Identifikasi Kualitas Minyak Sawit Menggunakan Sensor Gas dan Potensiometri dengan Metode Neural Network

Putra, Gede Prananda (2025) Identifikasi Kualitas Minyak Sawit Menggunakan Sensor Gas dan Potensiometri dengan Metode Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Indonesia merupakan salah satu negara produsen minyak kelapa sawit terbesar di dunia. Minyak kelapa sawit merupakan komoditas yang digunakan baik dalam produk makanan maupun non-makanan. Salah satu bentuk produk dari minyak sawit dalam bidang pangan adalah minyak goreng sawit, sehingga menjaga kualitas dalam kegiatan produksi maupun rantai pasokan sangat diperlukan. Salah satu faktor yang mempengaruhi kualitas minyak goreng sawit ditentukan oleh tingkat oksidasi. Metode analisis kualitas melalui laboratorium yang memerlukan biaya yang mahal, memakan waktu yang lama, dan memerlukan sampel dalam jumlah besar. Studi ini menawarkan penerapan sistem berbasis sensor potensiometri dan gas yang dilengkapi dengan jaringan saraf tiruan untuk menganalisis kualitas minyak goreng sawit. Percobaan meliputi klasifikasi antar produk minyak goreng sawit dan identifikasi kualitas minyak goreng sawit yang telah terdegradasi. Hasil studi ini menunjukkan bahwa sistem ini mampu mengklasifikasikan antar produk minyak goreng sawit dan mengidentifikasi kualitas minyak goreng yang telah terdegradasi dengan rata-rata akurasi masing-masing 91%, dan 90%.
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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 form of palm oil product in the food sector is palm cooking oil, so maintaining 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. The quality analysis method through a laboratory incurs expensive costs, takes a long time, and requires large samples. This study proposes the application of a potentiometric and gas sensor-based system equipped 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 quality of degraded cooking oil with an average accuracy of 91% and 90%, respectively.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	pangan, minyak goreng sawit, potensiometri, sensor gas, neural network, food, palm cooking oil, potentiometric, gas sensor, neural network
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors 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:	Gede Prananda Putra
Date Deposited:	23 Jul 2025 01:08
Last Modified:	23 Jul 2025 01:08
URI:	http://repository.its.ac.id/id/eprint/120595

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