Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method

Gede Prananda Putra; Muhammad Rivai; Rudy Dikairono; Faisal Hadi

doi:10.1109/ISITIA66279.2025.11137557

Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method

Gede Prananda Putra
, Muhammad Rivai
, Rudy Dikairono
, Faisal Hadi

Department of Electrical Engineering
Institut Teknologi Sepuluh Nopember
Graduate School of Science and Engineering
Saga University

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

Abstract

Indonesia is one of the largest palm oil producing countries in the world. Palm oil is a commodity used in both food and nonfood products. One of the main food-sector products derived from palm oil is palm cooking oil; therefore, maintaining its quality in production activities and supply chains is essential. One of the factors that affects the quality of palm cooking oil is the level of oxidation. Laboratory-based quality analysis methods are costly, take a long time, and require large samples. This study proposes the application of a potentiometric and gas sensor-based system integrated with an artificial neural network to analyze the quality of palm cooking oil. The experiments included classification of palm cooking oil products and identification of the quality of degraded palm cooking oil. The results of this study indicate that this system is able to classify between palm cooking oil products and identify the level of degradation in palm cooking oil with an average accuracy of 88% and 93%, respectively.

Original language	English
Title of host publication	26th International Seminar on Intelligent Technology and Its Applications
Subtitle of host publication	Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	355-360
Number of pages	6
Edition	2025
ISBN (Electronic)	9798331537609
DOIs	https://doi.org/10.1109/ISITIA66279.2025.11137557
Publication status	Published - 2025
Event	26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025 - Hybrid, Surabaya, Indonesia Duration: 23 Jul 2025 → 25 Jul 2025

Conference

Conference	26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025
Country/Territory	Indonesia
City	Hybrid, Surabaya
Period	23/07/25 → 25/07/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

food
gas sensor
neural network
palm cooking oil
potentiometric sensor

Access to Document

10.1109/ISITIA66279.2025.11137557

Cite this

Putra, G. P., Rivai, M., Dikairono, R., & Hadi, F. (2025). Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method. In 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings (2025 ed., pp. 355-360). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISITIA66279.2025.11137557

Putra, Gede Prananda ; Rivai, Muhammad ; Dikairono, Rudy et al. / Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method. 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 355-360

@inproceedings{11093cc22a0147429fee13a1801a5953,

title = "Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method",

abstract = "Indonesia is one of the largest palm oil producing countries in the world. Palm oil is a commodity used in both food and nonfood products. One of the main food-sector products derived from palm oil is palm cooking oil; therefore, maintaining its quality in production activities and supply chains is essential. One of the factors that affects the quality of palm cooking oil is the level of oxidation. Laboratory-based quality analysis methods are costly, take a long time, and require large samples. This study proposes the application of a potentiometric and gas sensor-based system integrated with an artificial neural network to analyze the quality of palm cooking oil. The experiments included classification of palm cooking oil products and identification of the quality of degraded palm cooking oil. The results of this study indicate that this system is able to classify between palm cooking oil products and identify the level of degradation in palm cooking oil with an average accuracy of 88\% and 93\%, respectively.",

keywords = "food, gas sensor, neural network, palm cooking oil, potentiometric sensor",

author = "Putra, \{Gede Prananda\} and Muhammad Rivai and Rudy Dikairono and Faisal Hadi",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025 ; Conference date: 23-07-2025 Through 25-07-2025",

year = "2025",

doi = "10.1109/ISITIA66279.2025.11137557",

language = "English",

pages = "355--360",

booktitle = "26th International Seminar on Intelligent Technology and Its Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

edition = "2025",

}

Putra, GP, Rivai, M , Dikairono, R & Hadi, F 2025, Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method. in 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025 edn, Institute of Electrical and Electronics Engineers Inc., pp. 355-360, 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025, Hybrid, Surabaya, Indonesia, 23/07/25. https://doi.org/10.1109/ISITIA66279.2025.11137557

Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method. / Putra, Gede Prananda; Rivai, Muhammad ; Dikairono, Rudy et al.
26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. p. 355-360.

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

TY - GEN

T1 - Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method

AU - Putra, Gede Prananda

AU - Rivai, Muhammad

AU - Dikairono, Rudy

AU - Hadi, Faisal

PY - 2025

Y1 - 2025

N2 - Indonesia is one of the largest palm oil producing countries in the world. Palm oil is a commodity used in both food and nonfood products. One of the main food-sector products derived from palm oil is palm cooking oil; therefore, maintaining its quality in production activities and supply chains is essential. One of the factors that affects the quality of palm cooking oil is the level of oxidation. Laboratory-based quality analysis methods are costly, take a long time, and require large samples. This study proposes the application of a potentiometric and gas sensor-based system integrated with an artificial neural network to analyze the quality of palm cooking oil. The experiments included classification of palm cooking oil products and identification of the quality of degraded palm cooking oil. The results of this study indicate that this system is able to classify between palm cooking oil products and identify the level of degradation in palm cooking oil with an average accuracy of 88% and 93%, respectively.

AB - Indonesia is one of the largest palm oil producing countries in the world. Palm oil is a commodity used in both food and nonfood products. One of the main food-sector products derived from palm oil is palm cooking oil; therefore, maintaining its quality in production activities and supply chains is essential. One of the factors that affects the quality of palm cooking oil is the level of oxidation. Laboratory-based quality analysis methods are costly, take a long time, and require large samples. This study proposes the application of a potentiometric and gas sensor-based system integrated with an artificial neural network to analyze the quality of palm cooking oil. The experiments included classification of palm cooking oil products and identification of the quality of degraded palm cooking oil. The results of this study indicate that this system is able to classify between palm cooking oil products and identify the level of degradation in palm cooking oil with an average accuracy of 88% and 93%, respectively.

KW - food

KW - gas sensor

KW - neural network

KW - palm cooking oil

KW - potentiometric sensor

UR - https://www.scopus.com/pages/publications/105016901573

U2 - 10.1109/ISITIA66279.2025.11137557

DO - 10.1109/ISITIA66279.2025.11137557

M3 - Conference contribution

AN - SCOPUS:105016901573

SP - 355

EP - 360

BT - 26th International Seminar on Intelligent Technology and Its Applications

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th International Seminar on Intelligent Technology and Its Applications, ISITIA 2025

Y2 - 23 July 2025 through 25 July 2025

ER -

Putra GP, Rivai M , Dikairono R, Hadi F. Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method. In 26th International Seminar on Intelligent Technology and Its Applications: Fostering Equal Opportunities for Breakthrough Technology Innovations, ISITIA 2025 - Proceedings. 2025 ed. Institute of Electrical and Electronics Engineers Inc. 2025. p. 355-360 doi: 10.1109/ISITIA66279.2025.11137557

Quality Assessment of Palm Cooking Oil Using Potentiometric and Gas Sensors with Neural Network Method

Abstract

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this