Development of information technology (IT) has been driven changes in various fields, one of which is in marketing. The use of IT in marketing will not be separated from internet, where e-commerce services have now emerged that allow businesses to conduct face-to-face transactions by removing barriers related to distance in interacting with customers, so that every stakeholder will have same opportunity for promoting their products. On the other, Indonesia is country fourth largest population in the world, where 99.8 percent of its business units are small and medium enterprises (SMEs). Although the number of business units in the SMEs sector is very large, ecommerce adoption is still dominated by large business units, so research related to the adoption of e-commerce by SMEs is still very limited, especially in developing countries. This study aims to fill that research gap. In this study a TOE framework will be examined to determine the determinants of e-commerce adoption by SMEs. TOE framework considers technology, organization and environment variables to measure adoption of information technology. Analysis of TOE framework will consider empirical literature. This study also makes recommendations for future research.

The world of banking requires a marketer to be able to reduce the risk of borrowing by keeping his customers from occurring non-performing loans. One way to reduce this risk is by using data mining techniques. Data mining provides a powerful technique for finding meaningful and useful information from large amounts of data by way of classification. The classification algorithm that can be used to handle imbalance problems can use the Random Forest (RF) algorithm. However, several references state that an optimization algorithm is needed to improve the classification results of the RF algorithm. Optimization of the RF algorithm can be done using Bagging and Genetic Algorithm (GA). This study aims to classify Bank Marketing data in the form of loan application receipts, which data is taken from the www.data.world site. Classification is carried out using the RF algorithm to obtain a predictive model for loan application acceptance with optimal accuracy. This study will also compare the use of optimization in the RF algorithm with Bagging and Genetic Algorithms. Based on the tests that have been done, the results show that the most optimal performance of the classification of Bank Marketing data is by using the RF algorithm with an accuracy of 88.30%, AUC (+) of 0.500 and AUC (-) of 0.000. The optimization of Bagging and Genetic Algorithm has not been able to improve the performance of the RF algorithm for classification of Bank Marketing data.

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The increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Data mining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve Bayes performs well in terms of classification. However, optimization is needed in order to obtain more optimal classification results. Optimization techniques in handling imbalance data have been developed with several approaches. Bagging and Genetic Algorithms can be used to overcome imbalance data. This study aims to compare the accuracy level of the naïve Bayes algorithm after optimization using the bagging and genetic algorithm. The results showed that the combination of bagging and a genetic algorithm could improve the performance of Naive Bayes by 4.57%.

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.