TY - GEN
T1 - Optimal Feature Selection Algorithm (FSA) for Electronic Nose Signal
AU - Putri, Rizqy Ahsana
AU - Sabilla, Shoffi Izza
AU - Sarno, Riyanarto
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In E-Nose data, redundancy and overlapping are frequent issues. This is due to the fact that certain sensors have the same gas target as other sensor gas targets. The goal of this research is to classify meat odor E-Nose data into four classes, including chicken, beef, lamb, and pork. 276 meat odor data in total-108 chicken, 61 beef, 27 lamb, and 80 pork-were used in this research. Additionally, this research aims to identify the most effective Feature Selection Algorithm (FSA) to address issues with redundancy and overlapping data. In this study, FSA techniques such as Pearson Correlation, ANOVA F -value, Chi-Square, and MIFS were applied. SVM, K-NN, and Random Forest are the implementations of classification algorithms. According to the research's findings, SVM-MIFS (k = 10) is the best combination method for classifying meat odor data, achieving an accuracy rate of 98.91 %.
AB - In E-Nose data, redundancy and overlapping are frequent issues. This is due to the fact that certain sensors have the same gas target as other sensor gas targets. The goal of this research is to classify meat odor E-Nose data into four classes, including chicken, beef, lamb, and pork. 276 meat odor data in total-108 chicken, 61 beef, 27 lamb, and 80 pork-were used in this research. Additionally, this research aims to identify the most effective Feature Selection Algorithm (FSA) to address issues with redundancy and overlapping data. In this study, FSA techniques such as Pearson Correlation, ANOVA F -value, Chi-Square, and MIFS were applied. SVM, K-NN, and Random Forest are the implementations of classification algorithms. According to the research's findings, SVM-MIFS (k = 10) is the best combination method for classifying meat odor data, achieving an accuracy rate of 98.91 %.
KW - Classification
KW - Electronic Nose
KW - Feature Selection Algorithm
KW - Meats
KW - Redundancy
UR - http://www.scopus.com/inward/record.url?scp=85190064387&partnerID=8YFLogxK
U2 - 10.1109/ICONNIC59854.2023.10467988
DO - 10.1109/ICONNIC59854.2023.10467988
M3 - Conference contribution
AN - SCOPUS:85190064387
T3 - 2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding
SP - 310
EP - 315
BT - 2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023
Y2 - 14 October 2023
ER -