Dimensi Social Capital yang Memengaruhi Kinerja Pegawai Bpjs Kesehatan

Social capital dipandang dari perspektif sumber daya manusia merupakan upaya mengelola sumber daya menjadi aspek penting dalam pembentukan organisasi. Social capital didefinisikan sebagai serangkaian sumber daya potensial dan aktual yang tersedia pada jejaring organisasi yang dikembangkan oleh individu maupun kelompok. Tujuan penelitian mengkaji tingkat keterpengaruhan structural relational dan cognitive social capital terhadap kinerja pegawai BPJS Kesehatan Cabang Pasuruan. Rancangan penelitian bersifat kuantitatif, serta menggunakan pendekatancross sectional dengan sampel sebanyak 52 responden yang merupakan total populasi. Data dikumpulkan melalui pengisian kuesioner. Variabel-variabel diuji dengan menggunakan uji regresi linier sederhana dan berganda. Diperoleh temuan bahwa structural social capital relational social capital maupun cognitive social capital secara terpisah mempunyai pengaruh yang positif terhadap kinerja pegawai BPJS Kesehatan Cabang Pasuruan. Interaksi sosial, tingkat kepercayaan serta kesamaan dalam mengartikan visi Perusahaan dipandang merupakan aspek yang dapat mewarnai kinerja pegawai. Ketika ketiga dimensi ini disatukan (unidimensi) masih tampak adanya pengaruh terhadap kinerja pegawai. Namun, manakala ketiga dimensi social capital tersebut diperlakukan secara multidimensi tidak diperoleh hasil keterpengaruhan yang signifikan. Social capital sebagai satu kesatuan tidak dapat diposisikan pada dimensinya masing-masing, tetapi harus diletakkan sebagai sebuah kerangka dimensi yang utuh

Analisis Korelasi Untuk Menentukan Hubungan Literasi Sains Dan Motivasi Berprestasi Terhadap Hasil Belajar Pendidikan Agama Islam

Abstract: Islamic education in vocational schools lately is still less encouraging. In terms of learning outcomes and motivation to excel in the lesson is still relatively low when compared to other lessons, especially vocational lessons. Islamic Religious Education learning is also less associated with science. This is exacerbated by the results of science literacy by PISA (International Student AssessmentProgram)  obtained by our country is also very low. Therefore it is considered necessary to improve Islamic Religious Education through the motivation of  achievement and science literacy. The purpose of this study is to find out the  correlation between science literacy and motivation of achievement with the results of learning Pendidikan  Agama  Islam. The methodology the authors used in this study was  descriptive quantitative with correlational analysis methods. Data analysis techniques of the results of the study using pearson productmoment correlation technique.  The results of correlation analysis test resulted in a positive and significant correlation between science literacy and motivation of achievement with the results of learning Pendidikan  Agama  Islam,  obtained product moment coefficient price (R) of  0.306 with the contribution of science literacy, motivation to achieve the results of learning Pendidikan  Agama  Islam by 9.40% and the remaining 91.60% contributed by other factors. This study can be concluded that variables X1  and X2 have a simultaneous and significant effect on Y, the value of r2  from correlation X1X2  with Y of 0.094 means, variable X1X2  produces a positive correlation with variable Y.Abstrak: Pendidikan Agama Islam di SMK belakangan ini masih kurang menggembirakan. Dari segi hasil belajar dan motivasi untuk berprestasi dalam pelajaran tersebut masih tergolong rendah jika dibandingkan dengan pelajaran lainnya, terutama pelajaran kejuruan. Pembelajaran Pendidikan Agama Islam juga kurang dikaitkan dengan sains.  Hal ini diperparah dengan hasil literasi sains oleh PISA (Program Penilaian Pelajar Internasional) yang didapat negara kita juga sangat rendah. Oleh karena itu dipandang perlu untuk meningkatkan Pendidikan Agama Islam melalui motivasi berprestasi dan literasi sains. Tujuan dari penelitian ini adalah untuk mengetahui korelasi antara literasi sains dan motivasi berprestasi dengan hasil belajar Pendidikan Agama Islam. Metodologi yang penulis gunakan dalam penelitian ini adalah deskriptif kuantitatif dengan metode analisis korelasional. Teknik analisis data hasil penelitian menggunakan teknik korelasi Pearson Product Moment. Hasil pengujian analisis korelasi menghasilkan korelasi yang positif dan signifikan antara literasi sains dan motivasi berprestasi dengan hasil belajar Pendidikan Agama Islam, didapat harga koefisien Product Moment (R) sebesar 0,306 dengan kontribusi literasi sains, motivasi berprestasi terhadap hasil belajar Pendidikan Agama Islam sebesar 9,40 % dan sisanya 91,60% dikontribusi oleh faktor-faktor lainnya. Penelitian ini dapat disimpulkan bahwa variabel X1 dan X2 berpengaruh secara simultan dan signifikan terhadap Y, nilai r2 dari korelasi X1X2 dengan Y sebesar 0,094 artinya, variabel X1X2 menghasilkan korelasi positif dengan variabel Y

Identifikasi Sebaran Spasial Genangan Banjir Memanfaatkan Citra Sentinel-1 dan Google Earth Engine (Studi Kasus: Banjir Kalimantan Selatan)

