Dekomposisi Citra Gerakan Dalam Rekaman Cctv Menggunakan Transformasi Wavelet Diskrit

Pada makalah ini dipresentasikan hasil dekomposisi data citra gerakan dalam rekaman CCTV menggunakan transformasi wavelet diskrit. Citra gerakan pada rekaman CCTV diambil dengan menggunakan background substraction. Dekomposisi data citra ditujukan untuk mendapatkan jumlah data citra yang lebih sedikit tetapi tidak menghilangkan cirri atau karakter citra aslinya. Nilai data pada setiap pixel citra yang sebelumnya tersusun dua dimensi diubah menjadi deret nilai pixel satu dimensi. Penerapan transformasi wavelet diskrit dilakukan dengan teknik pemfilteran menggunakan impuls daubechies orde 4 (Db4) wavelet. Pemfilteran ini menghasilkan dekomposisi sebuah sinyal dengan pengurangan setengah data di setiap level dekomposisi. Dari pengujian yang dilakukan, pada dekomposisi level 1 pengurangan data sebesar 49,99% dengan Perubahan parameter rata-rata nilai pixel 1,19% dan Perubahan pola pixel 1,93%. Pada level 2, pengurangan jumlah data 24,99% rata-rata nilai pixel 1,62% dan Perubahan pola pixel 2,46%. Pada level 3 Perubahan jumlah data 12,48% dengan Perubahan rata-rata pixel 2,32% dan pola pixel 3,82%. Pada level 4 Perubahan jumlah data mencapai 6,22% dengan Perubahan rata-rata nilai pixel 2,31% dengan Perubahan pola 4,57% dari citra aslinya. Dari hasil tersebut dapat disimpulkan bahwa transformasi wavelet dapat digunakan untuk memperkecil jumlah data citra tanpa kehilangan cirri atau karakteristik aslinya

Rancang Bangun Aplikasi Data Mining untuk Memprediksi Hasil Belajar Siswa Sekolah Menengah Atas Berbasis Web dengan Algoritma K-NN (Studi Kasus: SMKN 2 Pekanbaru)

Curriculum 2013 (K-13) was first announced in 2014 which has been applied to number of schools. Preparation of this new curriculum by the government aimed at making education in Indonesia is not only focused on cognitive aspects or skills possessed, but also at students' interest and motivation. Unfortunately, behind the goal, there are issues occured in the school during the application of K-13. Those are input process and values conversion that takes relatively much time. The things are caused by the dissimilarity of the standards and the assessment scale between current curriculum with the previous one. Meanwhile, the academic system running in schools is still pretty conventional. Therefore, this research will construct an application which have capability to handle the things. Beside those additional features, this research is build an application in order to apply the data mining with k-NN algorithm to predict students learning outcomes based on certain subjects. Data source that used in this research were consisted into 500 data training that covered up all classes or labels. Testing methods which have been applied are black box testing and confusion matrix. There are 3 techniques of black box testing that applied in order to test the system functionality according to its input values. Those are equivalence class partitioning, boundary value analysis and decision table based testing. Meanwhile in confusion matrix, it has been done 3 times testing according by k value in k-NN algorithm. With k-5 acquired accurate rate 79.34%, k-10 with accurate rate 62.67%, then k-15 with accurate rate 64%. Thus, information that can conluded from those testing methods is the algorithm with k-5 is more accurate than any others

The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Rainfall Characteristics of Johor Bahru and Kota Bharu, Malaysia

Characteristics of rainfall was influenced by climate variability. Occurrence of extreme events due to climate variability may lead to flash floods or monsoonal flooding. This paper discusses on characteristics and trend of monthly and annual rainfall at Johor Bahru and Kota Bharu. Daily rainfall observations at the stations that spans over 13 years, i.e. from year 2004 to year 2016 was used. Minimum and maximum total annual rainfall recorded at Johor Bahru and Kota Bharu are 1708.0 mm and 3455.5 mm, and 1036.3 mm and 3037.0 mm, respectively. Mann-Kendall test has showed that the monthly and annual rainfall trends at both stations are insignificant. Longer period of rainfall observations should be considered to obtain more significant trends. The longest dry and wet days within a month observed over the 13-year rainfall are 26 days and 17 days at Johor Bahru, and 29 days and 25 days at Kota Bharu. Both Johor Bahru and Kota Bharu receives higher rain during the beginning of north-east monsoon, i.e. in November and December

Aplikasi Penerjemah Bahasa Isyarat Indonesia Menjadi Suara Berbasis Android Menggunakan Tensorflow

Indonesian Sign Language or BISINDO is a two-handed sign language that is used as a liaison in communication. BISINDO is used by people who have limited speech or hearing, but not for other communities. This causes BISINDO users have difficulties in conveying information because only a few people understand BISINDO. Therefore, an application was developed to help communication between BISINDO users and Indonesian in realtime. BISINDO classification is carried out using the Convolutional Neural Network method and the MobilenetV2 architecture using tensorflow. The classification results are used as a model for android which is then used as a sound. Based on model testing, the resulting accuracy rate resulted in 54.8% in the classification of 30 specified languages. Thus, the performance of the model can be said to be not optimal in classifying. Based on the application testers to 30% of respondents, it was found that respondents strongly agreed with this application with an average value of 83.95%

Performance of Discrete Wavelet Transform on CCTV Images Data Decomposition

Volume 8 Issue 4 (April 201

Performance of Discrete Wavelet Transform on CCTV Images Data Decomposition

Volume 8 Issue 4 (April 201

Sistem Terintegrasi untuk Mendeteksi Perubahan Lingkungan dengan Algoritma Frame Difference dan Dynamic - Adaptive Template Matching Menggunakan Raspberry Pi dan Virtual Private Network (VPN)

Theft of empty houses and rampant illegal parking is a problem that often occurs in the community, causing anxiety. For this reason, a solution that can provide alarms / notifications automatically if such conditions occur to registered users is needed. This research develops an integrated system that has the ability to detect changes in environmental conditions, such as the occurrence of illegal parking or the presence of strangers in the house, through monitoring of connected IP cameras. The image captured through an IP camera is processed using the Frame Difference algorithm, to further be decided whether it includes an environmental condition changes or not. Compared to conventional IP cameras or IP cameras that include motion detection features, this research system has advantages in terms of notification form flexibility and internet connection flexibility

Manuscript; Reviewer Comments from Investigation of structural, electronic and optical properties of two-dimensional MoS₂-doped-V₂O₅ composites for photocatalytic application: a density functional theory study

Manuscript file; Reviewer Comments Fil