Identifikasi Kualitas Daging Sapi di Dalam Ruangan Menggunakan Deret Sensor Gas dan Metode Probabilistic Neural Network

Amalia, Aslikha (2024) Identifikasi Kualitas Daging Sapi di Dalam Ruangan Menggunakan Deret Sensor Gas dan Metode Probabilistic Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Daging sapi merupakan salah satu makanan yang paling banyak dikonsumsi manusia. Namun daging sapi busuk banyak ditemukan di pasar atau supermarket. Salah satu penyebabnya adalah kelalaian tim Quality Control dalam membuang daging sapi busuk yang disimpan di gudang. Daging sapi busuk dapat menghasilkan produk samping metabolisme seperti amonia (NH3), hidrogen sulfida (H2S), dan volatile organic compounds (VOC). Penelitian ini mengembangkan sistem hidung elektronik untuk mengidentifikasi kualitas daging sapi yang ada di dalam ruangan. Sensor gas yang digunakan adalah MQ-137, MQ-136, dan TGS 2602. Namun aliran udara di dalam ruangan dapat menyebabkan gangguan konsentrasi gas sehingga membuat respon sensor menjadi tidak stabil. Oleh karena itu, metode probabilistic neural network (PNN) digunakan untuk mengidentifikasi kualitas daging sapi secara akurat. Hasil percobaan menunjukkan bahwa metode ini berhasil mengidentifikasi kualitas daging sapi segar, hampir busuk, dan busuk dengan tingkat keberhasilan 94.9%.
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Beef is one of the foods most consumed by humans. However, rotten beef is often found in markets. This indicates the omission of the rotten beef, which is still stored in the warehouse. Rotten beef can release metabolic products such as ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOC). This study has developed an electronic nose system that can identify the quality of beef indoors. This system uses the MQ-137, MQ-136, and TGS 2602 gas sensors. However, the airflow in the room can cause a disturbance in the concentration of the gas, making the sensor’s response unstable. Therefore, a probabilistic neural network (PNN) is employed to identify beef quality indoors. The experimental results show that this method can identify the quality of fresh, spoiled, and rotten beef with a success rate of 94.9%.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	kualitas daging, makanan, PNN, sensor gas, beef quality, food, gas sensors
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7878 Electronic instruments
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User:	Aslikha Amalia
Date Deposited:	05 Feb 2024 04:04
Last Modified:	05 Feb 2024 04:04
URI:	http://repository.its.ac.id/id/eprint/105865

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