Pengaruh Kualitas Produk, Harga, Dan Saluran Distribusi Terhadap Loyalitas Pelanggan Majalah Swa Melalui Variabel Kepuasan Pelanggan (Studi Kasus Pada Pelanggan Majalah Swa Di DKI Jakarta)

Customer loyalty is a goal that has to be achieved by a company. To be able to get loyal customers, SWA magazine needs to pay attention on the factors that influence customers\u27 loyalty. Moreover, business and economy themed magazines are mushrooming, leading to the opportunity for the readers to move from one magazine to another.This research aimed to ascertain the effect of product quality, price, and distribution channel on customer loyalty of SWA magazine in Jakarta through customer satisfaction variable both simultaneously and partially. The hypothesis was there was an effect of product quality, price, and distribution channel on customer loyalty of SWA magazine in Jakarta through customer satisfaction variable both simultaneously and partially. The type of this research was explanatory research with 97 respondents with multi stage sampling technique through questionnaire and interview. The data was analyzed using linear regression method with the assistance of SPSS 16.0.The result of this research showed that product quality, price and distribution channel variables had significant and positive effect partially on customer satisfaction. Product quality variable did not have partially significant effect on customer loyalty. Price and distribution channel variables had partially significant and positive effect on customer loyalty. Product quality and price variables had simultaneously positive and significant effect on customer satisfaction while distribution channel had simultaneously negative effect on customer satisfaction. Simultaneously, product quality, price, and distribution channel variables had positive effect and not significant effect on customer loyalty. Partially, customer satisfaction had positive and significant effect on customer loyalty.Based on the result of this research, a conclusion was drawn that customers\u27 perception on product quality, price, and distribution channel was good. Customers\u27 satisfaction and loyalty of SWA magazine were also good. The company was suggested to improving the product quality, adjusting the price and boosting the distribution channel of SWA magazine in accordance with customers\u27 needs and expectation, so that, customers can feel the satisfaction and decided to be loyal customers

ROC curves for different kinship-based subsets to evaluate the suitability of specific farm groups with the LogitBoost classifier.

<p>To calculate sensitivity and specificity, data were divided in half and used as a training and test set. Threshold-specific performance could then be monitored using continuous cutoffs based on the ROC curves. All processes were conducted for the four subsets with two approaches. The D89 class showed the lowest performance in most cases.</p

Scatter plots for four subsets with different kinship coefficient criteria (X-axis: Eigen vector 1 and Y-axis: Eigen vector 2).

<p>Scatter plots were generated by PCA using GCTA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139685#pone.0139685.ref024" target="_blank">24</a>]. Each point represents an individual animal and is colored based on the farm information. When the kinship cutoff increased, each farm was more clearly distinguishable.</p

Results of sample size and number of classes correction.

<p>Data for the three simulation analyses were generated by adjusting three factors (sample size, number of classes, or both). For the top box-plot, sample size was set at 67, which was the smallest of the four subsets. For the middle box-plot, the number of classes was set at two, which was also the smallest for the four subsets. Finally, the bottom box-plot was generated using 26 samples (the smallest sample size among all classes) for each class (binary class). To determine the classification accuracies, 10-fold cross-validations were performed. All of these processes were conducted 1000 times using 92 features. Red dots represent the previously calculated observed accuracies.</p

Application of LogitBoost Classifier for Traceability Using SNP Chip Data

<div><p>Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.</p></div

Evaluation of predicted performance according to balanced accuracy.

<p>The balanced accuracies were calculated by 10-fold cross-validation. Values represent the mean ± 10-fold variance. Figures written in bold represent a higher level of balanced accuracy than those of the other classifiers in each class. Figures given in parentheses represent the number of features used in classifiers.</p

Line plots for comparing classification accuracy according to several factors, including classifiers, feature subsets, and kinship-based filtered subsets.

<p>The X-axis contains the number of features (1 to 92 SNPs), while the Y-axis shows classification accuracy. Approach 1 is the top-down feature selection method while Approach 2 is the bottom-up feature selection technique. LogitBoost-based classification accuracy is represented by the red line. Lines corresponding to the KNN and SVM classification methods are green and blue, respectively.</p

Best classification accuracies for diverse situations (two different feature selection approaches, four different kinship filtered sets, and three classifiers).

<p>Levels of accuracy were calculated by 10-fold cross-validation and expressed as the means ± 10-fold variance. Bold represents greater accuracy than other classifiers for each kinship-based filtered subset.</p

A diagram representing the processes of building the prediction model for traceability.

<p>The prescreening process for selecting the SNP markers consists of two major steps: retrieval of common SNPs for five pig breeds and selection of SNP markers based on geographical distribution (farm location). Farms were filtered by the kinship coefficient mean and four subsets were generated. The feature selection process for removing redundant features was performed using two approaches (detailed descriptions of these techniques are provided in the manuscript) and three classifiers. Using the selected features, classification performance was evaluated based on three factors (classification accuracy, balanced accuracy, and ROC curves).</p

Additional file 1: of Genome sequencing and protein domain annotations of Korean Hanwoo cattle identify Hanwoo-specific immunity-related and other novel genes

Table S1. Summary of sequencing data (DOCX 28 kb