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The Role Of Fatherhood Towards Early Childhood In Javanese Culture: Ethnoparenting Study In Wonolelo
Get PDFThe aim of this research is to explore the role of fathers in caring for early childhood children in Javanese culture. The phenomenon of fatherlessness in parenting is increasingly widespread in Indonesia. The Javanese tribe, one of the largest ethnic groups in Indonesia, was the sample in this research. The research method used is qualitative research with an ethnoparenting approach. The research locus was determined based on the subject's domicile, which was the Wonolelo area, Pleret, Bantul, Yogyakarta. The snowball technique was used as a method in selecting respondents. Respondents totaled 14 families. Data collection techniques were carried out through direct observation and interviews. Data were analyzed using thematic analysis. The research results show that The role of the father in the Javanese tribe of Wonolelo Village is still the same as the role of most fathers in Indonesia, namely as a decision maker and as a leader, the economic provider, where the family still adheres to a patriarchal culture.
Fathers do not always directly involved in parenting at children's daily life. Fathers play more of a role as disciplinarians, role models in carrying out religious teachings, cultural and social traditions, . The mother dominates the parenting role at home. It is essential for fathers to increase their parenting role by interacting directly to meet children's psychological and physiological needs
Word Embedding Feature for Improvement Machine Learning Performance in Sentiment Analysis Disney Plus Hotstar Comments
Get PDFIn this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computations, memory limitations, and ignoring irrelevant attributes. Then RF has a weakness, namely that the evaluation value can change significantly with just a slight change in the data. Feature selection in text classification is crucial for enhancing scalability, efficiency, and accuracy. Our testing results indicate that KNN achieved the highest accuracy both before and after feature selection. The FastText feature led to the highest performance for KNN, yielding balanced accuracy, precision, recall, and F1-score values
LAPORAN MANAJEMEN PROYEK TEKNOLOGI INFORMASI PEMBUATAN WEB PROFILE DAN SISTEM PEMBELIAN BARANG MENGGUNAKAN SCAN BARCODE TOKO MITRA TANI KLATEN
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A Hybrid CNN-SVR for Airfoil Aerodynamic Coefficient Prediction
Get PDFThe prediction of aerodynamic coefficients on airfoils using machine learning is increasingly popular due to its efficiency in time and cost. Research typically focuses on a single image type without comparing various types and output quantities (single or multi-output). Although convolutional neural networks (CNN) are widely used, their final layer is often suboptimal as a linear operator, and feature extraction results contain many parameters that can still be trained. Support vector regression (SVR) with kernel functions effectively reduces common errors in feature vectors. We propose a hybrid method, AeroCNNSVR, combining CNN as a feature extractor and SVR as a regressor to predict aerodynamic coefficients on airfoils. This study focuses on the shape and position of airfoils according to the angle of attack (AoA) without considering flow conditions. Using 14533 aerodynamic coefficients from 563 airfoil types, we created a dataset of grayscale and RGB airfoil images. Results show the proposed method with grayscale images performs better because combining SVR strengthens the predictive model, while grayscale images accurately represent the airfoil's shape and position. AeroCNNSVR achieves lower RMSE values for Cl (0.101522), Cd (0.016450), and Cm (0.129661) compared to the CNN model’s Cl (0.112493), Cd (0.019060), and Cm (0.130041). Additionally, AeroCNNSVR's R² values for Cl (0.976071), Cd (0.928700), and Cm (0.860574) surpass those of the CNN model (Cl 0.970620, Cd 0.904282, Cm 0.816355). This research contributes by 1) proposing an alternative besides CFD for predicting and identifying trends in aerodynamic coefficients of airfoils in a much shorter time during the design stage; 2) offering wind tunnel practitioners for early detection of configuration errors; 3) providing an overview of the aerodynamic characteristics of the airfoil under test, including the angle at which stall conditions occur
Study of Indirect Vector Control Induction Motor Based on Takagi Sugeno Type Fuzzy Logic on Rotational Speed Control Primary Surveillance Radar
Get PDFDuring the rainy season and bad weather, strong winds blowing at an airport can cause dynamic changes in performance of the primary surveillance radar (PSR) antenna which is driven by an induction motor (IM). Changes in dynamic performance that occur in this IM can be in the form of changes in PSR rotation speed, changes in torque values, and changes in stator current values. In this article, we propose the application of the Takagi Sugeno method to fuzzy logic indirect vector control of IM as a solution that can reduce changes in the dynamic performance of motor as PSR drivers during bad weather. The contribution of this research is the application of the Takagi Sugeno method in a fuzzy inference system (FIS), where this fuzzy logic control system replaces the conventional proportional integral (PI) controller for indirect vector control IM. Takagi Sugeno method is computationally efficient and works well with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for dynamic nonlinear systems. Takagi Sugeno type FIS uses weighted average to compute the crisp output, so the Sugeno’s output membership functions are either linear or constant. Furthermore, Takagi Sugeno method has better processing time since the weighted average replace the time consuming defuzzification process. The results obtained after simulation in MATLAB Simulink environment showed that fuzzy logic using the Takagi Sugeno method which is used as a substitute controller for indirect vector control can provide better performance when compared to conventional PI controllers. These results can be seen from the response values of rotor rotation speed, electromagnetic torque, and stator current. Overall, this research provides discourse on improving the dynamic performance of IM through the application of the Takagi Sugeno fuzzy logic indirect vector control method
