Analisis Kinerja Sekolah (Studi Kasus Sdn 002 Koto Peraku Kecamatan Cerenti Kabupaten Kuantan Singingi)

Elementary school 002 Koto Peraku is unit technical institution from Kuansing department. The function is giving attendant to make students intelligence in area subdistrict Cerenti. To create the purpose of education can useful for people or public, performance of school is influences to know the capability education in this school. But, these performance of school at SDN 002 Koto Peraku subdistrict Cerenti, Kuansing regency still low.Researcher use teori based on Mohamad Mahsun said there is some indicator in performance of organization: input, process, and output. The purpose of this research is to analyze how performance of school at SDN 002 Koto Peraku subdistrict Cerenti, Kuantan Singingi regency. This research is descriptive research the researcher. Used metode avalitative and snowball sampling to get the data the researcher use interview, observation, and documentation.Result of this result is performance of school at the SDN 002 Koto Peraku still low because lers the teacher (need teacher) means need teacher based on quality and quantity at this school sarana and prasarana still low to increase process studying. Support factor performance of school is teamwork all the teacher, obstacle factor performance of school is headmaster at school dont care with teacher and students, individual factor the teacher cant develop individual, system factor facilities of school dont enought to increase students and situational factor area at the school dont comfortable and safe for students.Keyward : Analyze, Performanc

Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy

Implementasi Mikrokontroler Untuk Deteksi Drop Tegangan Pada Instalasi Sederhana

Listrik merupakan energi yang sangat penting dalam kehidupan sehari-hari. Kebutuhanakan energi listrik semakin hari semakin besar sehingga perlu untuk di kembangkandalam memenuhi kebutuhan tersebut. Pada bidang industri, perkantoran dan Perumahan penggunaan energi listrik sudah menjadi suatu kebutuhan pokok. Oleh karena itukebutuhan energi listrik yang begitu besar sering kalimembuat pembangkit bekerja tidak maksimal karena terjadinya drop tegangan. Penelitian ini mencoba membuat alat untukmendeteksi drop tegangan yangmenggunakan trafo yang dihubungkan dengan resistor pembagi tegangan yangdigunakansebagai media sensor tegangan PLN dan mikrokontroler ATmega8535L sebagai pusat pengontrolan.Alat ini nantinya dapatmengendalikan beban secara otomatis jika terjadi tegangan generator turun, sehingga memilki tingkat keamanan pada beban beban yang penting seperti computer, laptop, danlain-lain. Hasil penelitian menunjukkan bahwa alat detector drop tegangan akan segeramemutus secara otomatis beban jika terjadi sebuah drop tegangan di sumber. Sistem iniakan memutus secara otomatis beban yang melebihi dari daya sumber PLN. Selain memutus secara otomatis terhadap beban yang berlebih, sistem juga akanmembunyikan buzzer yang berfungsi sebagai alarm

Anomaly-based intrusion detection system for IoT networks through deep learning model

The Internet of Things (IoT) idea has been developed to enhance people's lives by delivering a diverse range of smart interconnected devices and applications in several domains. However, security threats are main critical challenges for the devices in an IoT environment. Many approaches have been proposed to secure IoT appliances in state of the art, still advancement is desirable. Machine learning has demonstrated a capability to detect patterns when other methodologies have collapsed. One advanced method to enhance IoT security is to employ deep learning. This formulates a seamless option for anomaly-based detection. This paper presents a CNN-based approach for anomaly-based intrusion detection systems (IDS) that takes advantage of IoT's power, providing qualities to efficiently examine whole traffic across the IoT. The proposed model shows ability to detect any possible intrusion and abnormal traffic behavior. The model is trained and tested using the NID Dataset and BoT-IoT datasets and achieved an accuracy of 99.51% and 92.85%, respectively