Recognition of original arabica civet coffee based on odor using electronic nose and machine learning

Whilly Harsono; Riyanarto Sarno; Shoffi Izza Sabilla

doi:10.1109/iSemantic50169.2020.9234234

Recognition of original arabica civet coffee based on odor using electronic nose and machine learning

Whilly Harsono, Riyanarto Sarno^*, Shoffi Izza Sabilla

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

Many studies have used an electronic nose (E-nose) to detect several types of coffee. To the best of our knowledge, none of the studies have tried to detect odors from a mixture of several types of coffee. Therefore, this research proposes E-nose which can be used to recognize original Arabica civet coffee. The mixture of Arabica civet coffee and Robusta coffee (non-civet coffee) is used as the object of this research. Nine combinations of mixture are prepared in this study. Those combinations are referred to as classes. After collecting the data, a statistical calculation would be determined to obtain parameter statistics. Moreover, the classification method used in this study is to recognize original Arabica civet coffee and original Robusta coffee. Several classifications had been compared, namely Logistic Regression (LR), Linear Discriminant Analysis (LDA), and K-Nearest Neighbors (KNN). The best result is the KNN method with an accuracy value of 97.7% for nine classes.

Original language	English
Title of host publication	Proceedings - 2020 International Seminar on Application for Technology of Information and Communication
Subtitle of host publication	IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	333-339
Number of pages	7
ISBN (Electronic)	9781728190686
DOIs	https://doi.org/10.1109/iSemantic50169.2020.9234234
Publication status	Published - 19 Sept 2020
Event	2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020 - Semarang, Indonesia Duration: 19 Sept 2020 → 20 Sept 2020

Publication series

Name	Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020

Conference

Conference	2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020
Country/Territory	Indonesia
City	Semarang
Period	19/09/20 → 20/09/20

Keywords

Arabica Coffee
Civet Coffee
Classification
E-nose
Robusta Coffee
Sensors

Access to Document

10.1109/iSemantic50169.2020.9234234

Cite this

Harsono, W., Sarno, R., & Sabilla, S. I. (2020). Recognition of original arabica civet coffee based on odor using electronic nose and machine learning. In Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020 (pp. 333-339). Article 9234234 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iSemantic50169.2020.9234234

Harsono, Whilly ; Sarno, Riyanarto ; Sabilla, Shoffi Izza. / Recognition of original arabica civet coffee based on odor using electronic nose and machine learning. Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 333-339 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020).

@inproceedings{54b09240a89940db96491fd38036815f,

title = "Recognition of original arabica civet coffee based on odor using electronic nose and machine learning",

abstract = "Many studies have used an electronic nose (E-nose) to detect several types of coffee. To the best of our knowledge, none of the studies have tried to detect odors from a mixture of several types of coffee. Therefore, this research proposes E-nose which can be used to recognize original Arabica civet coffee. The mixture of Arabica civet coffee and Robusta coffee (non-civet coffee) is used as the object of this research. Nine combinations of mixture are prepared in this study. Those combinations are referred to as classes. After collecting the data, a statistical calculation would be determined to obtain parameter statistics. Moreover, the classification method used in this study is to recognize original Arabica civet coffee and original Robusta coffee. Several classifications had been compared, namely Logistic Regression (LR), Linear Discriminant Analysis (LDA), and K-Nearest Neighbors (KNN). The best result is the KNN method with an accuracy value of 97.7% for nine classes.",

keywords = "Arabica Coffee, Civet Coffee, Classification, E-nose, Robusta Coffee, Sensors",

author = "Whilly Harsono and Riyanarto Sarno and Sabilla, {Shoffi Izza}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020 ; Conference date: 19-09-2020 Through 20-09-2020",

year = "2020",

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doi = "10.1109/iSemantic50169.2020.9234234",

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Harsono, W, Sarno, R & Sabilla, SI 2020, Recognition of original arabica civet coffee based on odor using electronic nose and machine learning. in Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020., 9234234, Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020, Institute of Electrical and Electronics Engineers Inc., pp. 333-339, 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020, Semarang, Indonesia, 19/09/20. https://doi.org/10.1109/iSemantic50169.2020.9234234

Recognition of original arabica civet coffee based on odor using electronic nose and machine learning. / Harsono, Whilly; Sarno, Riyanarto ; Sabilla, Shoffi Izza.
Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 333-339 9234234 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Recognition of original arabica civet coffee based on odor using electronic nose and machine learning

AU - Harsono, Whilly

AU - Sarno, Riyanarto

AU - Sabilla, Shoffi Izza

PY - 2020/9/19

Y1 - 2020/9/19

N2 - Many studies have used an electronic nose (E-nose) to detect several types of coffee. To the best of our knowledge, none of the studies have tried to detect odors from a mixture of several types of coffee. Therefore, this research proposes E-nose which can be used to recognize original Arabica civet coffee. The mixture of Arabica civet coffee and Robusta coffee (non-civet coffee) is used as the object of this research. Nine combinations of mixture are prepared in this study. Those combinations are referred to as classes. After collecting the data, a statistical calculation would be determined to obtain parameter statistics. Moreover, the classification method used in this study is to recognize original Arabica civet coffee and original Robusta coffee. Several classifications had been compared, namely Logistic Regression (LR), Linear Discriminant Analysis (LDA), and K-Nearest Neighbors (KNN). The best result is the KNN method with an accuracy value of 97.7% for nine classes.

AB - Many studies have used an electronic nose (E-nose) to detect several types of coffee. To the best of our knowledge, none of the studies have tried to detect odors from a mixture of several types of coffee. Therefore, this research proposes E-nose which can be used to recognize original Arabica civet coffee. The mixture of Arabica civet coffee and Robusta coffee (non-civet coffee) is used as the object of this research. Nine combinations of mixture are prepared in this study. Those combinations are referred to as classes. After collecting the data, a statistical calculation would be determined to obtain parameter statistics. Moreover, the classification method used in this study is to recognize original Arabica civet coffee and original Robusta coffee. Several classifications had been compared, namely Logistic Regression (LR), Linear Discriminant Analysis (LDA), and K-Nearest Neighbors (KNN). The best result is the KNN method with an accuracy value of 97.7% for nine classes.

KW - Arabica Coffee

KW - Civet Coffee

KW - Classification

KW - E-nose

KW - Robusta Coffee

KW - Sensors

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U2 - 10.1109/iSemantic50169.2020.9234234

DO - 10.1109/iSemantic50169.2020.9234234

M3 - Conference contribution

AN - SCOPUS:85096881377

T3 - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020

SP - 333

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BT - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

Y2 - 19 September 2020 through 20 September 2020

ER -

Harsono W, Sarno R , Sabilla SI. Recognition of original arabica civet coffee based on odor using electronic nose and machine learning. In Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 333-339. 9234234. (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020). doi: 10.1109/iSemantic50169.2020.9234234

Recognition of original arabica civet coffee based on odor using electronic nose and machine learning

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