scholarly journals Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna

Infotekmesin ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 150-154
Author(s):  
Yunita Ardilla ◽  
Wilda Imama Sabilla ◽  
Nurissaidah Ulinnuha
Keyword(s):  
Decision Tree ◽  
Ant Colony Optimization ◽  
Time Series Data ◽  
Image Data ◽  
Ant Colony ◽  
Series Data ◽  
C4.5 Decision Tree ◽  
Decision Tree Method ◽  
Time Required ◽  
Tree Method

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.

scholarly journals J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Andrea Brunello ◽  
Enrico Marzano ◽  
Angelo Montanari ◽  
Guido Sciavicco
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Time Series Data ◽  
Temporal Information ◽  
Frequent Pattern ◽  
Series Data ◽  
The Novel ◽  
C4.5 Decision Tree ◽  
Business Setting ◽  
Interpretable Models

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

The Water Conservancy Water Electricity Construction Engineering Professional Result Analysis of Application Base on C4.5 Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 703-707
Author(s):  
Hu Yong
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Construction Engineering ◽  
Improvement Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Selection Decision ◽  
Decision Tree Method ◽  
Result Analysis ◽  
Tree Method

Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclusion, provide the decision the analysis.

scholarly journals Ant Colony Decision Tree Method to Detect the Suspicious News

2019 ◽  
Vol 8 (4) ◽  
pp. 269-274
Keyword(s):  
Social Media ◽  
Decision Tree ◽  
Research Work ◽  
Optimal Solution ◽  
Ant Colony ◽  
Continuous Growth ◽  
News Content ◽  
Decision Tree Method ◽  
The Impact ◽  
Tree Method

The ease of availability and low cost of social media platforms has made the human vulnerable to devour the information from social media. Social media serves the unverified information which easily gets disseminated among people through different groups, applications, and other online social platforms. These fake news can lead to any suspicious activities which urges to derive the term suspicious news. The escalation of these suspicious news has open a lot of research ventures to detect and attenuate the impact on human. These suspicious activities have become a nuisance for legitimate users. Despite the presence of existing methods for the suspicious news detection exists, but the continuous growth of such activities is difficult to manage with an individual approach. In this research work, an ensemble approach is considered to detect suspicious news content. Here, Ant Colony Decision Tree method is ensembled for the detection of suspicious news (ACDTDSN). This approach uses the heuristic function and pheromone trail to obtain the optimal solution. The overall functionality of system is based on the content based approach for the detection of suspicious news with steps of dataset consideration, pre-processing, feature selection, and classification. The experimentation is performed using the FakenewNet dataset which consist of BuzzFeed and PolitiFact categories of news content. The results of the proposed ACSTDSN framework are accessed with the performance evaluation measures.

scholarly journals OPTIMASI PERSEDIAAN MATERIAL TRANSFORMATOR MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN DAN ANT COLONY OPTIMIZATION DI PT. PLN (PERSERO) AREA JEMBER

2019 ◽  
Vol 4 (1) ◽  
pp. 25
Author(s):  
Rizki Herdatullah ◽  
Syaiful Bukhori ◽  
Windi Eka Yulia Retnani
Keyword(s):  
Ant Colony Optimization ◽  
Time Series Data ◽  
Historical Data ◽  
Ant Colony ◽  
Series Data ◽  
Neuron System ◽  
Final Project ◽  
Future Data ◽  
Future Prediction ◽  
Linear Problems

Optimization comes from basic words optimal which mean the best, highest, most beneficial, make the best, and do optimizing (make the best, highest, etc.). Forecasting is an attempt to predict the future. Prediction can be done by studying the pattern of historical data to find a model that can show future data. This methoed is called time series data forecasting. One of many algorithm that can builds model from historical data is Artificial Neural Networks (ANN). The algoritm mimics the human neuron system so that is can solve non-linear problems, such as the forecasting of transformator demand. In the process of modeling, ANN will always update the connection weights to find the optimum weights. In this final project ANN will be trained by Ant Colony Optimization (ACO). Based on the results can be seen that ANN with ACO as learning methods can predict transformator demand with good result.

scholarly journals Penerapan Decision Tree untuk Prediksi Kepuasan Pengguna Bus Transjakarta

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Reza Hikmatulloh ◽  
Hanna Mahaerani Putri ◽  
Qurrotul Aini
Keyword(s):  
Decision Tree ◽  
Public Transportation ◽  
User Satisfaction ◽  
Confusion Matrix ◽  
Capital City ◽  
The Public ◽  
Good Classification ◽  
C4.5 Decision Tree ◽  
Decision Tree Method ◽  
Tree Method

Transjakarta buses are one of the public transportation facilities used by residents of the capital city of Jakarta. The existence of Transjakarta Buses, transportation access is easier and easier for its users. With so many users, there are several things that must be considered in the facilities and infrastructure provided by the Transjakarta Bus. User satisfaction is an assessment given by a user of a service, whether satisfied or not. This research was conducted to analyze the satisfaction of Transjakarta Bus users who are students of Syarif Hidayatullah State Islamic University Jakarta. The attributes to be taken in the study include the rates, facilities, and services provided by the Transjakarta Bus where the questionnaire results will be processed using the C4.5 Decision Tree method. The results showed that an accuracy of 92.86% was obtained with accuracy in Good Classification using Confusion Matrix.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

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