Judul: Pengaruh Lama Perendaman Benih dalam Air dan Konsentrasi Penyemprotan Mixtalol terhadap Pertumbuhan Semaian Cengkeh (Eugina caryophyllus (Spreng.) Bullock et Harrison)
Abstrak: Tujuan penelitian ini adalah untuk mengetahui sejauh mana benih cengkeh perlu direndam dalam air dan pengaruh pemberian berbagai konsentrasi Mixtalol terhadap pertumbuhannya.
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Judul: Pengaruh naungan dan pemberian zat pengatur tumbuh mixtalol terhadap pertumbuhan bibit tanaman cengkeh (Eugenia caryophyllus Sprengel)
Abstrak: Tujuan penelitian adalah untuk mengetahui pengaruh penggunaan naungan, zat pengatur tumbuh mixtalol serta interaksi antara naungan dan zat pengatur tumbuh miztalol tersebut terhadap pertumbuhan bibit tanaman cengkeh (E. caryophyllus, Sp.). Penelitian dilaksanakan di Kebun Percobaan Cimanggu Balai Penelitian Tanaman Rempah dan Obat Bogor. Rancangan yang dipakai untuk menyusun percobaan adalah Rancangan Petak Terbagi, terdiri dari 2 faktor dan 3 ulangan/ kelompok. Petak Utama terdiri dari 4 tingkat naungan, berturut-turut naungan 0, 25, 50, dan 75 persen masing-masing pada No, N1, N2, dan N3. Anak Petak terdiri dari 4 konsentrasi zat pengatur tumbuh mixtalol, berturut 0, 1, 2, dan 3 ppm, masing-masing pada Mo, M1, M2, dan M. Pengamatan pertumbuhan bibit dilakukan setiap 2 minggu, kecuali terhadap bobot kering, luas daun, dan kematian tanaman setiap 4 minggu. Kandungan khlorofil daun diamati 3 kali setelah bibit diberi perlakuan 8 minggu, seterusnya setiap 6 minggu. Analisis unsur hara tanaman diambil pada pengamatan terakhir saja. Analisis pertumbuhan tanaman, berdasarkan bobot kering dan luas daun adalah suatu cara untuk mengikuti dinamika fotosintesis dan pertimbangan aspek fisiologis tanaman, diukur dan dihitung setiap 4 minggu. Unsur iklim diamati pada saat tertentu dan atau 1 kali seminggu. Hasil percobaan menunjukkan bahwa naungan yang diberi sebagai perlakuan pada bibit tanaman cengkeh (E. caryophyllus, Sp.) sangat mempengaruhi dan menentukan pertumbuhan dan perkembangan bibit tersebut. Makin tinggi intensitas cahaya yang diberikan, maka makin berkurang pertumbuhan bibit. Dibawah cahaya penuh bibit tumbuh kerdil, daun-daun kecil dan berwarna kekuningan, dan mudah terserang hama penyakit akibatnya kemungkinan kematian bibit lebih besar. Naungan disamping menghindari efek penyinaran cahaya mataha- ri langsung, juga mengurangi pengaruh suhu tanah yang tinggi dan laju transpirasi bibit yang besar, serta kekurangan hara. ..
Keyword: Eugenia caryophyllus, Mixtalol

Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation