Judul: Sistem Prediksi Status Gizi Balita dengan Menggunakan Support Vector Regression
Abstrak: Children under five years are the most vulnerable age group in terms of nutrition and health in a community. Nutritional problems often occur at the age of five, but the diagnosis of malnutrition is still done by directly measuring nutrition indicators such as weight, height or biochemical markers of some nutrients. With the technological advances in computing and statistical data processing program, the available non-nutritional data can be used to predict the nutritional status of children. The objective of this study was to investigate the use of support vector regression (SVR) as a machine-learning method to find models that can predict the nutritional status of children and to develop prediction system from the SVR models. The best model was produced by RBF kernel, with the highest degree of correlation and the lowest error in each type of Z-score predicted. With the best SVR model, a system that can predict Z-score can be developed, although it is not quite accurate.
Keyword: children, machine-learning, nutrition, prediction, SVR

Judul: Penerapan Metode CART dan Agregat Bootstrap dalam Klasifikasi Status Gizi Balita Di Kecamatan Taman Sari, Kabupaten Bogor.
Abstrak: Nutrition problem is a multidimensional problem that affect health and productivity of Indonesian society. The number of short nutritional status (stunting) and high malnutrition is a serious health problem because it picturizes life for the next 100 years. About 90% of the short children problems in the world occur in 36 countries, and the rank of Indonesia is number five. The purpose of this study is to determine the factors that affect the nutritional status of children and evaluate the stability of classification trees with bootstrap aggregating method.The research is conducted at Taman Sari subdistrict, Bogor with the number of samples 120 mothers who have toddlers. Statistical analysis used to analyze the factors that influence the nutritional status of children is the method of CART (Classification and Regression Tree). CART is a method of data exploration which uses decision tree technique. This study also applies bagging method (bootstrap aggrergating) to evaluate the stability of classification trees generated by CART. The results of the classification tree formed produces optimum tree with eight terminal nodes. Explanatory variables into the main node is the nutritional behavior. Level of accuracy for the CART method was 69.17%.The percentage of the classification on the delimitation of the best z using z-score -2.5 SD with level of accuracy 76.67% and 88.33%, and z-score -3 SDwith level of accuracy 73.33% and 79.17%. Bagging method has been able to evaluate the stability of classification trees by 79.28%.
Keyword: bootstrap aggregating, CART, the nutritional status of children

Judul: Sintesis 1-Kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai Prekursor Asam (S)-(+)-(6-Metoksi-2-naftil)propanoat
Abstrak: Senyawa 1-kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai prekursor asam (S)-(+)-(6-metoksi-2-naftil)propanoat disintesis dari bahan awal 2-metoksinaftalena. Tahapan sintesis diawali dengan klorinasi menggunakan 2 jenis pereaksi klorinasi, yakni isosianurat terklorinasi dan NaOCl. Klorinasi dengan isosianurat terklorinasi berhasil menyintesis 1-kloro-2-metoksinaftalena sebagai produk monoklorinasi melalui pencirian dengan kromatograf gas-spektrometer massa (GC-MS). Nisbah konversi produk paling efisien diperoleh pada laju penambahan 2.5 mmol/jam. Sebaliknya, klorinasi dengan NaOCl menghasilkan produk triklorinasi. 1-Kloro-2-metoksinaftalena selanjutnya diasilasi dengan propanoil klorida, tetapi analisis dengan GC menunjukkan bahwa produk asilasi tidak terbentuk. Sistem bebas-air diperlukan untuk keberhasilan proses asilasi tersebut.
Keyword: isosianurat terklorinasi, NaOCl, nisbah konversi