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Bandung, Indonesia

Purbasari A.,Pasundan University | Suwardi I.S.,Bandung Institute of Technology | Santoso O.,Bandung Institute of Technology | Mandala R.,Bandung Institute of Technology
Telkomnika (Telecommunication Computing Electronics and Control)

This research conducted experiments on population-based heuristic parallel algorithms, which are inspired by the clonal selection, called Clonal Selection Algorithm (CSA). Course-grained parallelism model applied to improve execution time. Inter-process communication overhead is addressed by adjusting communication frequencies and size of data communicated. Experiments on six parallel computing models represent all possible partitions and communications and using data of NP-Problem, Traveling Salesman Problem (TSP). The algorithm is implemented using model of message passing libraries MPJExpress and ran in a cluster computation environment. Result shows the best parallelism model is achieved by partitioning initial population data at the beginning of communication and the end of generation. Communication frequency can be up to per 1% of a population size generated. Using four dataset from TSPLib, experiments show the effect of communication frequency that increased best cost, from 44.16% to 87.01% for berlin52.tsp; from 9.61% to 53.43% for kroA100.tsp, and from 12.22% to 17.18% for tsp225.tsp. With eight processors, using communication frequency will be reduced execution time e.g 93.07%, 91.60%, 89.60%, 74.74% for burma14.tsp, berlin52.tsp, kroA100.tsp, and tsp225.tsp respectively. We conclude that frequency of communication greatly affects execution time, and also best cost. It improved execution time and best cost. Source

Iskandar D.A.,Pasundan University
WSEAS Transactions on Power Systems

From cognitive model approach perspective, a person with major depressive disorder has commonly five depression symptoms i.e. affective, cognitive, motivational, physical and behavioral symptoms. Cognitive model expresses high possible dependency on a person with depression syndrome as one real form of behavioral symptom. High possibility on one person with depressive disorder tends to give him/her high possibility for interpersonal dependency, one dependency one experienced by a person making another person the dependent object. Used research method is qualitative with interview approach equipped with observation method. The population includes people who have been diagnosed for mayor depression by both psychologists and psychiatrist. Sampling has been made using theory based/operation construct in which the sample is selected by certain criteria, based on theory or operational depression construct included into DSM IV TR. There are various interpersonal dependencies on some people with depressive disorder classified into four dimensions, cognitive, motivational, affective and behavioral whereas cognitive and affective dimension have more dominant roles. In addition, there is one extrinsic factor on person with mayor depressive disorder which has role in the process of interpersonal dependency emergence. The factor is parenting style. Source

Asep E.K.,University Putra Malaysia | Asep E.K.,Pasundan University | Jinap S.,University Putra Malaysia | Jahurul M.H.A.,Universiti Sains Malaysia | And 2 more authors.
Innovative Food Science and Emerging Technologies

Cocoa butter was successfully extracted from cocoa liquor by supercritical carbon dioxide (SC-CO2) at 35 MPa, 60°C and 2 mL/min with 5%, 15% and 25% cosolvents. The extraction yield of triglycerides (TG) and fatty acid (FA) compositions were significantly influenced by the concentration of polar cosolvents. The SC-CO2 extraction efficiency was increased with cosolvent significantly. Ethanol was found to be the best cosolvent for cocoa butter extraction using SC-CO2 followed by isopropanol and acetone. The triglycerides of 1,3-dipalmitoyl-2-oleoylglycerol (POP), 1-palmitoyl-2-oleoyl-3-stearoyl-glycerol (POS) and 1,3-distearoyl-2-oleoyl- glycerol (SOS) were contained in the extracted cocoa butter with POS being the major component. Where palmitic, stearic and oleic were the main fatty acids in the cocoa butter samples, with stearic being the highest component. The lower molecular weight (MW) of TGs and FAs showed the higher selectivity compared to the high MW of TGs and FAs. Thus, they were fractionated during the first stage of SC-CO2 process. Industrial relevance The cocoa butter was successfully extracted from cocoa liquor by SC-CO2 at 35 MPa, 60°C and 2 mL/min using different concentrations of polar cosolvents (ethanol, isopropanol and acetone). The extraction yield was significantly (p < 0.05) influenced by the concentration of polar cosolvents. Similarly, polar cosolvent concentration had significant (p < 0.05) effects on the TG and FA compositions. Ethanol was found to be the most efficient polar cosolvent for cocoa butter extraction compared to isopropanol and acetone. POS (42.2-45.9%) being the major triglycerdies component, followed by SOS (27.6-31.4%) and POP (20.3-22.7). Palmitic, stearic and oleic acids were the main fatty acids in the extracted cocoa butter, with stearic being the highest (34.9-37.8%), followed by oleic (30.3-31.8%) and palmitic (28.3-30.0%) acids, respectively. The choice of modifiers becomes a great challenge and ethanol was shown to be the best polar cosolvent, and it enhanced the solubility during the cocoa butter extraction by SC-CO2. This method can be feasibly implemented in the cocoa industry for the production of high quality cocoa butter. © 2013 Elsevier Ltd. All rights reserved. Source

Sagadavan R.,University of Technology Malaysia | Djauhari M.A.,Pasundan University | Mohamad I.,University of Technology Malaysia
IEEE International Conference on Industrial Engineering and Engineering Management

In statistical process control, monitoring process target is as important as process variability. In multivariate setting, the latter is still in development due to the complexity of multivariate variability measure. That is why the former is more popular than the latter. The most widely used measure of multivariate variability is the so called generalized variance (GV). In order to monitor GV, we need to estimate the population generalized variance and its square. In the literature, those estimates are given based on single sample. Only recently, it has been developed for the case of m independent samples with equal sample size. This motivates us to further develop in this paper for the case of m independent samples with unequal sample sizes which is usually encountered in service industry. An example of GV control charting for unequal sample sizes will be presented to illustrate the advantages of this method of estimation in monitoring the quality of service. © 2014 IEEE. Source

Lee S.L.,University of Technology Malaysia | Djauhari M.A.,Pasundan University | Mohamad I.,University of Technology Malaysia
IEEE International Conference on Industrial Engineering and Engineering Management

In past literature, a primary solution to deal with autocorrelated process data consists of two steps, namely (i) time series model building and (ii) control charting based on the residuals. However, it requires some sophisticated statistical skills to build a satisfactory model during the first step. This has motivated us to propose a new procedure of time series model building. If traditionally time series model building is based on autoregressive integrated moving average (ARIMA) models, in this paper we show that a great number of time series data are governed by geometric Brownian motion (GBM) law. If the process is governed by GBM law, the appropriate model is directly derived from the properties of that law. Otherwise, the model is constructed by using the standard practice. An industrial example is presented to illustrate the advantages of the proposed method. © 2014 IEEE. Source

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