Meningkatkan Kinerja melalui Motivasi dengan Anteseden Kepemimpinan Terpersepsi dan Lingkungan Kerja Terpersepsi

Tujuan penelitian ini adalah untuk menguji dan menganalisis pengaruh kepemimpinan terpersepsi dan lingkungan terpersepsi terhadap kinerja karyawan PT Kanindo Makmur Jaya, Jepara yang dimediasi melalui motivasi kerja.. Dalam penelitian ini yang menjadi populasinya adalah seluruh karyawan yang ada di PT. Kanindo Makmur Jaya kabupaten Jepara yang berjumlah 2100 orang. Sedangkan sampel yang diambil 100 orang. Metode pengujian menggunakan regresi linear berganda. Hasil penelitian menun- jukkan bahwa (1) Kepemimpinan terpersepsi berpengaruh positif dan signifikan terhadap Motivasi; (2) Lingkungan kerja terpersepsi berpengaruh positif dan sinifikan terhadap Motivasi; (3) Kepemimpinan terpersepsi berpengruh positif dan signifikan pada kinerja; (4) Lingkungan kerja terpersepsi berpengaruh positif dan sinifikan terhadap kinerja; (5) Motivasi berpengaruh positif dan signifikan pada kinerja. Kata Kunci : Kepemimpinan terpersepsi, lingkungan kerja terpersepsi, motivasi kerja, dan kinerja

Formation of super-massive black holes in galactic nuclei I: delivering seed intermediate-mass black holes in massive stellar clusters

Supermassive black holes (SMBHs) are found in most galactic nuclei. A significant fraction of these nuclei also contain a nuclear stellar cluster (NSC) surrounding the SMBH. In this paper, we consider the idea that the NSC forms first, from the merger of several stellar clusters that may contain intermediate-mass black holes (IMBHs). These IMBHs can subsequently grow in the NSC and form an SMBH. We carry out $N$-body simulations of the simultaneous merger of three stellar clusters to form an NSC, and investigate the outcome of simulated runs containing zero, one, two and three IMBHs. We find that IMBHs can efficiently sink to the centre of the merged cluster. If multiple merging clusters contain an IMBH, we find that an IMBH binary is likely to form and subsequently merge by gravitational wave emission. We show that these mergers are catalyzed by dynamical interactions with surrounding stars, which systematically harden the binary and increase its orbital eccentricity. The seed SMBH will be ejected from the NSC by the recoil kick produced when two IMBHs merge, if their mass ratio $q\gtrsim 0.15$. If the seed is ejected then no SMBH will form in the NSC. This is a natural pathway to explain those galactic nuclei that contain an NSC but apparently lack an SMBH, such as M33. However, if an IMBH is retained then it can seed the growth of an SMBH through gas accretion and tidal disruption of stars.Comment: 19 pages, 17 figures, 6 tables, accepted for publication in MNRA

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error

An investigation of factors related to self-efficacy for java programming among engineering students

The purpose of this study was to examine the factors related to self-efficacy for Java programming among first year engineering students. An instrument assessing Java programming self-efficacy was developed from the computer programming self-efficacy scale of Ramalingam & Wiedenbeck. The instrument was administered at the beginning of the course via Internet with a questionnaire concerning gender, department, computer skills, computer experience, frequency of computer use and family background. Results indicated that self-efficacy of males were stonger than females, 11. 8 % of the variance in self-efficacy was explained by computer experience, the correlation coefficient calculated with the regression factor score of computer skills and self-efficacy scores was statistically significant. In addition siblings use of computers was significant and the mother's role was critical. © The Turkish Online Journal of Educational Technology 2002

SOSIALISASI BISNIS DIGITAL UNTUK SISWA-SISWI SEKOLAH MENENGAH KEJURUAN (SMKS PRIMA TIARA MAKASSAR)

Digital business atau disebut juga bisnis digital adalah bisnis tercanggih saat ini yang diperkirakan terus berkembang setiap tahun dan semakin lama akan meningkat tajam. Seiring dengan perkembangan teknologi informasi yang bergerak sangat cepat baik dibidang pendidikan, bisnis, pemasaran dan bidang lainnya. Sekolah Menengah Kejuruan (SMK) lebih fokus pada penyiapan siswanya untuk memasuki dunia kerja. SMK membekali siswanya untuk bisa bekerja secara mandiri dan terampil. Kegiatan pengabdian ini menggunakan metode sosialisasi dan metode diskusi. Proses sosialisasi terkait bisnis digital ini disajikan dalam bentuk pemaparan materi secara langsung. kegiatan pengabdian masyarakat ini diharapkan akan memberikan wawasan dan inspirasi bagi siswa-siswi SMK Prima Tiara Makassar yang tertarik untuk menjelajahi potensi bisnis digital

Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using ehg records

Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate. © 2014 Springer International Publishing Switzerland

Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques. Features are ranked to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the Radial Basis Function Neural Network classifier performed the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants is most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography – EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organized network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. The proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%

A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible to Polygenic Obesity and Overweigh

Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the most difficult clinical and public health challenges worldwide. While eating healthy and exercising regularly are obvious ways to combat obesity, there is a need to understand the underlying genetic constructs and pathways that lead to the manifestation of obesity and their susceptibility metrics in specific individuals. In particular, the interpretation of genetic profiles will allow for the identification of Deoxyribonucleic Acid variations, known as Single Nucleotide Polymorphism, associated with traits directly linked to obesity and validated with Genome-Wide Association Studies. Using a robust data science methodology, this paper uses a subset of the TwinsUK dataset that contains genetic data from extremely obese individuals with a BMI≥40, to identify significant obesity traits for potential use in genetic screening for disease risk prediction. The approach posits a framework for methodical risk variant identification to support intervention strategies that will help mitigate long-term adverse health outcomes in people susceptible to obesity and overweight