Judul: Study of Single and Ensemble Classifiers of Classification Tree and Support Vector Machine
Abstrak: A classifier is such a rule that can be used to group an object into predetermined group or classs based on its attributes. There are two types of approach to develop a classifier rules are a parametric and a nonparametric. Parametric method requires certain assumptions to obtain the best classification but not all assumptions are met so that makes it difficult for researchers. The violation of the assumptions might lead to the lack of the effectiveness and the validity results. Recently, people pay more attention to non parametric classifiers such as Support Vector Machine (SVM) and Classification Tree (CT) to overcome the violation of the assumptions of parametric method. Some resent research figured out that an ensemble of classifiers could be an effective way to improve the classification accuracy and reduce the prediction variation of a single classifier (Valentini dan Dietterich 2000). The ensemble method is combining the class predictions resulted by a set of single classifiers into a single prediction by applying a majority vote rule. Among some popular techniques a method of bagging (bootstrap agregating) by Breiman (1996) is the simplest but powerful technique. The data used in this research are simulation data and real-life data. Simulation data are used to assess and compare the performance of single and ensemble classifiers of classification tree and SVM in three different data structures: (1) a situation where the members of different classes are perfectly linear separable, (2) a situation where the members of different classes are linerseparable but not perfect and (3) a situation where the members of different classes could not be separated by a linear function. Single and ensemble classifiers of classification trees and SVM will be applied to classify the successful study of postgraduate IPB students in Statistics department enrollment 2000-2010. Our research revealed that SVM resulted better classifier compared to Classification Tree. It is valid for all three data structure under consideration. Moreover, ensemble treatment to the classifier succeeded in improving the classification performance, especiality when radial kernel function is embedded in the procedure. Ensemble SVM in real-life data with a radial kernel function has the best performance compared to other methods and is the most appropriate method to classify the successful study of postgraduate IPB students in Statistics department enrollment 2000-2010.
Keyword: classification tree, support vector machine, ensemble methods, bagging

Judul: Evaluation of Ensemble Method for Multiclass Classification on Unbalanced Data
Abstrak: Salah satu masalah yang sering muncul pada analisis klasifikasi adalah data tak seimbang. Masalah tersebut menyebabkan kesalahan prediksi pada proses klasifikasi sehingga mempengaruhi sensitivitas khususnya pada kelas minoritas. Data tak seimbang dapat ditangani menggunakan Synthetic Minority Oversampling Technique (SMOTE). Selain itu, metode ensemble digunakan pada proses klasifikasi karena mampu meningkatkan kinerja klasifikasi. Penelitian ini mengevaluasi metode ensemble dan mengombinasikannya dengan SMOTE untuk mengatasi masalah tersebut. Data yang digunakan pada penelitian ini meliputi data balance-scale, nursery, redwine quality, internet firewall dan Indeks Standar Pencemaran Udara (ISPU). Penelitian difokuskan pada random forest dan adaboost yang merupakan anggota metode ensemble dan sebagai pembanding untuk mengetahui kebaikan kinerja metode tersebut dikaji pula metode k-Nearest Neighbor (KNN) dan decision tree dari keluarga pengklasifikasi tunggal. Hasil klasifikasi dievaluasi dengan cara membandingkan metode ensemble dan pengklasifikasi tunggal berdasarkan akurasi, sensitivitas, dan spesifisitas pada kondisi data sebelum dan setelah proses SMOTE. Evaluasi hasil klasifikasi pada kelima dataset yang digunakan menunjukkan bahwa metode ensemble cenderung memberikan kinerja yang lebih baik dibandingkan dengan decision tree dan KNN. Data yang telah melalui proses SMOTE menghasilkan sensitivitas yang lebih baik, khususnya pada kelas minoritas. Kata kunci: adaboost, random forest, SMOTE, One of the problems that often arise in classification analysis is unbalanced data. This problem causes prediction errors in the classification process, so that it affects the sensitivity, especially in the minority class. Unbalanced data can be handled using Synthetic Minority Oversampling Technique (SMOTE). In addition, the ensemble method is used in the classification process because it can improve classification performance. The study evaluates the ensemble method and combines it with the SMOTE to deal with the problem. The data used in this study include balance-scale, nursery, redwine quality, internet firewall, and Air Pollution Standard Index. The study focused on random forest and adaboost in the class of ensemble methods and as a comparison to determine the good performance of this method, the k-Nearest Neighbor (KNN) and decision tree in the class of single classifier. The classification results are evaluated by comparing the ensemble and single classifier methods based on accuracy, sensitivity, and specificity on the data conditions before and after the SMOTE process. The evaluation of the classification result on the five datasets used shows that the ensemble method tends to provide better performance than decision tree and KNN. Data that has been processed with SMOTE produced a better sensitivity, especially in the minority class. Keywords: adaboost, random forest, SMOTE
Keyword: adaboost, random forest, SMOTE

Judul: Konsumsi Pangan dan Status Gizi Ibu Hamil di Kecamatan Danurejan, Kodya Yogyakarta, Daerah Istimewa Yogyakarta
Abstrak: Hasil penelitian menunjukkan bahwa rata-rata konsumsi energi per orang per hari adalah 1 525 Kalori, konsumsi protein 45,l g, konsumsi zat besi 12,8 mg dan konsumsi vitamin A 2 264 SI. Rata-rata kecukupan energi per orang per hari adalah 2 213 Kalori, kecukupan protein 75,6 g, kecukupan zat besi 29,O mg dan kecukupan vitamin A 4 500 SI.
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