PELATIHAN PENGGUNAAN E-DRAW MAX UNTUK MEMBUAT DESAIN SISTEM

Edraw Max is a 2D business technical diagram software that helps create flowcharts, organization charts, mind maps, network diagrams, floor plans, workflow diagrams, business charts and Engineering diagrams, flowcharts, graphs and mappings, besides E- Draw Max is also a vector-based diagramming software, which is usually used to make it easier to make a design. One of them is the design of a production process system in a factory that can use this application. In this training, the E-Draw Max tools will focus on making Data Flow Diagrams (DFD), which are useful for describing the flow of the created system, which consists of Context Diagrams, Level Diagrams, and Level n Diagrams. Data Flow Diagram (DFD) is a diagram that describes the flow of data from a process or information system. In DFD, there is information related to the input and output of each of these processes. DFD also has various functions, such as conveying system design, describing systems, and designing models. This training was held at the ITS NU Pekalongan Institute of Computer Technology. The purpose of this training is to help ITS NU Pekalongan S1 Computer Technology students in making system designs, specifically designing Data Flow Diagrams (DFD) using E-Draw Max tools to make it easier for students to make the flow of the system made in this case DFD on production process in Industry

Fuzzy Inference System Tsukamoto–Decision Tree C 4.5 in Predicting the Amount of Roof Tile Production in Kebumen

Tile is a product that is in great demand by many people. This has become a trigger for producers to improve their management. The company's tile production management is still experiencing problems, namely frequent miscalculations in determining the agreement that must be issued in making tile production from customer requests. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a big profit. In this study, to obtain a prediction of the amount of tile production, computerized calculations were carried out using the Tsukamoto fuzzy logic method. This method uses the concept of rules from the C 4.5 decision tree in the building to make it easier to determine the rules that are built without having to consult an expert because C 4.5 will study existing datasets to serve as a reference in forming these rules according to conditions that often occur. The modeling results produce relevant rules after being compared with the actual results. The results of the comparison of predictions with actual production have an error percentage of 29.34%, with a truth of 70.66% (based on the calculation of the Average Forecasting Error Rate (AFER)). Therefore when implemented in the Tsukamoto Fuzzy Inference System it can produce predictions of tile production that are quite optimum. It is said to be quite optimum because all customer requests are met, either generated by the production prediction itself or the prediction results are added up with inventory data, and all predictions are close to actual production

Ripensare la Heimat con la letteratura

This contribution analyzes the new narratives of the Heimat (home-country), the new facets and meanings elaborated by contemporary European literature. It explores in the most recent fiction the different cognitive perspectives and the different sensitivities and experiences fielded by the authors to redefine both the contents of the Heimat and the feelings and emotions it evokes. There is an interesting effort to rethink the traditional concept of Heimat as the founding nucleus of personal, ethnic and cultural identity; to find other ways to describe their idea of home-country, corresponding to the change in lifestyles and cultural priorities. And an expansion of the relative semantic field is evident, which crosses integration, inclusiveness, supranational opening

Dance Gesture Recognition using Laban Movement Analysis with J48 Classification

This study describes the introduction of classical dance movements using the Laban Movement Analysis (LMA) method which consists of 3 main components, namely Body, Space, and Shape. How to carry out the classical motion recognition process using Kinect which is then read by the screen using the Brekel Kinect and produces dance motion pictures in different formats (. * BVH). After that, it is calculated using the LMA method by obtaining the results obtained in the form of numerical data from each joint from the direction of the axis (xyz), then classification is carried out using the J48 classification method provided at WEKA tools after 50 training data is carried out. 96% truth is recognized, because it guarantees those who meet the requirements, 12 data tests are carried out apart from training data, which can be 92% accurate on average, so it is very possible that this method can be used in dance preparation, especially in classical dance

Selection bias in the M_BH-sigma and M_BH-L correlations and its consequences

It is common to estimate black hole abundances by using a measured correlation between black hole mass and another more easily measured observable such as the velocity dispersion or luminosity of the surrounding bulge. The correlation is used to transform the distribution of the observable into an estimate of the distribution of black hole masses. However, different observables provide different estimates: the Mbh-sigma relation predicts fewer massive black holes than does the Mbh-L relation. This is because the sigma-L relation in black hole samples currently available is inconsistent with that in the SDSS sample, from which the distributions of L or sigma are based: the black hole samples have smaller L for a given sigma or have larger sigma for a given L. This is true whether L is estimated in the optical or in the NIR. If this is a selection rather than physical effect, then the Mbh-sigma and Mbh-L relations currently in the literature are also biased from their true values. We provide a framework for describing the effect of this bias. We then combine it with a model of the bias to make an estimate of the true intrinsic relations. While we do not claim to have understood the source of the bias, our simple model is able to reproduce the observed trends. If we have correctly modeled the selection effect, then our analysis suggests that the bias in the relation is likely to be small, whereas the relation is biased towards predicting more massive black holes for a given luminosity. In addition, it is likely that the Mbh-L relation is entirely a consequence of more fundamental relations between Mbh and sigma, and between sigma and L. The intrinsic relation we find suggests that at fixed luminosity, older galaxies tend to host more massive black holes.Comment: 12 pages, 7 figures. Accepted by ApJ. We have added a figure showing that a similar bias is also seen in the K-band. A new appendix describes the BH samples as well as the fits used in the main tex

Subjectivity Tracking System for Poor Scholarship Recipients at Elementary School Using the MOORA Method

This research was conducted because of complaints from several parents at Elementary School regarding recipients of the Poor Student Assistance (PSA) who were still less objective. Elementary School XY regularly conduct screening activities every year to select prospective PSA recipients. This selection is made so that the recipients of this assistance are students entitled to it. Some students should be accepted as a selection committee but do not mistake of choosing some students who have kinship or subjective matters. Therefore, this study aims to explore and create applications that apply the Multi Objective Optimization to the basic  Ratio Analysis (MOORA) method, which is a method for determining students based on predetermined criteria. The criteria used are the value of report cards, student achievement, student activity, parental income, parental dependents, and home conditions. After conducting a search and implementation using the MOORA method in determining PSA recipients, it was found that there were some non-objective results where the student's criteria and final results were lower than some other students. However the Elementary School provided a recommendation to get PSA. If this happens again, then the importance of this system is to help objective selection. The accuracy results explained that 14.39% of PSA recipients were subjective. It was concluded that this research helps an objective decision and facilitates the decision maker in determining the best 3 recipients from each class at Elementary School XY