Decision Support System Design for Informatics Student Final Projects Using C4.5 Algorithm

Academic consultation activities between students and academic supervisors are necessary to help students carry out academic activities. Based on the transcript of grades obtained, many students do not choose the appropriate final project/thesis specialization fields based on their academic abilities, resulting in a lot of inconsistencies between the course grades and the final project specialization fields. The purpose of this research is to minimize the subjectivity aspect of students in choosing their final project academic supervisors and minimize the inconsistencies between the course grades and the final project specialization fields. The method used in this research is classification data mining using the Decision Tree and C4.5 Algorithm methods, with the attributes involved being courses, course grades, and specialization courses. The C4.5 Decision Tree algorithm is used to transform data (tables) into a tree model and then convert the tree model into rules. The implementation of the C4.5 Decision Tree algorithm in the specialization field decision support system has been successfully carried out, with an accuracy rate of 70% from the total calculation data. The data used in this research is a sample data from several senior students in the Informatics program at Ubhara-Jaya. The results of the research decision support system can be used as a good recommendation for the Informatics program and senior students to direct their final project research. It is expected that further research will use more sample data so that the accuracy rate will be better and can be implemented in website or mobile-based applications

Use of decision trees algorithm for the territorial logistic planning.

Data mining, and in particular decision trees have been used in different fields: engineering, medicine, banking and finance, etc., to analyze a target variable through decision variables. The following article examines the use of the decision trees algorithm as a tool in territorial logistic planning. The decision tree built has estimated population density indexes for territorial units with similar logistics characteristics in a concise and practical way

A Forest of Possibilities: Decision Trees and Beyond

Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ensemble methods like Random Forests. Additionally, it covers handling categorical data, imbalanced datasets, missing values, and hyperparameter tuning. Decision trees are valuable for feature selection and model interpretability. However, they have drawbacks, such as overfitting and sensitivity to data variations. Nevertheless, they find applications in fields like finance, medicine, and natural language processing, making them a critical topic in machine learning

Rapid Identification Of Aspergillus Spp. Using A Pcr Based Melting Curve Method And Characterization Of A Novel Chitinase In Insect Resistant Maize Lines

Identification of fungal isolates is critical in studying Aspergillus flavus ecology and for developing methods to reduce aflatoxin contamination. In our efforts to track biocontrol applications of the atoxigenic A. flavus K49 (NRRL 30797), we have developed a rapid and accurate classification system for A. flavus based on PCR product melting temperatures (Tm). Using 18 primers and a total of 59 Aspergilli strains, including all 49 representatives of the Georgian peanut Vegetative Compatibility Groups (VCGs), a decision tree Tm flowchart was generated. The decision tree can classify all 59 strains using only 9 of the SSR primers and an average of 3.4 primers for each definitive classification. To confirm the effectiveness of the decision tree for strain identification, unknown samples isolated from experimental fields inoculated with various A. flavus strains in Stoneville, MS were analyzed. Ninety-six percent of the samples could be placed into a VCG using Tm(s) coupled with the decision tree. This dynamic system is an excellent tool for researchers studying biodiversity of A. flavus

Rapid Identification Of Aspergillus Spp. Using A Pcr Based Melting Curve Method And Characterization Of A Novel Chitinase In Insect Resistant Maize Lines

Identification of fungal isolates is critical in studying Aspergillus flavus ecology and for developing methods to reduce aflatoxin contamination. In our efforts to track biocontrol applications of the atoxigenic A. flavus K49 (NRRL 30797), we have developed a rapid and accurate classification system for A. flavus based on PCR product melting temperatures (Tm). Using 18 primers and a total of 59 Aspergilli strains, including all 49 representatives of the Georgian peanut Vegetative Compatibility Groups (VCGs), a decision tree Tm flowchart was generated. The decision tree can classify all 59 strains using only 9 of the SSR primers and an average of 3.4 primers for each definitive classification. To confirm the effectiveness of the decision tree for strain identification, unknown samples isolated from experimental fields inoculated with various A. flavus strains in Stoneville, MS were analyzed. Ninety-six percent of the samples could be placed into a VCG using Tm(s) coupled with the decision tree. This dynamic system is an excellent tool for researchers studying biodiversity of A. flavus

Prediction of Heart Disease Using Machine Learning Algorithms

The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification

التنبؤ بمرض السكري وأنواعه باستخدام تنقيب البيانات

The research problem lies in predicting diabetes and using data mining to predict type 1 and type 2 diabetes. Data mining and analysis has become a widespread study in recent times and it can be applied to various fields where this method extracts unspecified data elements. The researcher is studying the possibility of using data mining to predict diabetes of the first and second types, and determining the appropriate method for predicting diabetes using the descriptive and analytical approach by mining the data. There are models used in the prediction process in general. We will choose from them the decision tree and the linear regression and make a comparison between them. In accuracy, precision, Recall and F measure using Rapid Miner. The researcher used the data (Pima Indians diabetics) that contain 769 records and 9 characteristics. When executing the linear regression algorithm inside the Rapidminer، we get a (accuracy = 76.09%)، (precision = 79.14%)، (Recall = 86.00%) and (F measure = 82.43%) and upon implementing the decision tree we got (accuracy = 70.87%)، (precision = 71.28%)، ( Recall = 92.67%) and (F measure = 80.58%). By comparing the results we obtained، we find that linear regression is better than the decision tree in predicting the type of diabetes. Keywords: data mining، rapidminer، decision tree، linear regressio