Indonesia memiliki berbagai warisan budaya tak benda salah satunya adalah kain songket. Kain songket memiliki banyak ragam sesuai ciri khas dari setiap daerah, khususnya songket Palembang. Kain songket Palembang memiliki keistimewaan dibandingkan songket dari daerah lain. Selain memiliki nilai sejarah, kain songket Palembang memiliki motif, mutu dan tingkat kerumitan yang tinggi dalam proses pembuatannya. Pada penelitian ini digunakan metode Random Forest untuk klasifikasi citra motif kain songket Palembang dengan mengunakan ekstraksi fitur Scale-Invariant Feature Transform (SIFT). Proses pembentukan fitur dengan metode SIFT melalui tahap scale space extrema detection, keypoint localization, orientation assignment, dan keypoint descriptor. Fitur yang dihasilkan digunakan untuk klasifikasi Random Forest. Citra motif songket yang digunakan pada penelitian ini sebanyak 115 citra dari setiap jenis motif, yaitu Bunga cina, Cantik Manis, dan Pulir. Pemilihan citra diambil dari 5 warna setiap motif songket Palembang. Data latih dan data uji yang digunakan masing-masing sebanyak 100 dan 15 untuk setiap motif Songket Palembang. Hasil pengujian menunjukkan bahwa metode SIFT dan Random Forest untuk klasifikasi citra motif kain Songket Palembang dapat memberikan akurasi yang cukup baik, dimana metode SIFT dan Random Forest mampu menghasilkan rata-rata overall accuracy 92,98%, per class accuracy 94,07%, presision 92,98%, dan recall 89,74%.
Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.
Econo mic load dispatch (ELD) problem is a common task in the operational planning of a power system, which requires to be optimized. This paper presents an effective and reliab le part icle swarm optimization (PSO) technique for the economic load dispatch problem. The results have been demonstrated for ELD of standard 3-generator and 6-generator systems with and without consideration of transmission losses. The final results obtained using PSO are compared with conventional quadratic programming and found to be encouraging.
Background: Access to information through continuing education (CE), reference and other resource materials is a prerequisite to the achievement of MDGs. Access of health workers to information in Ethiopia has been poor and will be further challenged by the deployment of 30,000 Health Extension Workers (HEW). Therefore, a study was undertaken to make a clear needs assessment, define priorities and identify resources to plan appropriate CE programs and prepare reference materials. Method: The study was conducted in 27 woredas, 50 health posts (HP) and on 60 HEW in all regions where HEW have been already deployed. Results: Almost all HEW have participated, on average for three and half days, in CE since their deployment. Woreda Health Offices (WHOs) were the main organizers. The HEW training modules are the only reference materials available at the HP level. Training in curative care and delivery related subjects stand out in future CE expectations of the HEW. Discussion and conclusions: The attention given to CE is encouraging but requires better planning and coordination. There is need to provide more reference and other resource materials. Increased use of modern technology for providing information should be explored.
Objectives:The objective of the study was to collect Moringa stenopetala (M. stenopetala) samples from 19 locations all over Ethiopia to generate a national data on its nutritional profile Methods: Fresh green Moringa stenopetala (M. stenopetala) leaf samples obtained from farming area in different provinces in Ethiopia were dried and physicochemical analysis was carried out employing AOAC methods of analysis. Results: The samples collected had a mean value of 8.09%, 28.44%, 0.7%, 11.62%, 12.63%, 38.49%, 274Kcal of moisture, protein, fat, crude fiber, ash, carbohydrate and energy respectively. Moreover, the samples had a mean value of 54.85 mg/100gm, 1,918 mg/100gm, 2.16 mg/100gm, 0.78 mg/100gm, 38.19 mg/100gm, 2,094 mg/100gm and 214.10 mg/100gm of Fe, Ca, Zn, Cu, P, K and Na respectively. The mean value of the anti-nutritional factors analyzed -phytate and tannin -was 378.44 mg/100gm and 358.89 mg/100gm, respectively. There has been a statistically significant difference in the mean values of all nutrition composition parameters between study regions-Tigray, Amhara, Oromia, SNNPR and Dire Dawa -except for tannin content of the samples. Conclusions: These finding reveals that M. stenopetala species of Moringa tree in Ethiopia has
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