PENGEMBANGAN BUDAYA SEKOLAH UNTUK PENINGKATAN KOMPETENSI PROFESIONAL GURU PADA MTSN 1 TAKENGON

PENGEMBANGAN BUDAYA SEKOLAH UNTUK PENINGKATAN KOMPETENSI PROFESIONAL GURU PADA MTSN 1 TAKENGONOleh: NazirUsmanNIM: 1109200050056KomisiPembimbing:1.Prof. Dr. Yusrizal, M. Pd.2.Prof. Dr. Murniati AR, M. Pd.ABSTRAKPengembangan budaya organisasi merupakan variable kunci yang bias mendorong pengembangan profesionalisme guru yang pada akhirnya dapat meningkatkan kinerja sekolah, berhasil atau tidaknya seorang guru dalam kegiatan belajar mengajar dibutuhkan budaya organisasi yang baik untuk meningkatkan mutu pendidikan. Penelitian ini bertujuan untuk mendapatkan data tentang budaya organisasi yang diterapkan disekolah sehingga dapat meningkatkan kompetensi profesional guru.Untuk mencapai penelitian ini peneliti menggunakan pendekatan kualitati fdengan metode deskriptif. Tekhnik pengumpulan data observasi, wawancara, dan study dokumentasi prosedur analisis data adalah reduksi data, display data, danverifikasi, subjek penelitian adalah kepala sekolah, wakil kepala sekolah, dan guru. Hasil penelitian menunjukkan bahwa: (1) Pengembangan budaya disiplin dalam menaati peraturan sekolah oleh kepala sekolah dapat meningkatkan professional guru dalam meningkatkan mutu pendidikan sekolah, (2) Pengembangan budaya tanggung jawab oleh kepala sekolah selalu dipenuhi oleh kepala sekolah sehingga semua guru tidak bermain-main dalam menjalankan aktivitas nya sebagai pendidik. (3) Pengembangan budaya malu oleh kepala sekolah juga terus dilakukan kepala sekolah sehingga guru-guru tidak bias bermain main dalam proses belajar pembelajaran.Kata kunci : BudayaSekolah/Organisasidan Profesional Gur

Khazanay.com set their accounts straight

Khazanay.com, an online portal selling used stuff online, is started by two enterprising young individuals in November 2015, Usman and Farid. Setting up the business was a tough task to conquer for used goods business is different from regular trading because of the inconsistent quality of procured products and the additional complexity of grading the products for quality. Further, identical goods are non-existent as every item is unique.The marketing end is also complicated as customers know that the good is already used and would have signs of wear and tear.However, equipped with healthy corporate training, quality education, unwavering commitment and persistence in getting the idea to work, both entrepreneurs managed to develop and maintain a process where the business has become profitable within one year.Being professional accountants, both entrepreneurs are aware of the importance of Financial Reporting, and their accounting prowess has been one of the main factors of their success.This case study talks about the entrepreneurial journey and how financial reporting may help a start-up in the initial phase

An investigation into psychosocial and educational predictors of entrepreneurial intentions of graduates - A study of the Centre of Entrepreneurial Development (CED) from Karachi (Pakistan)

People join Entrepreneurship Education Programmes (EEPs) for several reasons which may include gaining a certificate, learning new ideas or concepts, and/or starting their own business. One of the main aims of EEPs is to prepare students for entrepreneurship. The aim of the current project is to investigate entrepreneurial intentions in conjunction with the teaching methods deployed by the EEP called Certificate in Entrepreneurship (CIE) Programme in the Centre of Entrepreneurial Development (CED) Karachi. The project uses the well-established Theory of Planned Behaviour (TPB, Ajzen, 1985) as a theoretical framework, and looks into the extended TPB factors that might predict entrepreneurial intentions. We included psychosocial factors such as attitudes towards becoming an entrepreneur, social norms, perceived behavioural control about becoming an entrepreneur, and perceived effectiveness of the teaching methods adopted by the EEP, i.e., as case studies, anecdotes, lectures, industry visits, seminars and networking events. To address the overall aim of the study, we used a qualitative and quantitative approach, adopting the Francis et al. (2004) procedure for the development of a TPB instrument. The First Stage of the project included interviews (N=11) followed by open-ended questionnaires (N=12) with graduates of the CIE program which allowed us to gather qualitative data concerning teaching methods and individuals’ motivations for starting their own business. The results of thematic analysis on the qualitative data were then used, in the Second Stage, to develop an extended TPB instrument which included measures for attitudes towards becoming an entrepreneur, social norms, perceived behavioural control about becoming an entrepreneur, and attitudes towards teaching methods and entrepreneurial intentions. The quantitative data (N=247) gathered from the extended TPB survey was used to perform several regressions in order to determine the best predictors for entrepreneurial intentions. Our findings suggest that using guest speakers, local cases and networking sessions are the most impactful in influencing entrepreneurial intention of the CIE participants. Our results also showed that, with the exception of subjective norms, attitudes towards becoming an entrepreneur and perceived behavioural control mediate the relationship between these teaching methods and entrepreneurial intentions. Theoretical and practical implications include extended TPB model validation with teaching methods, mediating roles of attitude and perceived behavioural control, cultural influence on subjective norms, a TPB instrument and practical recommendation for EEPs that share similar socio-cultural context as CIE around the world

Case: Sour experiences make Waji Boutique look for better ways to run the business

It had only been a few months since Mr Bashir Ahmed, a retired IT Consultant, opened a boutique. He had done a good job in setting up the boutique and sourcing the necessary products for it.His business was slowly nudging towards break-even and everything was running smoothly, so he thought. After seven months of work, his salesperson and the only employee, Khawar, left work without prior notice. Mr Bashir decided to sit at the boutique himself until he found a suitable replacement. He was shocked to find all the fraudulent activities and deceptive tactics Khawar had been using right under his nose.There was inventory mismanagement, accounts manipulation and hidden loans on company’s accounts.Mr Bashir is sitting at the boutique and thinking how to fix the issues and the best way to move forward

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset

Stalking of healthcare professionals by their clients: The prevalence, motivation, and effect

It is proposed that healthcare professionals are prone to be stalked by their patients, but many feel ashamed to talk about it, believing that they might have done something to warrant the stalking. We undertook a rigorous review of the literature with the primitive goal of highlighting noteworthy issues on the stalking of healthcare professionals and psychologists by their patients. Databases such as PubMed and Google Scholar were searched to collate articles and studies on the topic with the keywords “stalker, stalking, assaults, aggression, and physicians”. From the review, the prevalence rate of stalking healthcare professionals ranged between 6% - 53%. This huge variation was largely due to the inconsistencies in the definition of what constituted stalking. The common motives of stalkers were largely due to erotomania or misguided expectation and a sense of resentment due to service dissatisfaction. Finally, it was apparently right to conclude that, medical doctors or healthcare professionals are at risk of being stalked on the grounds of service dissatisfaction, and mismanagement of treatment processes resulting in physical or perceived client injuries. Whereas psychologists and other psychiatrists are more prone to be stalked due to erotomaniac reasons

Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using spectral indices followed by random forest classification on the remaining mask with carefully selected features. Using Sentinel-2 satellite imagery, we evaluate the performance of the proposed technique on a specified area (approximately 82 acres) of Lahore University of Management Sciences (LUMS) and demonstrate that our method outperforms a conventional random forest classifier as well as state-of-the-art methods such as European Space Agency (ESA) WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.Comment: IEEE IGARSS 202