Hujan dengan intensitas sedang hingga tinggi menyebabkan banjir pada pertengahan bulan Januari 2021 di Provinsi Kalimantan Selatan. Banjir di Provinsi Kalimantan Selatan pada Januari 2021 membawa dampak korban jiwa maupun materi. Dalam rangka mengurangi kerugian materi yang lebih besar, perlu dilakukan identifikasi wilayah yang mengalami banjir. Dalam penelitian ini daerah sebaran genangan banjir diidentifikasi menggunakan metode change detection dan threshold atau ambang batas. Data diperoleh dari hasil pengolahan menggunakan Google Earth Engine (GEE) berupa peta genangan banjir yang dievaluasi dengan peta hasil pengolahan Badan Perencanaan Pembangunan Daerah (BPPD) Provinsi Kalimantan Selatan. Metode change detection dilakukan pada citra dengan cara membagi nilai piksel citra saat banjir dibagi dengan sebelum banjir. Ekstraksi area genangan kemudian dilakukan dengan nilai threshold sebesar 1,10. Pengolahan dilakukan secara komputasi cloud menggunakan Google Earth Engine (GEE). Luas genangan banjir yang dihasilkan pada tanggal 20 Januari 2021 adalah seluas 226.905 hektar. Hasil tersebut dievaluasi terhadap data genangan banjir oleh Badan Perencanaan Pembangunan Daerah (BPPD) memiliki overall accuracy sebesar 97%

Zero-Shot Deep Domain Adaptation

Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision (ECCV), 201

Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results

Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive person Re-IDentification (ReID) is challenging because of the large domain gap between source and target domains, as well as the lackage of labeled data on the target domain. This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. The local one-hot classification assigns images in a training batch with different person IDs, then adopts a Self-Adaptive Classification (SAC) model to classify them. The global multi-class classification is achieved by predicting labels on the entire unlabeled training set with the Memory-based Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering both visual similarity and temporal consistency to ensure the quality of label prediction. The two classification models are combined in a unified framework, which effectively leverages the unlabeled data for discriminative feature learning. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks. For example, under unsupervised setting, our method outperforms recent unsupervised domain adaptive methods, which leverage more labels for training

PERENCANAAN GEOMETRIK DAN TEBAL PERKERASAN LENTUR JALAN BATAS KOTA PRABUMULIH – SIMPANG BELIMBING – MUARA ENIM STA 78+850 – STA 84+350 PROVINSI SUMATERA SELATAN

Di dalam penulisan laporan akhir ini, penulis melakukan perencanaan geometrik dan tebal perkerasan lentur jalan batas Kota Prabumulih – Simpang Belimbing – Muara Enim STA 78+850 – 84+350 Provinsi Sumatera Selatan. Di dalam merencanakan desain geometrik jalan raya, hal–hal yang menjadi acuan dalam perencanaan meliputi perhitungan alinyemen horizontal, alinyemen vertikal, kelas jalan, serta menetapan perkerasan apa yang digunakan. Dari hasil perhitungan – perhitungan maka Ruas Jalan Simpang Belimbing Muara Enim ini merupakan jalan Kolektor kelas II C dengan panjang jalan 5,5 km, dengan kecepatan rencana 80 km/jam, terdapat 2 lajur 2 arah dengan lebar jalan 2 x 3 m, dan lebar bahu jalan 2 x 1,5 m. Pada jalan ini menggunakan 9 buah tikungan. Lapis permukaan jalan menggunakan Lapis Pondasi agregat kelas B CBR 60% = 15 cm, Lapis Pondasi agregat kelas A CBR 90% = 15 cm, AC-Base Stabilitas 1800 kg dengan ketebalan 7,5 cm, AC-BC Stabilitas 800 kg dengan tebal 6 cm, AC-WC dengan tebal 800 kg dengan tebal 4 cm. Dan pembangunan ruas jalan ini dilaksanakan dalam waktu 249 hari kerja dengan total Rp 29.748.980 (Dua Puluh Sembilan Milyar Tujuh Ratus Empat Puluh Delapan Juta Sembilan Ratus Delapan Puluh Ribu Rupiah)

A Multi-robot System Coordination Design and Analysis on Wall Follower Robot Group

In this research, multi-robot formation can be established according to the environment or workspace. Group of robots will move sequently if there is no space for robots to stand side by side. Leader robot will be on the front of all robots and follow the right wall. On the other hand, robots will move side by side if there is a large space between them. Leader robot will be tracked the wall on its right side and follow on it while every follower moves side by side. The leader robot have to broadcast the information to all robots in the group in radius 9 meters. Nevertheless, every robot should be received information from leader robot to define their movements in the area. The error provided by fuzzy output process which is caused by read data from ultrasound sensor will drive to more time process. More sampling can reduce the error but it will drive more execution time. Furthermore, coordination time will need longer time and delay. Formation will not be establisehed if packet error happened in the communication process because robot will execute wrong command

Peran Ayah Dalam Pendidikan Anak Perspektif Al-Qur’an (Telaah Tafsir Ibnu Katsîr dan Al-Mishbâẖ)

This study aims to analyze the morals of fathers in educating children in the perspective of the Qur'an by studying Ibn Katsr's interpretation and al-Mishbâẖ's interpretation. This study uses a library research method using primary data of Ibnu Katsr's interpretation and al-Mishbâẖ's interpretation, secondary data in the form of pre-existing sources such as other supporting books related to the object being studied. The main steps of data analysis in this study begin with an inventory of the text in the form of verses, examine the text, look at the historical verses and look at the hadiths. Furthermore, it is interpreted objectively and described descriptively and then drawn some deductive conclusions. The results of this study indicate that the morals of fathers in educating children in the perspective of the Qur'an with the interpretation of Ibn Katsr and the interpretation of al-Mishbâẖ are wills, love for children with the call yâ bunayya (O my son), grateful, not burdensome to children and prospective in-laws, and pray