Bagus Sartono

Optimal blocking for general resolution-3 designs

This paper presents a general method for finding optimal blocking arrangements of pure-level and mixed-level orthogonal designs of resolution 3. The method requires enumeration of all nonisomorphic ‘full designs’ that include the treatment factors as well as the blocking factor. For all these designs, generalized word counts expressing the aliasing between main effects and two-factor interactions as well as the aliasing among two-factor interactions are calculated. The same is done for all projections into treatment designs obtained by dropping any possible blocking factor. The generalized word counts of the full design and treatment designs then allow the selection of blocking arrangements that are optimal with respect to five criteria appropriate for blocking resolution-3 orthogonal designs. We provide optimal blocking arrangements for orthogonal pure-level and mixed-level designs of 12, 16, 18, 20, and 27 runs.

Constructing General Orthogonal Fractional Factorial Split-Plot Designs

While the orthogonal design of split-plot fractional factorial experiments has received much attention already, there are still major voids in the literature. First, designs with one or more factors acting at more than two levels have not yet been considered. Second, published work on nonregular fractional factorial split-plot designs was either based only on Plackett–Burman designs, or on small nonregular designs with limited numbers of factors. In this article, we present a novel approach to designing general orthogonal fractional factorial split-plot designs. One key feature of our approach is that it can be used to construct two-level designs as well as designs involving one or more factors with more than two levels. Moreover, the approach can be used to create two-level designs that match or outperform alternative designs in the literature, and to create two-level designs that cannot be constructed using existing methodology. Our new approach involves the use of integer linear programming and mixed integer linear programming, and, for large design problems, it combines integer linear programming with variable neighborhood search. We demonstrate the usefulness of our approach by constructing two-level split-plot designs of 16–96 runs, an 81-run three-level split-plot design, and a 48-run mixed-level split-plot design. Supplementary materials for this article are available online.

Blocking Orthogonal Designs With Mixed Integer Linear Programming

We present a mixed integer linear programming approach to orthogonally block two-level, multilevel, and mixed-level orthogonal designs. The approach involves an exact optimization technique which guarantees an optimal solution. It can be applied to many problems where combinatorial methods for blocking orthogonal designs cannot be used. By means of 54-run and 64-run examples, we demonstrate that our approach outperforms two benchmark techniques in terms of the number of estimable two-factor interaction contrasts and in terms of the D-efficiency for models with main effects and some two-factor interaction contrasts. We demonstrate the generic nature of our approach by applying it to the most challenging instances in a catalog of all orthogonal designs of strength 3 with up to 81 runs as well as a small catalog of strength-4 designs. The approach can also be applied to strength-2 designs, but, for these cases, alternative methods described in the literature may perform equally well. Supplementary materials for this article are available online.

I-Optimal Design of Mixture Experiments in the Presence of Ingredient Availability Constraints

Mixture experiments usually involve various constraints on the proportions of the ingredients of the mixture under study. In this paper, inspired by the fact that the available stock of certain ingredients is often limited, we focus on a new type of constraint, which we refer to as an ingredient availability constraint. This type of constraint substantially complicates the search for optimal designs for mixture experiments. One difficulty, for instance, is that the optimal number of experimental runs is not known a priori. To deal with this complication, we propose a variable neighborhood search algorithm to find I-optimal designs for mixture experiments in case there is a limited stock of certain ingredients.

Sequential pattern mining of rawi hadis (Case study: Shahih hadis of Imam Bukhari from software Ensiklopedi Hadis Kitab 9 Imam)

This research is about rawi data of shahih hadith Imam Bukhari. The purpose of the research is to find out the association pattern that was formed between rawi hadith in shahih Bukhari book. The data used in this research is secondary data from software Ensiklopedia Kitab 9 Imam was created by Lidwa Pusaka. Sequential pattern mining technique is the method that used to analysis the data. SPADE (Sequential Pattern Discovery using Equivalent classes) is one of sequential pattern algorithm is used to find sequence pattern in this research. This algorithm to find frequent sequence of rawi data transaction, using vertical database and sequence join process. SPADE algorithm would result frequent sequence that then used to form the rules of rawi.

Synthetic Over Sampling Methods for Handling Class Imbalanced Problems : A Review

Class imbalanced commonly found in any real cases. Class imbalanced occur if one of the classes has smaller amount, called minority class, than other class (majority class). The problem of imbalanced data is usually associated with misclassification problem where the minority class tends to be misclassified as compared to the majority class. There are two approaches should be performed to solve imbalanced data problems, those are solution at data level and solution at algorithm level. Over sampling approach is used more frequently than the other data level solution methods. This study gives review of synthethic over sampling methods for handling imbalance data problem. The implementation of different methods will produce different characteristics of the generated synthetic data and the implementation of appropriate methods must be adapted to the problems faced such as the level and pattern of imbalanced data of data available. Results of the review show that there is no absolute methods that are more efficient in dealing with the class imbalance. However, the class imbalance problem depends on complexity of the data, level of class imbalance, size of data and classifier involved. Determination of over sampling strategy will affect the outcome of the over sampling. So it is still open better development oversampling methods for handling the class imbalance. The selection classifier and evaluation measures are important to get the best results. Statistical test approach is needed to assess the theoritical propertis of synthetic data and evaluate missclassification in addition to the evaluation methods that have been used.

Ensemble Decomposition Method for Predicting the Price of Rice in Jakarta

Time series analysis is one of statistical procedures in time series data which is applied to predict the conditions that will come in the context of decision making. Generally, the huge size of data not only non linear but also non stationary and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process, the process of changing into a simpler form. Decomposition method that is Ensemble Empirical Mode Decomposition (EEMD). Decomposed time series data can also be used for prediction of the initial data. The ensemble methods can be used such as Fourier analysis used because IMF patterned sinusoid and ARIMA is used because this method is very popular in time series data. The methodology is applied to forecast weekly rice prices in Jakarta province from January 2002 to August 2013. Keyword: ARIMA, EEMD, Ensemble, Fourier Analysis, Time Series data.