TY - GEN
T1 - Text Classification and Visualization on News Title Using Self Organizing Map
AU - Christyawan, Tomi Yahya
AU - Mahmudy, Wayan Firdaus
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The presence of news articles in the world wide web (www) is growing very fast that give the challenge to analyze, organize, and label the documents. A method for analyzing document category is classification. However, the problems in text classification are visualization on the process and result. This study proposes the visualization of Self Organization Map (SOM) process for text classification of news title data. The visualization of topographic map of SOM may be used to gather new knowledge about the training process of neural network and enable the researcher to figure out the best parameter value for the SOM and also detect possible outlier data. From the numerical experiment, SOM proved reliable in classification with the highest accuracy 82.05%, and average 81.87% and it is better than the Naive Bayes method. SOM proved the capability to visualize the learning process and the results. SOM visualization can detect and show outliers of training data as a new knowledge.
AB - The presence of news articles in the world wide web (www) is growing very fast that give the challenge to analyze, organize, and label the documents. A method for analyzing document category is classification. However, the problems in text classification are visualization on the process and result. This study proposes the visualization of Self Organization Map (SOM) process for text classification of news title data. The visualization of topographic map of SOM may be used to gather new knowledge about the training process of neural network and enable the researcher to figure out the best parameter value for the SOM and also detect possible outlier data. From the numerical experiment, SOM proved reliable in classification with the highest accuracy 82.05%, and average 81.87% and it is better than the Naive Bayes method. SOM proved the capability to visualize the learning process and the results. SOM visualization can detect and show outliers of training data as a new knowledge.
KW - high dimensional data reduction
KW - neural network
KW - self organizing map
KW - text classification
KW - visualization
UR - https://www.scopus.com/pages/publications/85065227522
U2 - 10.1109/SIET.2018.8693189
DO - 10.1109/SIET.2018.8693189
M3 - Conference contribution
AN - SCOPUS:85065227522
T3 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
SP - 332
EP - 336
BT - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018
Y2 - 10 November 2018 through 12 November 2018
ER -