Lateness arrives at work can be experienced by anyone, including teachers. Teachers who are late arriving at school have shown examples of bad behavior for students. It takes a study to determine the factors that cause a teacher to arrive late to school. Data Mining is selected to process the data that has been available. Processing uses 3 classification algorithms which are decision tree (C4.5, Random Tree, and Random Forest) algorithms. All three algorithms will be tested for known performance, where the best algorithm is determined by accuracy and AUC. The results of the research were obtained that Random Forest with pruning and pre-pruning is the best for accuracy value with 74.63% and also AUC value with 0.743. The teacher's delay in this study is often done by teachers who have a vehicle compared to those who do not have a vehicle.
The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword “Brimob” with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%.
This research revealed the model of entrepreneurial marketing, especially on Moslem fashion womenpreneurs. Recently, the opportunity for women in entrepreneurship with micro and small scale businesses (MSMEs) has increase. Womenpreneur also contributes to the field of human resource management, namely empowering women and playing a role in the nation's economy. The main reason is that they want to be independent, followed by the second rank who says that opening a business, especially the Moslem fashion business, as an effort to increase family income. Implementing the concept of entrepreneurial marketing requires supporting factors, namely market orientation, innovation, value creation, and risk-taking. The purpose of this research is to implement the model of entrepreneurial marketing on womenpreneurs. This research used a descriptive survey method through SEM (Structural Equation Models) analysis. Sample of this research is 209 Moslem fashion womenpreneurs with a small business scale in West Java from different characteristics, both shar'i Moslem fashion (veiled), semi-shari'a and trendy. This research has been reduced the dimensions of previous studies, only focus on market orientation, innovation, and value creation, without risk-taking factors because womenpreneurs have been thinking first before they start the business and they have calculated risk factors. that value creation can be formed from market orientation and innovation. As we can say, market orientation and innovation are important factors in the formation of value creation for women entrepreneurs of small scale Moslem fashion in West Java By applying these concepts, it builds an effective entrepreneurial marketing performance for womenpreneur.
Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, “transform case”, “tokenize”, “token filters by length”, “n-gram”, “stemming” were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.
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