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    A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform
    (Springer International Publishing Ag, 2020) Atagun, Ercan; Argun, Irem Duzdar
    With the development of internet technologies, the use of video sharing sites has increased. Video sharing sites allow users to watch videos of others. In addition, users can create an account to upload content and upload videos. These platforms stand out as the places where individuals are both producers and consumers. In this study, data about YouTube which is a video sharing site was used. The content of the content, which is also called as a channel on YouTube, was made by using a set of producers. The data set with 5000 samples on YouTube channels is taken from Kaggle. The data were classified using 4 different data mining tools such as Weka, RapidMiner, Knime and Orange using Naive Bayes and Random Forest algorithms. The parameters are requested from the user in order to obtain a more efficient result in the application of data mining algorithms and in the data preprocessing steps and in the data mining steps. Although these parameters are common in some data mining software, they are not included in all data mining software. Data mining software provides management of some parameters while other parameters cannot be managed. These changes affect the accuracy value in the study and affect the accuracy value in different ratios. Changing the values of the parameters revealed differences in the accuracy rates obtained. A data mining software model has been proposed by emphasizing to what extent the management of the parameters of the study and the extent of the management of the parameters should be connected to the data mining software developer.
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    Models for Energy Efficiency Obligation Systems through different perspectives
    (Elsevier Sci Ltd, 2021) Argun, Irem Duzdar; Kayakutlu, Gulgun; Ozgozen, Neslihan Yilmaz; Daim, Tugrul U.
    As environmental problems such as global warming has become increasingly prevalent, and with the rise in foreign dependence on fossil fuels, energy efficiency and energy saving programs and research have risen. This global focus on energy efficiency has accelerated studies on this subject in order to decrease foreign dependency on fossil fuels, to protect the environment, to minimize the effect of energy costs to national economies, and to assure the energy supply. With increasing population and fast economic development, energy consumption in Turkey has increased significantly. Therefore, current policies need to be updated and additional measures need to be implemented. The Energy Efficiency Strategy document states that Turkey aims to decrease its energy intensity by 20% by 2023, and accordingly new policies and strategies are being carried out in every sector to achieve this result. The uninterrupted, cost-effective, and globally sustainable energy supply is at the heart of national energy policies globally. The Energy Efficiency Obligation Scheme (EEOS) is among the fundamental tools created to increase energy efficiency in the European Union (EU). Many EU countries have successfully implemented EEOS, and further white certificate markets as a central tool for increasing energy efficiency. For example, Italy has seen particularly positive achievements with a white certificate market by avoiding consumption of 6.7 million tons of oil equivalent (TOE). The implementation of National Energy Efficiency Action Plan (NEEAP) in terms of energy saving was a great step in Turkey. In this document, EEOS has been analyzed in detail. All of these studies are expected to shed light on the energy efficiency liability system to be implemented in Turkey in the future. For this purpose, it is thought that the inclusion of energy service companies into these models will have a positive effect on the success of the system. Within the scope of this study, it has been concluded that energy resources and technologies should have the flexibility to be localized and to resist unexpected changes. Another reason to encourage incentive policies in energy efficiency will be to reduce energy importation.
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    Prediction of Potential Bank Customers: Application on Data Mining
    (Springer International Publishing Ag, 2020) Basarslan, Muhammet Sinan; Argun, Irem Duzdar
    Banking is an important industry, where financial transactions are performed to meet our needs in our everyday lives. Today, banks are frequently used to meet all kinds of financial transactions. In line with the increasing competition, the banks are aiming at acquiring new customers through customer satisfaction. At this point, studies on acquiring new customers by analyzing the customer data have gained importance recently. As a result, data analysis units have been established in the banks. In addition to the banks, these units have also been established for data analysis in customer focused industries such as insurance and telecommunication. In this study, models are established by using classification algorithms to estimate potential bank customers on the bank dataset obtained by telemarketing method in UCI Machine Learning Repository, and the results are compared. Using this comparison result, it is aimed to perform a more detailed and effective data analysis. Various models have been established with various classification algorithms for the estimation of customer acquisition. The classification algorithms used in this study include the C4.5 Decision Tree, Navie Bayes (NB) algorithm, K nearest neighbors algorithm (k-nn), Logistic Regression algorithm (LogReg), Random Forest algorithm (RanFor), and Adaptive Boosting algorithm (AdaBoostM1-Ada). While establishing the classification models, it is aimed to achieve consistency in the performance of the classification models by dividing the test and training data set by two different methods. K-fold Cross Validation and Holdout methods are used for this purpose. In the K-fold cross validation, training and test da-ta sets are separated with 5- and 10-fold cross validation. In the holdout method, the dataset was divided into training and test datasets with the 60-40%, 75-25% and 80-20% training and test separation ratios, respectively. These separations are evaluated for Accuracy (ACC), Precision (PPV), Sensitivity (TPR), and F-measure (F) performance. The performance results are similar in both separation results. According to the Accuracy and F-measure criteria, the classification model established by Random Forest algorithm highest results the other models, whereas the Naive Bayes algorithm gave highest results according to the precision criterion, and the AdaBoostM1 classification algorithm yielded better according to the sensitivity criterion.
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    Result of Digitalization in the Automotive Industry: Total Equipment Effectiveness and Bayesian Analysis
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Argun, Irem Duzdar; Kilic, Sedef Akyol
    Businesses today try to accommodate the fast-developing world through prioritizing high productivity, low cost, customer satisfaction, and most time-saving with the help of digitalization. The automotive industry as one of the greatest in global markets also finds its place in digitalization studies. The company analyzed in this article is producing automotive parts and investing in machines and software to enable digitalization. The aim of the firm is to raise the facilities' productivity in the digitalization process. The overall equipment efficiency study carried out in practice was carried out in order to observe the productivity change in the unit where the application was made with are proposed the digitalization transformation in the company. This article considers the total equipment effectiveness and Kaizen is applied for the interruptions with negative impacts on productivity. The productivity of the company is significantly increased after the Kaizen application. In order to digitalize the manufacturing processes successfully, more expert opinions are required. The Bayesian network (BN) is used to achieve higher increase at productivity. It has a powerful probability theory eliminating the inconsistent probability. During the implementation phase, the most important part of this article is the employment of components of the total equipment effectiveness as the variable for BN. The utilizing the expert opinions resulted to advance productivity. At the end of the Kaizen study, the productivity is raised from 83% to 85%. According to the results of the studied BN, the required suggestions to the company.