Monitoring Of Cooking In The Oven Using Thermal Camera and Gas Sensor

Raff, Zauqy Affan (2025) Monitoring Of Cooking In The Oven Using Thermal Camera and Gas Sensor. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

In typical Indonesian dishes, the level of food doneness has not received attention because generally Indonesian food must be cooked until it is completely cooked when compared with Europe which already has a certain level of food doneness. Besides that, two factors that are also important in determining the level of food doneness are the food scent during the cooking process and the temperature of the food. Therefore, this research will determine the level of doneness will be carried out on food cooked in a microwave. The use of thermal camera is to take pictures of food temperature, gas sensor to detect food scent while cooking process. The data collected will be processed using the neural network classification algorithm method. This study uses the MLX90640 thermal camera and the MQ-7 and MQ-135 gas sensors to distinguish between uncooked, cooked, and overcooked cookies in the microwave. Furthermore, the study compared three deep learning methods: CNN with thermal camera, LSTM with gas sensor, and a combination of the two (CNN-LSTM). The neural network structure resulted in data training accuracy rates of 83,32% for the CNN model and 66,67% for the LSTM Model.
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Pada masakan khas Indonesia, tingkat kematangan makanan belum mendapat perhatian karena umumnya makanan Indonesia harus dimasak hingga benar-benar matang jika dibandingkan dengan Eropa yang sudah memiliki tingkat kematangan makanan tertentu. Selain itu, dua faktor yang juga penting dalam menentukan tingkat kematangan makanan adalah aroma makanan selama proses memasak dan suhu makanan. Oleh karena itu, penelititan ini akan menentukan tingkat kematangan yang akan dilakukan pada makanan yang dimasak dalam microwave. Penggunaan kamera termal untuk mengambil gambar suhu makanan, sensor gas untuk mendeteksi aroma makanan selama proses memasak. Data yang dikumpulkan akan diolah menggunakan metode algoritma klasifikasi jaringan saraf tiruan. Penelitian ini menggunakan kamera termal MLX90640 dan sensor gas MQ-7 dan MQ-135 untuk membedakan antara kue yang belum matang, matang, dan terlalu matang dalam microwave. Lebih lanjut, penelitian ini membandingkan tiga metode pembelajaran mendalam: CNN dengan kamera termal, LSTM dengan sensor gas, dan kombinasi keduanya (CNN-LSTM). Struktur jaringan saraf menghasilkan tingkat akurasi pelatihan data sebesar 83,32% untuk model CNN dan 66,67% untuk model LSTM.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Food, Thermal Camera, Gas Sensor, Neural Network, Makanan, Kamera Thermal, Sensor Gas
Subjects:	Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Zauqy Affan Raff
Date Deposited:	31 Jul 2025 01:51
Last Modified:	31 Jul 2025 01:51
URI:	http://repository.its.ac.id/id/eprint/124091

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