Judul: Bioactivity of Essential Oil from Suren (Toona sinensis Roemor) Based on the Brine Shrimp Lethality Test (BSLT)
Abstrak: INTRODUCTION. This study to determine the yield of essential oil from the leaves, heart wood, sap wood and inner bark, and also to determine the bioactivity of essential oil from suren (Toona sinensis Roemor). E ANALYSIS AND METHOD. ssential oil of suren leaves have been tested for use as insecticide. Because of that, essential oil of suren are interesting to studied. Parts of trees that used are the leaves, inner bark, heart wood and sap wood. To generate the yield, the raw materials are destilated using method steam and water. As for determining bioactivity conducted tests on shrimp larvae Artimia salina Leach. through the Brine Shrimp Lethality Test methods. Thin Layer Chromatography Test (TLC) was conducted to estimate the number of compounds contained in the essential oil RESULT AND DISCUSSION. . The results showed that yield of the sap wood is (0.388%), heart wood (0.379%), leaves (0.302%), and inner bark (0.051%), which yield greatest value in the sap wood. The results of bioactivity tests showed LC50 values at the leaf essential oil (11.203 μg/mL), inner bark (6.851 μg/mL), heart wood (3.968 μg/mL), and sap wood (1.293 μg/mL). These results indicate that all essential oils are bioactive against A. salina and sapwood essential oils have the highest bioactivity effect. The results of the analysis by TLC showed that essential oils from various parts of the tree is thought to contain terpenoid compounds. Keyword: Surianwood, cederwood, bioactivity, Artemia salina.
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Judul: Bioaktivitas Minyak Atsiri Pohon Suren (Toona sinensis Roemor) Berdasarkan Uji Brine Shrimp Lethality Test (BSLT)
Abstrak: Penelitian ini bertujuan untuk menetapkan rendemen minyak atsiri pada bagian daun, kayu teras, kayu gubal dan kulit serta menentukan bioaktivitas minyak atsiri dari pohon suren (Toona sinensis Roemor). Pada masa sekarang minyak atsiri suren telah diuji untuk digunakan sebagai insektisida. Oleh karena itu, minyak atsiri menarik untuk diteliti pada seluruh bagian pohon. Pohon suren yang diteliti berasal dari hutan masyarakat, di Kecamatan Cibadak, Kabupaten Sukabumi. Pohon ini memiliki tinggi ± 11 m dengan diameter ± 23 cm. Bagian pohon yang digunakan adalah daun, kulit dalam, kayu teras dan kayu gubal. Untuk menghasilkan nilai rendemen, bahan baku disuling dengan metode uap dan air. Untuk menentukan bioaktivitasnya dilakukan pengujian terhadap larva udang Artimia salina Leach. melalui metode Brine Shrimp Lethality Test. Uji Kromatografi Lapis Tipis (KLT) dilakukan untuk menduga jumlah senyawa yang terdapat pada minyak atsiri tersebut. Hasil penelitian menunjukan bahwa rendemen minyak atsiri bagian kayu gubal (0,388%), bagian kayu teras (0,379%), bagian daun (0,302%), dan bagian kulit (0,051%), dimana nilai rendemen terbesar pada bagian gubal. Hasil uji bioaktivitas menunjukan nilai LC50 minyak atsiri pada bagian daun (11,203 µg/mL), kulit (6,851 µg/mL), kayu teras (3,968 µg/mL), dan kayu gubal (1,293 µg/mL). Hasil ini menunjukan bahwa semua minyak atsiri bersifat bioaktif terhadap A. salina dan minyak atsiri kayu gubal memiliki efek bioaktifitas tertinggi. Hasil analisis dengan KLT menunjukan bahwa minyak atsiri dari berbagai bagian pohon diduga mengandung senyawa terpenoid.
Keyword: Brine Shrimp Lethality Test (BSLT), essential oil, Thin Layer Chromatography

Judul: Pengenalan Wajah dengan Citra Pelatihan Tunggal Menggunakan Algoritme VFI5 Berbasis Histogram
Abstrak: Face recognition is one of the biometric methods which is frequently used. One of the approach in face recognition is view based (photometric) approach which represents the information of the image pixels. In this research, information from the image pixels represented using the image histogram. According to Gonzales (2002), histogram are the basis for numerous spatial domain processing techniques. Histogram is used as input to Voting Feature Intervals 5 (VFI5) algorithm. Range of image histogram is 0-255 divided into several intervals. Afterwards, on training stage will be determined the vote value from single training image per person that will be used in the classification stage. The recognition rate is reached 96.3%.
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