhile, simultaneously variable shareholding, ROI, total asset, and dividend have a significant influence on DAR at PT Media Nusantara Citra Tbk The year 2011-2019. Keywords: Shareholding; Total Assets; ROI; Dividend; DAR.