TY - GEN
T1 - Big Data Technologies using SVM (Case Study: Surface Water Classification on Regional Water Utility Company in Surabaya)
AU - Budiarti, Rizqi Putri Nourma
AU - Sukaridhoto, Sritrusta
AU - Hariadi, Mochamad
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - How important to the role of water for the survival of living beings, not only for human but also the other living beings need water as one of the elements that support the continuity of life in every living creature. To maintain the necessity of water resources such as river, recently the need for monitoring systems that able to take the parameter of water quality using sensors important. In the previous paper, we built the Internet of Things to get the data using a passive sensor and an active sensor. As additionally, we built Big-Data system equipped with machine learning algorithm that can perform water quality classification with the Support Vector Machine method. This system monitoring every activity in the Karang Pilang area and applying classification. The result of this system that the big data system can perform the classification of river water quality in interactive and accurate. The result discusses that we were able to classify by using Support Vector Machine with accuracy level 0.9138 by using Linear kernel and 0.8372 by using RBF kernel. From the ROC result, we achieved AUC value until 0.93. It's mean we achieved an excellent result.
AB - How important to the role of water for the survival of living beings, not only for human but also the other living beings need water as one of the elements that support the continuity of life in every living creature. To maintain the necessity of water resources such as river, recently the need for monitoring systems that able to take the parameter of water quality using sensors important. In the previous paper, we built the Internet of Things to get the data using a passive sensor and an active sensor. As additionally, we built Big-Data system equipped with machine learning algorithm that can perform water quality classification with the Support Vector Machine method. This system monitoring every activity in the Karang Pilang area and applying classification. The result of this system that the big data system can perform the classification of river water quality in interactive and accurate. The result discusses that we were able to classify by using Support Vector Machine with accuracy level 0.9138 by using Linear kernel and 0.8372 by using RBF kernel. From the ROC result, we achieved AUC value until 0.93. It's mean we achieved an excellent result.
KW - Big Data
KW - IOT
KW - Support Vector Machine
KW - Surface water classification
KW - Water quality
UR - http://www.scopus.com/inward/record.url?scp=85077816283&partnerID=8YFLogxK
U2 - 10.1109/ICOMITEE.2019.8920823
DO - 10.1109/ICOMITEE.2019.8920823
M3 - Conference contribution
AN - SCOPUS:85077816283
T3 - Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
SP - 94
EP - 101
BT - Proceedings - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
A2 - Slamin, S.
A2 - Prihandoko, Antonius Cahya
A2 - Adnan, Fahrobby
A2 - Prasetyo, Beny
A2 - Nerisafitra, Paramitha
A2 - Riskiawan, Hendra Yufit
A2 - Sulistiyani, Endang
A2 - Destarianto, Prawidya
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering, ICOMITEE 2019
Y2 - 16 October 2019 through 17 October 2019
ER -