Judul: Klasifikasi Habitat Perairan Dangkal Dari Citra Satelit Quickbird Menggunakan Metode Kecerdasan Buatan
Abstrak: Pendekatan ANN-SOM dapat mengenalisasi data dalam bentuk pengelompokkan berdasarkan radius ketetanggaan pixel dalam dimensi tinggi. Pendekatan ini merupakan salah satu teknik klasifikasi ANN secara unsupervised. Pendekatan ANN secara supervised menarik lainnya adalah single layer neuron (lapisan dua layer) dalam sistem ANN-BP dapat dioptimalkan dengan algoritma ANN-AdaBoost menggunakan kalman filtering (Freund dan Shapire 1996). Kenyataan ini menjadi penting artinya bahwa perlunya suatu percobaan dan pengembangan metode klasifikasi citra satelit untuk memetakan habitat perairan dangkal di Indonesia dengan berupaya meningkatkan accuracy assessment data selama proses penelitian. Penelitian ini dilaksanakan di Kepulauan Seribu Provinsi DKI Jakarta dari Maret sampai Juni 2009. Penelitian ini bertujuan mengembangkan metode klasifikasi algoritma ANN untuk memetakan skema klasifikasi dan menguji tingkat akurasi habitat perairan dangkal dari citra satelit Quickbird standar algoritma BP dan AdaBoost. Metode pengumpulan data primer dilakukan secara stratified random sampling. Data primer dikumpulkan melalui data remote sensing dan survey lapang, sedangkan data sekunder dikumpulkan dari penelitian yang relevan. Analisis klasifikasi citra digital menggunakan klasifikasi ANN unsupervised algoritma SOM dan ANN supervised algoritma BP dan AdaBoost. Hasil analisis klasifikasi ANN unsupervised SOM untuk klaster habitat perairan dangkal dari citra satelit Quickbird menunjukkan pola dan performa yang baik setelah data input dikoreksi menggunakan metode Deep Invariant Index (algoritma Lyzenga).
Keyword: 

Judul: Klasifikasi habitat perairan dangkal dari citra satelit quickbird menggunakan metode kecerdasan buatan
Abstrak: This research was conducted in Seribu Islands of DKI Jakarta province, from March to June 2009. The research objectives were to develop methods of ANN classification algorithm to map shallow water habitats, and to test the classification accuracy rate from image Quickbird satellite data with the standard method of ANN BP and AdaBoost algorithms. Primary data collected through remote sensing data and field surveys, while secondary data were collected from relevant research. Classification of digital image analysis using unsupervised classification ANN-SOM algorithm and supervised classification of BP and AdaBoost algorithm. The results showed that ANN-SOM algorithm to cluster shallow water habitats by Quickbird satellite show a pattern and a good performance after the input data is corrected using the method of invariant Deep Index (Lyzenga algorithm); ANN-BP and ANN-AdaBoost algorithm can mapp shallow water habitats classess; live coral, dead coral, sand, seagrass, sand mixed seagrass, sand mix coral; ANN-BP algorithm requires a number of iterations of 5.600 to recognize objects with cross entropy 0.20, while the AdaBoost algorithm requires the number of iterations 280, relatively little with quadratic error 0:24 until iteration stopping; level of classification accuracy thematic shallow water habitats training ANN-BP algorithm is obtained overall accuracy of 82.79% and 83.61% ANN-AdaBoost. Correction position shows the value of Delta E ranges between 0.4 - 6.7 meters, which explains that the positioning accuracy is better, although not optimal as using Differential GPS.
Keyword: 

Judul: Blood Bicchemistry Profile of Pregnant Women Consumed Fortified Cookies with Iron (Fe), Folic Acid, Vitamin A, Vitamin C, Zinc (Zn), and Iodine
Abstrak: The aim of this study was to analyze the effect of fortified cookies on blood biochemistry profile of pregnant women. For this purposed, an experimental study design was applied among 269 physically healthy pregnant women in Leuwiliang and Cibungbulang, Bogor for four treatment groups (I, 11, 111, IV) and control group (V), which given one type of formula cookies for each. The five type of formula are: (1) formula A, was fortified with Fe, folic acid, vitamin A and vitamin C; (2) formula B, was fortified with Fe, folic acid, vitamin A , vitamin C, and Zn; (3) formula C, was fortified with Fe, folic acid, vitamin A, vitamin C, and iodium; (4) formula D, was fortified with Fe, folic acid, vitamin A , vitamin C, Zn, and iodium; (5) formula E, was not fortified (control). The different formula were given to different group of women every two days during gestation period.
Keyword: