The recent progress of dimension scaling of electronic device into nano scale has motivated the invention of alternative materials and structures. One new device that shows great potential to prolong the scaling is junctionless FET (JLFET). In contrast to conventional MOSFETs, JLFET does not require steep junction for source and drain. The device processing directly influence the performance, therefore it is crucial to understand the role of gate processing in JLFET. This paper investigates the influence of gate material and process on subthreshold performance of junctionless FET, by comparing four sets of gate properties and process techniques. The result shows that in terms of subthreshold slope, JLFET approaches near ideal value of 60 mV/decade, which is superior than the SOI FET for similar doping rate. On the other hand, the threshold value shows different tendencies between those types of device.
AbstrakTerdapat banyak metode yang digunakan untuk mengenali identitas seseorang, misalkan nomor unik, kartu identitas dan sandi rahasia. Kekurangan metode-metode tersebut antara lain, kartu dapat hilang, nomor unik dan sandi rahasia dapat terlupakan. Salah satu solusi untuk masalah ini adalah sistem identifikasi seseorang berdasarkan metode biometrik jenis fisiologis. Penelitian ini merancang sebuah sistem untuk mengidentifikasi wajah. Citra wajah diambil menggunakan kamera web kemudian diekstraksi cirinya dengan metode local binary pattern (LBP). Ciri wajah yang diperoleh diklasifikasi menggunakan support vector machine (SVM). Model terbaik SVM dibangun berdasarkan validasi silang grid search. Kernel linier terbaik dibentuk dengan LBP 8,2 u2 dan parameter C = 10 5 . Kernel radial basis function (RBF) terbaik dicapai dengan LBP 16,2 u2 dan parameter C = 10 3 dan γ = 10 2 . Berdasarkan pengujian terhadap keseluruhan citra wajah, akurasi kedua kernel adalah 96,0%. Pada pengujian lima ekspresi wajah dengan SVM kernel linier, akurasi 100,0% diperoleh untuk ekspresi sedih, netral dan mata tertutup. Sedangkan SVM kernel RBF menghasilkan akurasi 100,0% untuk ekspresi terkejut, netral dan mata tertutup. Hasil pengujian tersebut menunjukkan sistem pengenalan wajah yang dirancang telah berfungsi baik. Kata kunci: pengenalan wajah, local binary pattern, support vector machine, validasi silang grid search AbstractThere are many methods to recognize a person's identity, e.g. unique number, ID card, and password. The methods have some weaknesses, e.g. ID can be lost, unique number and password can be forgotten. One of solution to these problems is recognition system based on physiological biometric. This Research designs a system to identify human face. Face images are taken using webcam and then local binary pattern (LBP) is employed for feature extraction. The obtained face features are classified using support vector machine (SVM). The best models are built based on grid search cross validation. The best linier kernel is constructed with LBP 8,2 u2 and parameter C = 10 5 . The best radial basis function (RBF) kernel is achieved with LBP 16,2 u2 and the parameters C = 10 3 and γ = 10 2 . Test to all images using both kernels produces an accuracy of 96.0%. In test based on five facial expressions shows that linier kernel achieves an accuracy of 100,0% in sad, neutral and closed eyes and RBF kernel obtains an accuracy of 100,0% for shocked, neutral and closed eyes. The results of these tests show that the face recognition system works well. keywords: face recognition, local binary pattern, support vector machine, grid search cross validation.
ABSTRAKEmisi CO2 yang terakumulasi di atmosfer menyebabkan terjadinya perubahan iklim secara global. Untuk mengurangi dampak perubahan iklim, salah satu upaya yang dapat dilakukan saat ini adalah meningkatkan penyerapan karbon dan/atau menurunkan emisi di atmosfer. Ekosistem mangrove memiliki fungsi ekologis sebagai pereduksi karbon melalui proses sekuestrasi (C-sequestration). Tujuan dari penelitian ini untuk menghitung kerapatan, kandungan karbon dan serapan CO2 serta mengetahui hubungan korelasi kerapatan mangrove dengan kandungan karbon pada tegakan mangrove. Penelitian dilakukan dengan metode non-destructive sampling menggunakan persamaan alometrik. Pendekatan persamaan alometrik yang digunakan yaitu model alometrik biomassa pohon untuk jenis atau tipe ekosistem mangrove yang sudah tersedia atau dikembangkan di lokasi lain. Hasil pengukuran menunjukkan kerapatan tegakan mangrove di area tanam tahun 2004, 2007, dan 2010 adalah 1460 individu/ha, 1868 individu/ha, dan 2128 individu/ha. Biomassa pada area tanam tahun 2004 sebesar 206,77 ton/ha (103,39 ton C/ha, dan menyerap 379,09 ton CO2/ha). Area tanam tahun 2007 memiliki biomassa 293,73 ton/ha (146,86 ton C/ha, dan menyerap 538,50 ton CO2/ha), dan area tanam tahun 2010 memiliki biomassa 260,02 ton/ha (130,01 ton C/ha, dan menyerap 476,67 ton CO2/ha). Berdasarkan hasil penelitian, kerapatan jenis dan umur tegakan mangrove merupakan faktor yang mempengaruhi besarnya biomassa. Hasil uji regresi-korelasi menunjukkan, perubahan kerapatan mangrove akan berpengaruh secara signifikan pada ekosistem mangrove dalam menyimpan karbon dan menyerap CO2 dari atmosfer. Kata Kunci: Mangrove, biomassa, karbon, kerapatan jenis ABSTRACTEmissions of CO2 that accumulated in the atmosphere causes global climate change. To reduce the impact of climate change, one of the efforts is increased carbon sequestration. The mangrove ecosystem has ecological function as reducing carbon through sequestration process (C-sequestration). Aimed of this research to calculate the density, carbon content and absorption ability CO2 as well as knowing the correlation of mangrove density with carbon sequestration in mangrove. The method used in this research is use non destructive sampling method with allometric equation. The approach allometric equations were used that tree biomass allometric models for the type or types of mangrove ecosystems that are already available or developed in other locations. The measurement results of mangrove density in planting area 2004, 2007 and 2010 were 1,460 individuals/ha, 1868 individuals/ha, and 2128 individuals/ha. Biomass at the planting area in 2004 amounted to 206.77 ton/ha (103.39 ton C/ha, and absorbs 379.09 ton CO2/ha). Planting area in 2007 have biomass 293.73 ton / ha (146.86 ton C/ha, and absorbs 538.50 ton CO2/ha), and the planting area in 2010 have biomass 260.02 ton/ha (130.01 ton C/ha, and absorbs 476.67 ton CO2/ha). Based on the research results, the density of mangrove species and age are factors that influence the amount of tree biomass. The results of regression-correlation analysis showed, if mangrove density change significantly, it can influence the mangrove ecosystem in carbon sequestration and absorb CO2 from the atmosphere. Keywords: Mangrove, biomass, carbon, mangrove density
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