HUBUNGAN ANTARA KECERDASAN EMOSIONAL DENGAN KOMPETENSI KEPRIBADIAN GURU

Abstrak. This study examines the relationship between emotional intelligence and personality competence. The subjects of this study were 60 teachers Of SMAN 1 Sungguminasa using total sampling techniques. Data retrieval used by using the emotional intelligence scale and personality competence scale. The data was then processed using parametric statistics, namely Pearson product moment. The results of the analysis show that there is a relationship between emotional intelligence and personality competence. This shows that there is a positive relationship between emotional intelegence and personality competence. The higher the emotional intelligence, the higher the personality competenc

Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games

Effect of Salinity Stress on Germination and Seedling Properties in Canola Cultivars (Brassica napus L.)

Germination and seedling responses of five rapeseed cultivars ('Elite', 'Fornax', 'Licord', 'Okapi', and 'SLM046') to salinity stress levels (0 cont, 5, 10, 15 and 20 dSm-1) evaluated in aRCBD base factorial design in three replicates in the glasshouse. Increasing Salinity decreased significantly rate and final germination, radicle and plumule length and fresh weight. The decreasing rate was different among cultivars. Salinity and cultivar interaction effect were significant in all attributes. Three parametric logistic regressions fitted the best estimation between germination and salinity levels. The highest and the lowest B coefficient belonged to 'Elite' and 'SLM046' that shows high and low susceptibility to salinity. Ld 50 threshold for germination for 'Elite' and 'SLM046' were 10.46 and 23.01 dSm-1, respectively. Tolerance ranking for cultivars was 'SLM046' > 'Okapi' > 'Fornax' > 'Licord' > 'Elite'. This classification belonged to early-season tolerance and it is necessary to study the next growth period to evaluate salinity tolerance rank among cultivars

LAPORAN PRAKTIK PENGALAMAN LAPANGAN UNIVERSITAS NEGERI YOGYAKARTA SD MUHAMMADIYAH MUTIHAN

SD Muhammadiyah Mutihan merupakan salah satu sekolah yang ditunjuk oleh pihak UNY untuk menjadi lokasi PPL pada tahun 2014.Tujuan dari program PPL adalah untuk memberikan pengalaman kepada mahasiswa dalam bidang manajerial dan pembelajaran di sekolah, memberikan pengalaman kepada mahasiswa dalam rangka melatih dan mengembangkan keprofesionalan dalam bidang keguruan atau pendidikan, memberikan kesempatan kepada mahasiswa untuk mengenal, belajar, dan memahami seluk beluk sekolah dengan segala permasalahannya, serta memberikan kesempatan kepada mahasiswa untuk menerapkan pengetahuan dan kemampuan yang telah dimiliki dalam proses pembelajaran. Program kegiatan PPL adalah mengajar mata pelajaran dasar selama kurang lebih dua setengah bulan dimulai tanggal 2 Juli sampai dengan 17 September 2014. Sebelum proses pembelajaran dilaksanakan di lapangan, praktikan menyusun Rencana Pelaksanaan Pembelajaran (RPP). Dari pelaksanaan kegiatan PPL, dapat disimpulkan bahwa kegiatan PPL, dapat memberikan pengalaman kepada mahasiswa dalam pengembangan kompetensi di bidang pendidikan, memberikan kesempatan kepada mahasiswa untuk belajar dan mengenal segala permasalahan di sekolah yang terkait dengan proses pembelajaran, memberikan kesempatan kepada mahasiswa untuk menerapkan ilmu, pengetahuan, dan keterampilan yang telah dipelajari dalam kehidupan nyata di sekolah, serta dapat meningkatkan hubungan kemitraan yang baik antara UNY dengan sekolah yang terkait

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition