TY - GEN
T1 - Clustering of Female Avatar Face Features Consumers Choice using KMeans and SOM Algorithm
AU - Megawati, Citra Dewi
AU - Yuniarno, Eko Mulyanto
AU - Nugroho, Supeno Mardi Susiki
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Games are interactive activities that is popular with consumers. However, illustrators are unable to determine of characters face many chosen with consumers. The paper proposes a clustering of face features often chosen by consumers for their female avatars. The face features involved in this research are the face shape, eyebrows, eyes, nose, lips, ears and skin color. The paper uses two methods for clustering, KMeans and SOM. These methods used class partitioning based on shape similarities. Using the K-Means results, 14% chose a triangular face, then 16% chose an diamond face shape, 23% chosen a heart face shape, 6% chosen a round face shape, 23% chosen a oval face shape, 18% chosen a square face shape. Results from SOM shows that 18% chose a triangular face, then 11% chose an diamond face shape, 24% chosen a heart face shape, 22% chosen a round face shape, 12% chosen a oval face shape, 13% chosen a square face shape. It shows that KMeans has better performance than SOM in determining the female avatar face classes that are wanted by customers.
AB - Games are interactive activities that is popular with consumers. However, illustrators are unable to determine of characters face many chosen with consumers. The paper proposes a clustering of face features often chosen by consumers for their female avatars. The face features involved in this research are the face shape, eyebrows, eyes, nose, lips, ears and skin color. The paper uses two methods for clustering, KMeans and SOM. These methods used class partitioning based on shape similarities. Using the K-Means results, 14% chose a triangular face, then 16% chose an diamond face shape, 23% chosen a heart face shape, 6% chosen a round face shape, 23% chosen a oval face shape, 18% chosen a square face shape. Results from SOM shows that 18% chose a triangular face, then 11% chose an diamond face shape, 24% chosen a heart face shape, 22% chosen a round face shape, 12% chosen a oval face shape, 13% chosen a square face shape. It shows that KMeans has better performance than SOM in determining the female avatar face classes that are wanted by customers.
KW - Algorithm
KW - Avatar
KW - Clustering
KW - KMeans
KW - SOM
UR - http://www.scopus.com/inward/record.url?scp=85078463397&partnerID=8YFLogxK
U2 - 10.1109/ISITIA.2019.8937279
DO - 10.1109/ISITIA.2019.8937279
M3 - Conference contribution
AN - SCOPUS:85078463397
T3 - Proceedings - 2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019
SP - 366
EP - 370
BT - Proceedings - 2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019
Y2 - 28 August 2019 through 29 August 2019
ER -